Practical Machine Learning in Spark

Chih-Chieh Hung

Tamkang University

Chih-Chieh Hung 洪智傑

• Tamkang University (Assistant Professor)

2016-

• Rakuten Inc., Japan (Data Scientist)

2013-2015

• Yahoo! Inc., Taiwan (Research Engineer)

2011-2013

• Microsoft Research Asia, China (Research Intern)

2010

Something About Big Data

Big Data Definition

• No single standard definition…

“Big Data” is data whose scale, diversity, and complexity require new

architecture, techniques, algorithms, and analytics to manage it and

extract value and hidden knowledge from it…

6

Scale (Volume)

• Data Volume• 44x increase from 2009 to 2020

• From 0.8 zettabytes to 35zb

• Data volume is increasing exponentially

Complexity (Varity)

• Various formats, types, and structures

• Text, numerical, images, audio, video, sequences, time series, social media data, multi-dim arrays, etc…

• Static data vs. streaming data

• A single application can be generating/collecting many types of data

8

To extract knowledge all these types of data need to linked together

Speed (Velocity)

• Data is begin generated fast and need to be processed fast

• Online Data Analytics

• Late decisions missing opportunities

Four V Challenges in Big Data

*. http://www-05.ibm.com/fr/events/netezzaDM_2012/Solutions_Big_Data.pdf

Apache Hadoop Stack

Apache Hadoop

• The Apache™ Hadoop® project develops open-source software for reliable, scalable, distributed computing.

• Three major modules:• Hadoop Distributed File System (HDFS™): A

distributed file system that provides high-throughput access to application data.

• Hadoop YARN: A framework for job scheduling and cluster resource management.

• Hadoop MapReduce: A YARN-based system for parallel processing of large data sets.

Hadoop Components: HDFS• File system

• Sit on top of a native file system• Based on Google’s GFS• Provide redundant storage

• Read/Write• Good at large, sequential reads• Files are “Write once”

• Components• DataNodes: metadata of files• NameNodes: actual blocks• Secondary NameNode: merges the fsimage

and the edits log files periodically and keeps edits log size within a limit

Hadoop Components: YARN

• Manage resource (Data operating system).

• YARN = Yet Another Resource Negotiator

• Manage and monitor workloads

• Maintain a multi-tenant platform.

• Implement security control.

• Support multiple processing models in addition to MapReduce.

Hadoop Components: MapReduce

• Process data in cluster.

• Two phases: Map + Reduce• Between the two is the “shuffle-and-sort” stage

• Map• Operates on a discrete portion of the overall dataset

• Reduce• After all maps are complete, the intermediate data are separated to nodes

which perform the Reduce phase.

The MapReduce Framework

MapReduce Algorithm For Word Count

• Input and Output

Step 1: Design Mapper (Must Implement)

• Write the mapper: output the key-value pair <word, 1>

Step 2: Sort and Shuffle (Don’t Need to Do)

• The values with the same key will send to the same reducer.

Step 3: Design Reducer (Must Implement)

• Write reducer as: (word, sum of all the values)

Spark

What is Spark?

Efficient• General execution graphs

• In-memory storage

Usable• Rich APIs in Java, Scala, Python

• Interactive shell

• Fast and Expressive Cluster Computing System Compatible with Apache Hadoop

Key Concepts

Resilient Distributed Datasets• Collections of objects spread across a

cluster, stored in RAM or on Disk

• Built through parallel transformations

• Automatically rebuilt on failure

Operations• Transformations

(e.g. map, filter, groupBy)

• Actions(e.g. count, collect, save)

• Write programs in terms of transformations on distributed datasets

Language Support

Standalone Programs

•Python, Scala, & Java

Interactive Shells

• Python & Scala

Performance

• Java & Scala are faster due to static typing

• …but Python is often fine

Pythonlines = sc.textFile(...)lines.filter(lambda s: “ERROR” in s).count()

