Learning Apache Mahout: Acquire practical skills in Big Data Analytics and explore data science with Apache Mahout

Maven can be downloaded from one of the mirrors of the Apache website http://maven.apache.org/download.cgi. We use Apache Maven 3.2.5 and the same can be downloaded using this command:

wget http://apache.mirrors.tds.net/maven/maven-3/3.2.5/binaries/apache-maven-3.2.5-bin.tar.gz
cd /usr/local
sudo tar xzf $HOME/Downloads/ /usr/local/apache-maven-3.2.5-bin.tar.gz
sudo mv apache-maven-3.2.5 maven
sudo chown -R $USER maven

Mahout can be configured to be run with or without Hadoop. Currently, efforts are on to port Mahout on Apache Spark but it is in a nascent stage. We will discuss Mahout on Spark in Chapter 8, New Paradigm in Mahout. In this chapter, you are going to learn how to configure Mahout on top of Hadoop.

We will have two configurations for Mahout. The first we will use for practicing command line examples of Mahout and the other, compiled from source, will be used to develop Mahout code using Java API and Eclipse.

Though we can use one Mahout configuration, I will take this opportunity to discuss both approaches.

Download the latest Mahout version using one of the mirrors listed at the Apache Mahout website https://mahout.apache.org/general/downloads.html. The current release version is mahout-distribution-0.9.tar.gz. After the download completes, the archive should be in the Downloads folder under the user's home directory. Type the following on the command line. The first command moves the shell prompt to the /usr/local directory:

cd /usr/local

Extract the downloaded file to the directory mahout-distribution-0.9.tar.gz under the /usr/local directory. The command tar is used to extract the archive:

sudo tar xzf $HOME/Downloads/mahout-distribution-0.9.tar.gz

The third command mv renames the directory from mahout-distribution-0.9 to mahout:

sudo mv mahout-distribution-0.9 mahout

The last command chown changes the ownership of the file from the root user to the current user. The Linux command chown is used for changing the ownership of files and directories. The argument –R instructs the chown command to recursively change the ownership of subdirectories and $USER holds the value of the logged in user's username:

sudo chown -R $USER mahout

We need to update the .bashrc file to export the required variables and update the $PATH variable:

cd $HOME
vi .bashrc

At the end of the file, copy the following statements:

#Statements related to Mahout
export MAVEN_HOME=/usr/local/maven
export MAHOUT_HOME=/usr/local/mahout
PATH=$PATH:/bin:$MAVEN_HOME/bin:$MAHOUT_HOME/bin
###end of mahout statement

Exit from all existing terminals, start a new terminal, and enter the following command:

echo $PATH

Check whether the output has the path recently added to Maven and Mahout.

Type the following commands on the command line; both commands should be recognized:

mvn –-version
mahout

Download Eclipse from the Eclipse mirror mentioned on the home page. We have used Eclipse Kepler SR2 for this book. The downloaded archive should be in the Downloads folder of the user's home directory. Open a terminal and enter the following command:

cd /usr/local
sudo tar xzf $HOME/Downloads/eclipse-standard-kepler-SR2-linux-gtk-x86_64.tar.gz
sudo chown -R $USER eclipse

Go into the Eclipse directory and open up the Eclipse GUI. We will now install the Maven plugin. Click on Help then Eclipse Marketplace and then in the search panel type m2e and search. Once the search results are displayed hit Install and follow the instructions. To complete the installation hit the Next button and press the Accept button whenever prompted. Once the installation is done, Eclipse will prompt for a restart. Hit OK and let Eclipse restart.

Now to add Mahout dependency to any Maven project we need, add the following dependency in the pom.xml file:

    <dependency>
      <groupId>org.apache.mahout</groupId>
      <artifactId>mahout-core</artifactId>
      <version>0.9</version>
    </dependency>
    <dependency>
      <groupId>org.apache.mahout</groupId>
      <artifactId>mahout-examples</artifactId>
      <version>0.9</version>
    </dependency>
    <dependency>
      <groupId>org.apache.mahout</groupId>
      <artifactId>mahout-math</artifactId>
      <version>0.9</version>
    </dependency>

Eclipse will download and add all the dependencies.

Now we should import the code repository of this book to Eclipse. Open Eclipse and follow the following sequence of steps. The pom.xml file has all the dependencies included in it and Eclipse will download and resolve the dependencies.

Go to File | Import | Maven | Existing Maven Projects | Next | Browse to the location of the source folder that comes with this book | Finish.

In this section, we will discuss the command line implementation of clustering in Mahout and use the example script as reference.

