Big Data Analytics using Mahout

Big Data AnalyticsUsing Mahout

Assoc. Prof. Dr. Thanachart NumnondaExecutive DirectorIMC InstituteApril 2015

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Mahout

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Mahout is a Java library which Implementing Machine Learning techniques for

clustering, classification and recommendation

What is Mahout?

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Mahout in Apache Software

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Why Mahout?

Apache License

Good Community

Good Documentation

Scalable

Extensible

Command Line Interface

Java Library

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List of Algorithms

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List of Algorithms

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List of Algorithms

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Mahout Architecture

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Use Cases

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Installing Mahout

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Select a EC2 service and click on Lunch Instance

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Choose My AMIs and select “Hadoop Lab Image”

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Choose m3.medium Type virtual server

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Leave configuration details as default

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Add Storage: 20 GB

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Name the instance

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Select an existing security group > Select SecurityGroup Name: default

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Click Launch and choose imchadoop as a key pair

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Review an instance / click Connect for an instruction to connect to the instance

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Connect to an instance from Mac/Linux

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Connect to an instance from Windows using Putty

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Connect to the instance

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Install Maven

$ sudo apt-get install maven

$ mvn -v

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Install Subversion

$ sudo apt-get install subversion

$ svn --version

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Install Mahout

$ cd /usr/local/

$ sudo mkdir mahout

$ cd mahout

$ sudo svn co http://svn.apache.org/repos/asf/mahout/trunk

$ cd trunk

$ sudo mvn -DskipTests

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Install Mahout (cont.)

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Edit batch files

$ sudo vi $HOME/.bashrc

$ exec bash

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Running Recommendation Algorithms

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MovieLenshttp://grouplens.org/datasets/movielens/

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Architecture for Recommender Engine

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Item-Based Recommendation

Step 1: Gather some test data

Step 2: Pick a similarity measure

Step 3: Configure the Mahout command

Step 4: Making use of the output and doing morewith Mahout

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Preparing Movielen data

$ wget http://files.grouplens.org/datasets/movielens/ml-100k.zip

$ unzip ml-100k.zip

$ hadoop fs -mkdir /input

$ hadoop fs -put u.data /input/u.data

$ hadoop fs -mkdir /results

$ unset MAHOUT_LOCAL

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Running Recommend Command

$ mahout recommenditembased -i /input/u.data -o/results/itemRecom.txt -s SIMILARITY_LOGLIKELIHOOD--tempDir /temp/recommend1

$ hadoop fs -ls /results/itemRecom.txt

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View the result

$ hadoop fs -cat /results/itemRecom.txt/part-r-00000

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Similarity Classname

SIMILARITY_COOCCURRENCE

SIMILARITY_LOGLIKELIHOOD

SIMILARITY_TANIMOTO_COEFFICIENT

SIMILARITY_CITY_BLOCK

SIMILARITY_COSINE

SIMILARITY_PEARSON_CORRELATION

SIMILARITY_EUCLIDEAN_DISTANCE

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Running Recommendation in a single machine

$ export MAHOUT_LOCAL=true

$ mahout recommenditembased -i ml-100k/u.data -o/results/itemRecom.txt -s SIMILARITY_LOGLIKELIHOOD--numRecommendations 5

$ cat results/itemRecom.txt/part-r-00000

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Running Example Program

Using CBayes classifer

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Running Example Program

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Preparing data

$ export WORK_DIR=/tmp/mahout-work-${USER}

$ mkdir -p ${WORK_DIR}

$ mkdir -p ${WORK_DIR}/20news-bydate

$ cd ${WORK_DIR}/20news-bydate

$ wget http://people.csail.mit.edu/jrennie/20Newsgroups/20news-bydate.tar.gz

$ tar -xzf 20news-bydate.tar.gz

$ mkdir ${WORK_DIR}/20news-all

$ cd

$ cp -R ${WORK_DIR}/20news-bydate/*/* ${WORK_DIR}/20news-all

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Note: Running on MapReduce

If you want to run onMapReduce mode, you need to run thefollowing commands before running the feature extractioncommands

$ unset MAHOUT_LOCAL

$ hadoop fs -put ${WORK_DIR}/20news-all ${WORK_DIR}/20news-all

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Preparing the Sequence File

Mahout provides you a utility to convert the given input file in to asequence file format.

The input file directory where the original data resides.

The output file directory where the clustered data is to be stored.

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Sequence Files

Sequence files are binary encoding of key/value pairs. There is aheader on the top of the file organized with some metadatainformation which includes:

– Version

– Key name

– Value name

– Compression

To view the sequential file

mahout seqdumper -i <input file> | more

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Generate Vectors from Sequence Files

Mahout provides a command to create vector files fromsequence files.

mahout seq2sparse -i <input file path> -o <output file path>

Important Options:

-lnorm Whether output vectors should be logNormalize.

-nv Whether output vectors should be NamedVectors

-wt The kind of weight to use. Currently TF or TFIDF.Default: TFIDF

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Extract Features

Convert the full 20 newsgroups dataset into a < Text, Text >SequenceFile.

Convert and preprocesses the dataset into a < Text,VectorWritable > SequenceFile containing term frequencies foreach document.

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Prepare Testing Dataset

Split the preprocessed dataset into training and testing sets.

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Training process

Train the classifier.

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Testing the result

Test the classifier.

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Dumping a vector file

We can dump vector files to normal text ones, as fillow

mahout vectordump -i <input file> -o <output file>

Options--useKey If the Key is a vector than dump that instead

--csv Output the Vector as CSV--dictionary The dictionary file.