Scalaval lines = sc.textFile(...)lines.filter(x => x.contains(“ERROR”)).count()

JavaJavaRDD<String> lines = sc.textFile(...);lines.filter(new Function<String, Boolean>() {Boolean call(String s) {return s.contains(“error”);

}}).count();

Spark Ecosystem

import sysfrom pyspark import SparkContext

if __name__ == "__main__":sc = SparkContext( “local”, “WordCount”, sys.argv[0], None)lines = sc.textFile(sys.argv[1])

counts = lines.flatMap(lambda s: s.split(“ ”)) \.map(lambda word: (word, 1)) \.reduceByKey(lambda x, y: x + y)

counts.saveAsTextFile(sys.argv[2])

An Simple Example of Spark App

sc

RDD ops

SparkContext

• Main entry point

• SparkContext is the object that manages the connection to the clusters in Spark and coordinates running processes on the clusters themselves. SparkContextconnects to cluster managers, which manage the actual executors that run the specific computations

SparkContext

• Main entry point to Spark functionality

• Available in shell as variable sc• In standalone programs, you’d make your own (see later for details)

Create SparkContext: Local Mode

• Very simple

Create SparkContext: Cluster Mode

• Need to write SparkConf about the clusters

Resilient Distributed Datasets (RDD)

• An RDD is Spark's representation of a dataset that is distributed across the RAM, or memory, of lots of machines.

• An RDD object is essentially a collection of elements that you can use to hold lists of tuples, dictionaries, lists, etc.

• Lazy Evaluation : the ability to lazily evaluate code, postponing running a calculation until absolutely necessary.

•

Working with RDDs

Transformation and Actions in Spark

• RDDs have actions, which return values, and transformations, which return pointers to new RDDs.

• RDDs’ value is only updated once that RDD is computed as part of an action

Example: Log MiningLoad error messages from a log into memory, then interactively search for various patterns

lines = spark.textFile(“hdfs://...”)

errors = lines.filter(lambda s: s.startswith(“ERROR”))

messages = errors.map(lambda s: s.split(“\t”)[2])

messages.cache()

Block 1

Block 2

Block 3

Worker

Worker

Worker

Driver

messages.filter(lambda s: “mysql” in s).count()

messages.filter(lambda s: “php” in s).count()

. . .

tasks

results

Cache 1

Cache 2

Cache 3

Base RDDTransformed RDD

Action

Full-text search of Wikipedia• 60GB on 20 EC2 machine• 0.5 sec vs. 20s for on-disk

Creating RDDs

# Turn a Python collection into an RDD>sc.parallelize([1, 2, 3])

# Load text file from local FS, HDFS, or S3>sc.textFile(“file.txt”)>sc.textFile(“directory/*.txt”)>sc.textFile(“hdfs://namenode:9000/path/file”)

# Use existing Hadoop InputFormat (Java/Scala only)>sc.hadoopFile(keyClass, valClass, inputFmt, conf)

Most Widely-Used Action and Transformation

Transformation

Basic Transformations

>nums = sc.parallelize([1, 2, 3])

# Pass each element through a function>squares = nums.map(lambda x: x*x) // {1, 4, 9}

# Keep elements passing a predicate>even = squares.filter(lambda x: x % 2 == 0) // {4}

# Map each element to zero or more others>nums.flatMap(lambda x: => range(x))

> # => {0, 0, 1, 0, 1, 2}

Range object (sequence of numbers 0, 1, …, x-1)

map() and flatMap()

• map()

map() transformation applies changes on each line of the RDD and returns the transformed RDD as iterable of iterables i.e. each line is equivalent to a iterable and the entire RDD is itself a list

map() and flatMap()

• flatMap()

This transformation apply changes to each line same as map but the return is not a iterable of iterables but it is only an iterable holding entire RDD contents.

map() and flatMap() examples>lines.take(2)

[‘#good d#ay #’,

‘#good #weather’]>words = lines.map(lambda lines: lines.split(' '))

[[‘#good’, ‘d#ay’, ’#’],

[‘#good’, ‘#weather’]]>words = lines. flatMap(lambda lines: lines.split(' '))

[‘#good’, ‘d#ay’, ‘#’, ‘#good’, ‘#weather’]

Filter()

• Filter() transformation is used to reduce the old RDD based on some condition.