On the terminal please type:

vi cluster-reuters.sh

This script clusters the Reuters dataset using a variety of algorithms. It downloads the dataset automatically, parses and copies it to HDFS (Hadoop Distributed File System), and based upon user input, runs the corresponding clustering algorithm.

On the vi terminal type the command:

:set number

This will display the line numbers of the lines in the file. The algorithms implemented are kmeans, fuzzykmeans, lda, and streamingkmeans; line 42 of the code has a list of all algorithms implemented in the script:

algorithm=( kmeansfuzzykmeansldastreamingkmeans) #A list of all algorithms implemented in the script

Input is taken from the user in line 51 by the read statement:

read -p "Enter your choice : " choice

Line 57 sets the temp working directory variable:

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

On line 79, the curl statement downloads the Reuters data to the working directory, first checking whether the file is already present in the working directory between lines 70 to 73:

curl http://kdd.ics.uci.edu/databases/reuters21578/reuters21578.tar.gz -o ${WORK_DIR}/reuters21578.tar.gz

From line 89, the Reuters tar is extracted to the reuters-sgm folder under the working directory:

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

Reuter's raw data file

Let's have a look at one of the raw files. Open the reut2-000.sgm file in a text editor such as vi or gedit.

The Reuter's raw file looks like this:

The Reuters data is distributed in 22 files, each of which contains 1,000 documents, except for the last (reut2-021.sgm), which contains 578 documents. The files are in the SGML (standard generalized markup language) format, which is similar to XML. The SGML file needs to be parsed.

On line 93, the Reuters data is parsed using Lucene. Lucene has built-in classes and functions to process different file formats. The logic of parsing the Reuters dataset is implemented in the ExtractReuters class. The SGML file is parsed and the text elements are extracted from it.

Tip

Apache Lucene is a free/open source information retrieval software library.

We will use the ExtractReuters class to extract the sgm file to text format.

$MAHOUT org.apache.lucene.benchmark.utils.ExtractReuters ${WORK_DIR}/reuters-sgm ${WORK_DIR}/reuters-out

Now let's look at the Reuters processed file. The following figure is a snapshot taken from the text file extracted from the sgm files we saw previously by the ExtractReuters class:

On lines 95 to 101, data is loaded from a local directory to HDFS, deleting the reuters-sgm and reuters-out folders if they already exist:

  echo "Copying Reuters data to Hadoop"
  $HADOOP dfs -rmr ${WORK_DIR}/reuters-sgm
  $HADOOP dfs -rmr ${WORK_DIR}/reuters-out
  $HADOOP dfs -put ${WORK_DIR}/reuters-sgm ${WORK_DIR}/reuters-sgm
  $HADOOP dfs -put ${WORK_DIR}/reuters-out ${WORK_DIR}/reuters-out

On line 105, the files are converted into sequence files. Mahout works with sequence files.

Tip

Sequence files are the standard input of Mahout machine learning algorithms.

$MAHOUT seqdirectory -i ${WORK_DIR}/reuters-out -o ${WORK_DIR}/reuters-out-seqdir -c UTF-8 -chunk 64 -xm sequential

On lines 109 to 111, the sequence file is converted to a vector representation. Text needs to be converted into a vector representation so that a machine learning algorithm can process it. We will talk about text vectorization in details in Chapter 10, Case Study – Text Analytics.

 $MAHOUT seq2sparse -i ${WORK_DIR}/reuters-out-seqdir/ 
  -o ${WORK_DIR}/reuters-out-seqdir-sparse-kmeans --maxDFPercent 85 – namedVector

From here on, we will only explain the k-means algorithm execution; we encourage you to read and understand the other three implementations too. A detailed discussion of clustering will be covered in Chapter 7, Clustering with Mahout.

Clustering is the process of partitioning a bunch of data points into related groups called clusters. K-means clustering partitions a dataset into a specified number of clusters by minimizing the distance between each data point and the center of the cluster using a distance metric. A distance metric is a way to define how far or near a data point is from another. K-means requires users to provide the number of clusters and optionally user-defined cluster centroids.

To better understand how data points are clustered together, please have a look at the sample figure displaying three clusters. Notice that the points that are nearby are grouped together into three distinct clusters. A few points don't belong to any clusters, those points represent outliers and should be removed prior to clustering.

