Apache Mahout

This is an example of how to create a simple app using Mahout as a Library. The source is available on Github in the 3-input-cooc project with more explanation about what it does (has to do with collaborative filtering). For this tutorial we’ll concentrate on the app rather than the data science.

The app reads in three user-item interactions types and creats indicators for them using cooccurrence and cross-cooccurrence. The indicators will be written to text files in a format ready for search engine indexing in search engine based recommender.

##Setup In order to build and run the CooccurrenceDriver you need to install the following:

Why install if you are only using them as a library? Certain binaries and scripts are required by the libraries to get information about the environment like discovering where jars are located.

Spark requires a set of jars on the classpath for the client side part of an app and another set of jars must be passed to the Spark Context for running distributed code. The example should discover all the neccessary classes automatically.

##Application Using Mahout as a library in an application will require a little Scala code. Scala has an App trait so we’ll create an object, which inherits from App

object CooccurrenceDriver extends App {
}

This will look a little different than Java since App does delayed initialization, which causes the body to be executed when the App is launched, just as in Java you would create a main method.

Before we can execute something on Spark we’ll need to create a context. We could use raw Spark calls here but default values are setup for a Mahout context by using the Mahout helper function.

implicit val mc = mahoutSparkContext(masterUrl = "local", 
  appName = "CooccurrenceDriver")

We need to read in three files containing different interaction types. The files will each be read into a Mahout IndexedDataset. This allows us to preserve application-specific user and item IDs throughout the calculations.

u1,iphone
u1,ipad
u2,nexus
u2,galaxy
u3,surface
u4,iphone
u4,galaxy

Mahout has a helper function that reads the text delimited files SparkEngine.indexedDatasetDFSReadElements. The function reads single element tuples (user-id,item-id) in a distributed way to create the IndexedDataset. Distributed Row Matrices (DRM) and Vectors are important data types supplied by Mahout and IndexedDataset is like a very lightweight Dataframe in R, it wraps a DRM with HashBiMaps for row and column IDs.

One important thing to note about this example is that we read in all datasets before we adjust the number of rows in them to match the total number of users in the data. This is so the math works out (A’A, A’B, A’C) even if some users took one action but not another there must be the same number of rows in all matrices.

/**
 * Read files of element tuples and create IndexedDatasets one per action. These 
 * share a userID BiMap but have their own itemID BiMaps
 */
def readActions(actionInput: Array[(String, String)]): Array[(String, IndexedDataset)] = {
  var actions = Array[(String, IndexedDataset)]()

  val userDictionary: BiMap[String, Int] = HashBiMap.create()

  // The first action named in the sequence is the "primary" action and 
  // begins to fill up the user dictionary
  for ( actionDescription <- actionInput ) {// grab the path to actions
    val action: IndexedDataset = SparkEngine.indexedDatasetDFSReadElements(
      actionDescription._2,
      schema = DefaultIndexedDatasetElementReadSchema,
      existingRowIDs = userDictionary)
    userDictionary.putAll(action.rowIDs)
    // put the name in the tuple with the indexedDataset
    actions = actions :+ (actionDescription._1, action) 
  }

  // After all actions are read in the userDictonary will contain every user seen, 
  // even if they may not have taken all actions . Now we adjust the row rank of 
  // all IndxedDataset's to have this number of rows
  // Note: this is very important or the cooccurrence calc may fail
  val numUsers = userDictionary.size() // one more than the cardinality

  val resizedNameActionPairs = actions.map { a =>
    //resize the matrix by, in effect by adding empty rows
    val resizedMatrix = a._2.create(a._2.matrix, userDictionary, a._2.columnIDs).newRowCardinality(numUsers)
    (a._1, resizedMatrix) // return the Tuple of (name, IndexedDataset)
  }
  resizedNameActionPairs // return the array of Tuples
}

// actions.map creates an array of just the IndeedDatasets
val indicatorMatrices = SimilarityAnalysis.cooccurrencesIDSs(
  actions.map(a => a._2))

// zip a pair of arrays into an array of pairs, reattaching the action names
val indicatorDescriptions = actions.map(a => a._1).zip(indicatorMatrices)
writeIndicators(indicatorDescriptions)