Filter() example

• How to filter out hashtags from words>hashtags = words.filter(lambda word: word.startswith("#")).filter(lambda word: word != "#")

[‘#good’, ‘#good’, ‘#weather’]

Join()

• Return a RDD containing all pairs of elements having the same key in the original RDDs

Join() Example

KeyBy()

• Create a Pair RDD, forming one pair for each item in the original RDD. The pair’s key is calculated from the value via a user-defined function.

KeyBy() examples

GroupBy()

• Group the data in the original RDD. Create pairs where the key is the output of a user function, and the value is all items for which the function yields this key.

GroupBy() example

GroupByKey()

• Group the values for each key in the original RDD. Create a new pair where the original key corresponds to this collected group of values.

GroupByKey() example

ReduceByKey()

• reduceByKey(f) combines tuples with the same key using the function we specify f.

>hashtagsNum = hashtags.map(lambda word: (word, 1))

[(‘#good’,1), (‘#good’, 1), (‘#weather’, 1)]

>hashtagsCount = hashtagsNum.reduceByKey(lambda a,b: a+b)

[(‘#good’,2), (‘#weather’, 1)]

The Difference between GroupByKey() and ReduceByKey()

Example: Word Count

> lines = sc.textFile(“hamlet.txt”)

> counts = lines.flatMap(lambda line: line.split(“ ”)).map(lambda word => (word, 1)).reduceByKey(lambda x, y: x + y)

“to be or”

“not to be”

“to”“be”“or”

“not”“to”“be”

(to, 1)(be, 1)(or, 1)

(not, 1)(to, 1)(be, 1)

(be, 2)(not, 1)

(or, 1)(to, 2)

Actions

Basic Actions

>nums = sc.parallelize([1, 2, 3])

# Retrieve RDD contents as a local collection>nums.collect() # => [1, 2, 3]

# Return first K elements>nums.take(2) # => [1, 2]

# Count number of elements>nums.count() # => 3

# Merge elements with an associative function>nums.reduce(lambda x, y: x + y) # => 6

# Write elements to a text file>nums.saveAsTextFile(“hdfs://file.txt”)

Collect()

• Return all elements in the RDD to the driver in a single list

• Do not do that if you work on a big RDD.

Reduce()

• Aggregate all the elements of the RDD by applying a user function pairwise to elements and partial results, and returns a result to the driver.

Aggregate()

• Aggregate all elements of the RDD by:• Applying a user function seqOp to combine elements with user-supplied

objects

• Then combining those user-defined results via a second user function combOp

• And finally returning a result to the driver

Aggregate(): Using the seqOp in each partition

Aggregate(): Using combOp among Partitions

Aggregate() example

More RDD Operators

• map

• filter

• groupBy

• sort

• union

• join

• leftOuterJoin

• rightOuterJoin

• reduce

• count

• fold

• reduceByKey

• groupByKey

• cogroup

• cross

• zip

sample

take

first

partitionBy

mapWith

pipe

save ...