Here is an example of the command line for k-means clustering:

Parameter	Description
--input (-i)	This is the path to the job input directory.
--clusters (-c)	These are the input centroids and they must be a SequenceFile of type Writable or Cluster/Canopy. If k is also specified, then a random set of vectors will be selected and written out to this path first.
--output (-o)	This is the directory pathname for the output.
--distanceMeasure	This is the class name of DistanceMeasure; the default is SquaredEuclidean.
--convergenceDelta	This is the convergence delta value; the default is 0.5.
--maxIter (-x)	This is the maximum number of iterations.
--maxRed (-r)	This is the number of reduce tasks; this defaults to 2.
--k (-k)	This is the k in k-means. If specified, then a random selection of k vectors will be chosen as the Centroid and written to the cluster's input path.
--overwrite (-ow)	If this is present, overwrite the output directory before running the job.
--help (-h)	This prints out Help.
--clustering (-cl)	If this is present, run clustering after the iterations have taken place.

Lines 113 to 118 take the sparse matrix and runs the k-means clustering algorithm using the cosine distance metric. We pass –k the number of clusters as 20 and –x the maximum number of iterations as 10:

     $MAHOUT kmeans \
    -i ${WORK_DIR}/reuters-out-seqdir-sparse-kmeans/tfidf-vectors/ \
    -c ${WORK_DIR}/reuters-kmeans-clusters \
    -o ${WORK_DIR}/reuters-kmeans \
    -dm org.apache.mahout.common.distance.CosineDistanceMeasure \
    -x 10 -k 20 -ow --clustering \

Lines 120 to 125 take the cluster dump utility, read the clusters in sequence file format, and convert them to text files:

 $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 -dm org.apache.mahout.common.distance.CosineDistanceMeasure -sp 0 \ 
    --pointsDir ${WORK_DIR}/reuters-kmeans/clusteredPoints \
&& \    
  cat ${WORK_DIR}/reuters-kmeans/clusterdump

The clusterdump utility outputs the center of each cluster and the top terms in the cluster. A sample of the output is shown here:

In this section, we will discuss the command line implementation of classification in Mahout and use the example script as a reference.

Classification is the task of identifying which set of predefined classes a data point belongs to. Classification involves training a model with a labeled (previously classified) dataset and then predicting new unlabeled data using that model. The common workflow for a classification problem is:

Repeat steps until the desired performance is achieved, or the best possible solution is achieved or the project's time is up.

On the terminal, please type:

vi classify-20newsgroups.sh

On the vi terminal, type the following command to show the line numbers for lines in the script:

:set number

The algorithms implemented in the script are cnaivebayes, naivebayes, sgd, and a last option clean, which cleans up the work directory

Line 44 creates a working directory for the dataset and all input/output:

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

Lines 64 to 74 download and extract the 20news-bydate.tar.gz file after making sure it is not already downloaded:

  if [ ! -e ${WORK_DIR}/20news-bayesinput ]; then
    if [ ! -e ${WORK_DIR}/20news-bydate ]; then
      if [ ! -f ${WORK_DIR}/20news-bydate.tar.gz ]; then
        echo "Downloading 20news-bydate"
        curl http://people.csail.mit.edu/jrennie/20Newsgroups/20news-bydate.tar.gz -o ${WORK_DIR}/20news-bydate.tar.gz
      fi
      mkdir -p ${WORK_DIR}/20news-bydate
      echo "Extracting..."
      cd ${WORK_DIR}/20news-bydate && tar xzf ../20news-bydate.tar.gz && cd ..&&cd ..
    fi
  fi

The 20 newsgroups dataset consists of messages, one per file. Each file begins with header lines that specify things such as who sent the message, how long it is, what kind of software was used, and the subject. A blank line follows, and then the message body follows as unformatted text.

Lines 90 to 101 prepare the directory and copy the data to the Hadoop directory:

  echo "Preparing 20newsgroups data"
  rm -rf ${WORK_DIR}/20news-all
  mkdir ${WORK_DIR}/20news-all
  cp -R ${WORK_DIR}/20news-bydate/*/* ${WORK_DIR}/20news-all

  if [ "$HADOOP_HOME" != "" ] && [ "$MAHOUT_LOCAL" == "" ] ; then
    echo "Copying 20newsgroups data to HDFS"
    set +e
    $HADOOP dfs -rmr ${WORK_DIR}/20news-all
    set -e
    $HADOOP dfs -put ${WORK_DIR}/20news-all ${WORK_DIR}/20news-all
  fi

A snapshot of the raw 20newsgroups data file is provided below.