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Sample Output

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Command line options

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Command line options

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Command line options

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K-means clustering

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Reuters Newswire

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Preparing data

$ export WORK_DIR=/tmp/kmeans

$ mkdir $WORK_DIR

$ mkdir $WORK_DIR/reuters-out

$ cd $WORK_DIR

$ wget http://kdd.ics.uci.edu/databases/reuters21578/reuters21578.tar.gz

$ mkdir $WORK_DIR/reuters-sgm

$ tar -xzf reuters21578.tar.gz -C $WORK_DIR/reuters-sgm

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Convert input to a sequential file

$ mahout org.apache.lucene.benchmark.utils.ExtractReuters$WORK_DIR/reuters-sgm $WORK_DIR/reuters-out

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Convert input to a sequential file (cont)

$ mahout seqdirectory -i $WORK_DIR/reuters-out -o$WORK_DIR/reuters-out-seqdir -c UTF-8 -chunk 5

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Create the sparse vector files

$ mahout seq2sparse -i $WORK_DIR/reuters-out-seqdir/ -o $WORK_DIR/reuters-out-seqdir-sparse-kmeans --maxDFPercent 85 --namedVector

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Running K-Means

$ mahout kmeans -i $WORK_DIR/reuters-out-seqdir-sparse-kmeans/tfidf-vectors/ -c $WORK_DIR/reuters-kmeans-clusters-o $WORK_DIR/reuters-kmeans -dmorg.apache.mahout.common.distance.CosineDistanceMeasure-x 10 -k 20 -ow

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K-Means command line options

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Viewing Result

$mkdir $WORK_DIR/reuters-kmeans/clusteredPoints

$ mahout clusterdump -i $WORK_DIR/reuters-kmeans/clusters-*-final -o $WORK_DIR/reuters-kmeans/clusterdump -d$WORK_DIR/reuters-out-seqdir-sparse-kmeans/dictionary.file-0-dt sequencefile -b 100 -n 20 --evaluate -dmorg.apache.mahout.common.distance.CosineDistanceMeasure-sp 0 --pointsDir $WORK_DIR/reuters-kmeans/clusteredPoints

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Viewing Result

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Dumping a cluster file

We can dump cluster files to normal text ones, as fillow

mahout clusterdump -i <input file> -o <output file>

Options -of The optional output format for the results.

Options: TEXT, CSV, JSON or GRAPH_ML

-dt The dictionary file type

--evaluate Run ClusterEvaluator

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Canopy Clustering

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Fuzyy k-mean Clustering

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Command line options

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Exercise: Traffic Accidents Dataset

http://fimi.ua.ac.be/data/accidents.dat.gz

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Import-Export RDBMS data

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Sqoop Hands-On Labs

1. Loading Data into MySQL DB

2. Installing Sqoop

3. Configuring Sqoop

4. Installing DB driver for Sqoop

5. Importing data from MySQL to Hive Table

6. Reviewing data from Hive Table

7. Reviewing HDFS Database Table files

Thanachart Numnonda, thanachart@imcinstitute.com Feb 2015Big Data Hadoop on Amazon EMR – Hands On Workshop

1. MySQL RDS Server on AWS

A RDS Server is running on AWS with the followingconfiguration

> database: imc_db

> username: admin

> password: imcinstitute

>addr: imcinstitutedb.cmw65obdqfnx.us-west-2.rds.amazonaws.com

[This address may change]

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1. country_tbl data

Testing data query from MySQL DB

Table name > country_tbl

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2. Installing Sqoop

# wget http://apache.osuosl.org/sqoop/1.4.5/sqoop-1.4.5.bin__hadoop-1.0.0.tar.gz

# tar -xvzf sqoop-1.4.5.bin__hadoop-1.0.0.tar.gz

# sudo mv sqoop-1.4.5.bin__hadoop-1.0.0 /usr/local/

# rm sqoop-1.4.5.bin__hadoop-1.0.0

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Installing SqoopEdit $HOME ./bashrc

# sudo vi $HOME/.bashrc

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3. Configuring Sqoop

ubuntu@ip-172-31-12-11:~$ cd /usr/local/sqoop-1.4.5.bin__hadoop-1.0.0/conf/

ubuntu@ip-172-31-12-11:~$ vi sqoop-env.sh

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4. Installing DB driver for Sqoop

ubuntu@ip-172-31-12-11:~$ cd /usr/local/sqoop-1.4.5.bin__hadoop-1.0.0/lib/

ubuntu@ip-172-31-12-11:/usr/local/sqoop-1.4.5.bin__hadoop-1.0.05/lib$wget https://www.dropbox.com/s/6zrp5nerrwfixcj/mysql-connector-java-5.1.23-bin.jar

ubuntu@ip-172-31-12-11:/usr/local/sqoop-1.4.5.bin__hadoop-1.0.055/lib$exit

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5. Importing data from MySQL to Hive Table

[hdadmin@localhost ~]$sqoop import --connectjdbc:mysql://imcinstitutedb.cmw65obdqfnx.us-west-2.rds.amazonaws.com/imc_db --username admin -P --table country_tbl--hive-import --hive-table country -m 1

Warning: /usr/lib/hbase does not exist! HBase imports will fail.

Please set $HBASE_HOME to the root of your HBase installation.

Warning: $HADOOP_HOME is deprecated.

Enter password: <enter here>

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6. Reviewing data from Hive Table

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7. Reviewing HDFS Database Table files

Start Web Browser to http://http://54.68.149.232:50070 then navigate to /user/hive/warehouse

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Sqoop commands

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Recommended Books

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www.facebook.com/imcinstitute

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Thank you

thanachart@imcinstitute.comwww.facebook.com/imcinstitutewww.slideshare.net/imcinstitutewww.thanachart.org