Lab 1

Example: PageRank

• Good example of a more complex algorithm• Multiple stages of map & reduce

• Benefits from Spark’s in-memory caching• Multiple iterations over the same data

Basic Idea

Give pages ranks (scores) based on links to them

• Links from many pages high rank

• Link from a high-rank page high rank

Image: en.wikipedia.org/wiki/File:PageRank-hi-res-2.png

Algorithm

1.0 1.0

1.0

1.0

1. Start each page at a rank of 1

2. On each iteration, have page p contribute

rankp / |neighborsp| to its neighbors

3. Set each page’s rank to 0.15 + 0.85 × contribs

Algorithm

1. Start each page at a rank of 1

2. On each iteration, have page p contribute

rankp / |neighborsp| to its neighbors

3. Set each page’s rank to 0.15 + 0.85 × contribs

1.0 1.0

1.0

1.0

1

0.5

0.5

0.5

1

0.5

Algorithm

1. Start each page at a rank of 1

2. On each iteration, have page p contribute

rankp / |neighborsp| to its neighbors

3. Set each page’s rank to 0.15 + 0.85 × contribs

0.58 1.0

1.85

0.58

Algorithm

1. Start each page at a rank of 1

2. On each iteration, have page p contribute

rankp / |neighborsp| to its neighbors

3. Set each page’s rank to 0.15 + 0.85 × contribs

0.58

0.29

0.29

0.5

1.850.58 1.0

1.85

0.58

0.5

Algorithm

1. Start each page at a rank of 1

2. On each iteration, have page p contribute

rankp / |neighborsp| to its neighbors

3. Set each page’s rank to 0.15 + 0.85 × contribs

0.39 1.72

1.31

0.58

. . .

Algorithm

1. Start each page at a rank of 1

2. On each iteration, have page p contribute

rankp / |neighborsp| to its neighbors

3. Set each page’s rank to 0.15 + 0.85 × contribs

0.46 1.37

1.44

0.73

Final state:

Lab 2

Machine Learning in 30 min

Machine Learning is…

• Machine learning is about predicting the future based on the past.

-- Hal Daume III

TrainingData

model/predictor

past

model/predictor

future

TestingData

Machine Learning Types

Supervised vs. Unsupervised Learning

Reinforcement Learning

General Flow for Machine Learning

Training Data, Testing Data, Validation Data

• Training data: used to train a model (we have)

• Testing data: test the performance of a model (we don’t have)

• Validation data: “artificial” testing data (we have)

Model Evaluation: What Are We Seeking?

• Minimize the error between training data and the model

Example: The Error of The Model

General Flow of Training and Testing

Classification Concept

Supervised Learning in A Nutshell

• Try to think how you learn when you were a baby. Mom taught you…

Supervised Learning in A Nutshell

• What is it?

Supervised Learning in A Nutshell

• Training data • Testing data

Label

FeaturesFeatures

Rabbit!

Label (We Guessed)

Handwritten Recognition

• Input: 1. hand-written words and labels, 2. a hand-written word W

• Output: the label of W

?

General Classification Flow

Before Hands-on

What is MLlib

• MLlib is an Apache Spark component focusing on machine learning:• MLlib is Spark’s core ML library

• Developed by MLbase team in AMPLab

• 80+ contributions from various organization

• Support Scala, Python, and Java APIs

Spark Ecosystem

Algorithms in MLlib

• Statistics: Description, correlation

• Clustering: k-means

• Classification: SVMs, naive Bayes, decision tree, logistic regression

• Regression: linear regression (+lasso, +ridge)

• Dimensionality: SVD, PCA

• Optimization Primitives: SGD, Parallel Gradient

• Collaborative filtering: ALS

Why Mllib

• Scalability

• Performance

• user-friendly documentation and APIs

• Cost of maintenance

Performance

Data Type

• Dense vector

• Sparse vector

• Labeled point

Dense & Sparse

• Raw Data:

ID A B C D E F

1 1 0 0 0 0 3

2 0 1 0 1 0 2

3 1 1 1 0 1 1

Dense vs Sparse

• A case study- number of example: 12 million- number of features: 500- sparsity: 10%

• Not only save storage, but also received a 4x speed up

Dense Sparse

Storge 47GB 7GB

Time 240s 58s

Labeled Point

• Dummy variable (1,0)

• Categorical variable (0, 1, 2, …)

from pyspark.mllib.linalg import SparseVectorfrom pyspark.mllib.regression import LabeledPoint