Lines 104 to 106 convert the full 20 newsgroups dataset into sequence files:

$ mahout seqdirectory  -i ${WORK_DIR}/20news-all –o ${WORK_DIR}/20news-seq -ow

Lines 109 to 111 convert the sequence files to vectors calculating the term frequency and inverse document frequency. Term frequency and inverse document frequency are ways of representing text using numeric representation:

./bin/mahout seq2sparse \
-i ${WORK_DIR}/20news-seq \
-o ${WORK_DIR}/20news-vectors -lnorm -nv  -wt tfidf

Lines 114 to 118 split the preprocessed dataset into training and testing sets. The test set will be used to test the performance of the model trained using the training sets:

./bin/mahout split \
-i ${WORK_DIR}/20news-vectors/tfidf-vectors \
--trainingOutput ${WORK_DIR}/20news-train-vectors \
--testOutput ${WORK_DIR}/20news-test-vectors  \
--randomSelectionPct 40 --overwrite --sequenceFiles -xm sequential

Lines 120 to 125 train the classifier using the training sets:

./bin/mahout trainnb \
-i ${WORK_DIR}/20news-train-vectors -el \
-o ${WORK_DIR}/model \
-li ${WORK_DIR}/labelindex \
-ow $c

Lines 129 to 133 test the classifier using the test sets:

./bin/mahout testnb \
-i ${WORK_DIR}/20news-train-vectors\
-m ${WORK_DIR}/model \
-l ${WORK_DIR}/labelindex \
-ow -o ${WORK_DIR}/20news-testing $c

We will use the bank-additional-full.csv file present in the learningApacheMahout/data/chapter4 directory as the input for our example. First, let's have a look at the structure of the data and try to understand it. The following table shows various input variables along with their data types:

Based on many attributes of the customer, we try to predict the target variable y (has the client subscribed to a term deposit?), which has a set of two predefined values, Yes and No. We need to remove the header line to use the data.

We will use logistic regression to build the model; logistic regression is a statistical technique that computes the probability of an unclassified item belonging to a predefined class.

You might like to run the example with the code in the source code that ships with this book; I will explain the important steps in the following section. In Eclipse, open the code file OnlineLogisticRegressionTrain.java from the package chapter4.logistic.src, which is present in the directory learningApacheMahout/src/main/java/chapter4/src/logistic in the code folder that comes with this book.

The first step is to identify the source and target folders:

String inputFile = "data/chapter4/train_data/input_bank_data.csv";
String outputFile = "data/chapter4/model";

Once we know where to get the data from, we need to tell the algorithm about how to interpret the data. We pass the column name and the corresponding column type; here, n denotes the numeric column and w, the categorical columns of the data:

List<String> predictorList =Arrays.asList("age","job","marital","education","default","housing","loan","contact","month","day_of_week","duration","campaign","pdays","previous","poutcome","emp.var.rate","cons.price.idx","cons.conf.idx","euribor3m","nr.employed");

List<String> typeList = Arrays.asList("n","w","w","w","w","w","w","w","w","w","n","n","n","n","w","n","n","n","n","n");

Set the classifier parameters. LogisticModelParameters is a wrapper class, in Mahout's example distribution, used to set the parameters for training logistic regression and to return the instance of a CsvRecordFactory:

LogisticModelParameters lmp = new LogisticModelParameters();
        lmp.setTargetVariable("y");
        lmp.setMaxTargetCategories(2);
        lmp.setNumFeatures(20);
        lmp.setUseBias(false);
        lmp.setTypeMap(predictorList,typeList);
        lmp.setLearningRate(0.5);
        int passes = 50;

We set the the target variable y to be used for training, the maximum number of target categories to be 2 (Yes, No), the number of features or columns in the data excluding the target variable (which is 20), and some other settings (which we will learn about later in this book). The variable passed has been given a value of 50, which means the maximum number of iteration over the data will be 50.

The CsvRecordFactory class returns an object to parse the CSV file based on the parameters passed. The LogisticModelParameters class takes care of passing the parameters to the constructor of CsvRecordFactory. We use the class RandomAccessSparseVector to encode the data into vectors and train the model using lr.train(targetValue, input):

CsvRecordFactory csv = lmp.getCsvRecordFactory();
lr = lmp.createRegression();
for (int pass = 0; pass < passes; pass++) {
                BufferedReader in = new BufferedReader(new FileReader(inputFile));

                
                csv.firstLine(in.readLine());

                String line = in.readLine();
                int lineCount = 2;
                while (line != null) {
                  
                  Vector input = new RandomAccessSparseVector(lmp.getNumFeatures());
                  int targetValue = csv.processLine(line, input);

                  // update model
                  lr.train(targetValue, input);
                  k++;

                  line = in.readLine();
                  lineCount++;
                }
                in.close();
              }

The output after running the code would be an equation denoting the logistic regression. Excerpts of the equation are copied here:

y ~ -97.230*age + -12.713*campaign + . . .

You will learn about logistic regression, how to interpret the equation, and how to evaluate the results in detail in Chapter 4, Classification with Mahout.