# Create a labeled point with a positive label and a dense feature vector.pos = LabeledPoint(1.0, [1.0, 0.0, 3.0])

# Create a labeled point with a negative label and a sparse feature vector.neg = LabeledPoint(0.0, SparseVector(3, [0, 2], [1.0, 3.0]))

Descriptive Statistics

• Supported function:- count- max- min- mean- variance…

• Supported data types- Dense- Sparse- Labeled Point

Example

from pyspark.mllib.stat import Statisticsfrom pyspark.mllib.linalg import Vectorsimport numpy as np

## example data（2 x 2 matrix at least）data= np.array([[1.0,2.0,3.0,4.0,5.0],[1.0,2.0,3.0,4.0,5.0]])

## to RDDdistData = sc.parallelize(data)

## Compute Statistic Valuesummary = Statistics.colStats(distData)print "Duration Statistics:"print " Mean: {}".format(round(summary.mean()[0],3))print " St. deviation: {}".format(round(sqrt(summary.variance()[0]),3))print " Max value: {}".format(round(summary.max()[0],3))print " Min value: {}".format(round(summary.min()[0],3))print " Total value count: {}".format(summary.count())print " Number of non-zero values: {}".format(summary.numNonzeros()[0])

Classification Algorithms

1. Naïve Bayesian Classification

• Given training data D, posteriori probability of a hypothesis h, P(h|D) follows the Bayes theorem

• MAP (maximum posteriori) hypothesis

)()()|(

)|(DP

hPhDPDhP

.)()|(maxarg)|(maxarg hPhDPHh

DhPHhMAP

h

Play-Tennis Example

• Given a training set and an unseen sample X = <rain, hot, high, false>, what class will X be?Outlook Temperature Humidity Windy Class

sunny hot high false N

sunny hot high true N

overcast hot high false P

rain mild high false P

rain cool normal false P

rain cool normal true N

overcast cool normal true P

sunny mild high false N

sunny cool normal false P

rain mild normal false P

sunny mild normal true P

overcast mild high true P

overcast hot normal false P

rain mild high true N

Training Step: Compute Probabilities

• We can compute:

Outlook Temperature Humidity Windy Class

sunny hot high false N

sunny hot high true N

overcast hot high false P

rain mild high false P

rain cool normal false P

rain cool normal true N

overcast cool normal true P

sunny mild high false N

sunny cool normal false P

rain mild normal false P

sunny mild normal true P

overcast mild high true P

overcast hot normal false P

rain mild high true N P(true|n) = 3/5P(true|p) = 3/9P(false|n) = 2/5P(false|p) = 6/9

P(high|n) = 4/5P(high|p) = 3/9P(normal|n) = 2/5P(normal|p) = 6/9

P(hot|n) = 2/5P(hot|p) = 2/9P(mild|n) = 2/5P(mild|p) = 4/9P(cool|n) = 1/5P(cool|p) = 3/9

P(rain|n) = 2/5P(rain|p) = 3/9P(overcast|n) = 0P(overcast|p) = 4/9P(sunny|n) = 3/5P(sunny|p) = 2/9

windy

humidity

temperature

outlook

P(n) = 5/14

P(p) = 9/14

Prediction Step

• An unseen sample X = <rain, hot, high, false>

1. P(X|p)·P(p) = P(rain|p)·P(hot|p)·P(high|p)·P(false|p)·P(p) = 3/9·2/9·3/9·6/9·9/14 = 0.010582

2. P(X|n)·P(n) = P(rain|n)·P(hot|n)·P(high|n)·P(false|n)·P(n) = 2/5·2/5·4/5·2/5·5/14 = 0.018286

• Sample X is classified in class n (don’t play)

Try It on Spark

• Download Experimental Data:

https://raw.githubusercontent.com/apache/spark/master/data/mllib/sample_naive_bayes_data.txt

• Download the Example Code of Naïve Bayes Classification:

https://raw.githubusercontent.com/apache/spark/master/examples/src/main/python/mllib/naive_bayes_example.py

Experimental Data

0,1 0 0

0,2 0 0

0,3 0 0

0,4 0 0

1,0 1 0

1,0 2 0

1,0 3 0

1,0 4 0

2,0 0 1

2,0 0 2

2,0 0 3

2,0 0 4

Feature Vector: (0,2,0)Class Label: 1

Naïve Bayes in Spark

• Step 1: Prepare data

• Step 2: NaiveBayes.train()

• Step 3: NaiveBayes.predict()

• Step 4: Evaluation1

2

3

4

*. Full Version: https://spark.apache.org/docs/latest/mllib-naive-bayes.html

2. Decision Tree

• Decision tree • A flow-chart-like tree structure• Internal node denotes a test on an attribute• Branch represents an outcome of the test• Leaf nodes represent class labels or class distribution

• Decision tree generation consists of two phases• Tree construction

• At start, all the training examples are at the root• Partition examples recursively based on selected attributes

• Tree pruning• Identify and remove branches that reflect noise or outliers

• Use of decision tree: Classifying an unknown sample• Test the attribute values of the sample against the decision tree

Example: Predict the Buys_Computer

age income student credit_rating buys_computer

<=30 high no fair no

<=30 high no excellent no

31…40 high no fair yes

>40 medium no fair yes

>40 low yes fair yes

>40 low yes excellent no

31…40 low yes excellent yes

<=30 medium no fair no

<=30 low yes fair yes

>40 medium yes fair yes

<=30 medium yes excellent yes

31…40 medium no excellent yes

31…40 high yes fair yes

>40 medium no excellent no

Decision Tree

age?

overcast

student? credit rating?

no yes fairexcellent

<=30 >40

no noyes yes

yes

30..40

Build A Decision Tree

• Step 1: All data in Root

• Step 2: Split the node which can lead to more pure sub-nodes

• Step 3: Repeat until terminal conditions meet

Measures for Purity

• Information Gain, Gini Index,…

• Example

Terminal Conditions

Decision Tree in Spark

• Step 1: Prepare data

• Step 2: DT.trainClassifier()

• Step 3: DT.predict()

• Step 4: Evaluation

*. Full Version: https://spark.apache.org/docs/latest/mllib-decision-tree.html

1

2

3

4

Ensemble Decision-Tree-based Algorithms

• Random Forest

Pick random subsets to build trees

• AdaBoost

Improve trees sequentially

3. Logistic Regression

• A classification algorithm

Hypotheses function

• hypotheses:

When outcome is only 1/0

Logistic Regression in Spark

• Step 1: Prepare data

• Step 2: LR.train()

• Step 3: LR.predict()

• Step 4: Evaluation

*. Full Version: https://spark.apache.org/docs/latest/mllib-linear-methods.html#classification

1

2

3

4

4. Support Vector Machine (SVM)

• SVMs maximize the margin around the separating hyperplane.

• The decision function is fully specified by a subset of training samples, the support vectors.

122

Sec. 15.1

How About Data Are Not Linear Separable?

• General idea: the original feature space can always be mapped to some higher-dimensional feature space where the training set is separable.

Sec. 15.2.3

KERNEL FUNCTION

Kernels

• Why use kernels?• Make non-separable problem separable.

• Map data into better representational space

• Common kernels• Linear

• Polynomial K(x,z) = (1+xTz)d

• Radial basis function (RBF)

124

Sec. 15.2.3

RBF

SVM with Different Kernels

SVM in Spark

• Step 1: Prepare data

• Step 2: SVM.train()

• Step 3: SVM.predict()

• Step 4: Evaluation1

2

3

4

*. Full Version: https://spark.apache.org/docs/latest/mllib-linear-methods.html#linear-support-vector-machines-svms

Lab 3