/**
 * Write indicatorMatrices to the output dir in the default format
 * for indexing by a search engine.
 */
def writeIndicators( indicators: Array[(String, IndexedDataset)]) = {
  for (indicator <- indicators ) {
    // create a name based on the type of indicator
    val indicatorDir = OutputPath + indicator._1
    indicator._2.dfsWrite(
      indicatorDir,
      // Schema tells the writer to omit LLR strengths 
      // and format for search engine indexing
      IndexedDatasetWriteBooleanSchema) 
  }
}

See the Github project for the full source. Now we create a build.sbt to build the example.

name := "cooccurrence-driver"

organization := "com.finderbots"

version := "0.1"

scalaVersion := "2.10.4"

val sparkVersion = "1.1.1"

libraryDependencies ++= Seq(
  "log4j" % "log4j" % "1.2.17",
  // Mahout's Spark code
  "commons-io" % "commons-io" % "2.4",
  "org.apache.mahout" % "mahout-math-scala_2.10" % "0.10.0",
  "org.apache.mahout" % "mahout-spark_2.10" % "0.10.0",
  "org.apache.mahout" % "mahout-math" % "0.10.0",
  "org.apache.mahout" % "mahout-hdfs" % "0.10.0",
  // Google collections, AKA Guava
  "com.google.guava" % "guava" % "16.0")

resolvers += "typesafe repo" at " http://repo.typesafe.com/typesafe/releases/"

resolvers += Resolver.mavenLocal

packSettings

packMain := Map(
  "cooc" -> "CooccurrenceDriver")

This will automatically set up some launcher scripts for the driver. To run execute

$ target/pack/bin/cooc

The driver will execute in Spark standalone mode and put the data in /path/to/3-input-cooc/data/indicators/indicator-type

##Using a Debugger To build and run this example in a debugger like IntelliJ IDEA. Install from the IntelliJ site and add the Scala plugin.

Open IDEA and go to the menu File->New->Project from existing sources->SBT->/path/to/3-input-cooc. This will create an IDEA project from build.sbt in the root directory.

At this point you may create a “Debug Configuration” to run. In the menu choose Run->Edit Configurations. Under “Default” choose “Application”. In the dialog hit the elipsis button “…” to the right of “Environment Variables” and fill in your versions of JAVA_HOME, SPARK_HOME, and MAHOUT_HOME. In configuration editor under “Use classpath from” choose root-3-input-cooc module.

Now choose “Application” in the left pane and hit the plus sign “+”. give the config a name and hit the elipsis button to the right of the “Main class” field as shown.

After setting breakpoints you are now ready to debug the configuration. Go to the Run->Debug… menu and pick your configuration. This will execute using a local standalone instance of Spark.

For small script-like apps you may wish to use the Mahout shell. It is a Scala REPL type interactive shell built on the Spark shell with Mahout-Samsara extensions.

Note the extension .mscala to indicate we are using Mahout’s scala extensions for math, otherwise known as Mahout-Samsara

$ rm -r /path/to/3-input-cooc/data/indicators

$ mahout spark-shell

MAHOUT_LOCAL is set, so we don't add HADOOP_CONF_DIR to classpath.

                     _                 _
         _ __ ___   __ _| |__   ___  _   _| |_
        | '_ ` _ \ / _` | '_ \ / _ \| | | | __|
        | | | | | | (_| | | | | (_) | |_| | |_
        |_| |_| |_|\__,_|_| |_|\___/ \__,_|\__|  version 0.10.0

  
Using Scala version 2.10.4 (Java HotSpot(TM) 64-Bit Server VM, Java 1.7.0_72)
Type in expressions to have them evaluated.
Type :help for more information.
15/04/26 09:30:48 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
Created spark context..
Mahout distributed context is available as "implicit val sdc".
mahout>

mahout> :load /path/to/3-input-cooc/bin/CooccurrenceDriver.mscala
Loading ./bin/CooccurrenceDriver.mscala...
import com.google.common.collect.{HashBiMap, BiMap}
import org.apache.log4j.Logger
import org.apache.mahout.math.cf.SimilarityAnalysis
import org.apache.mahout.math.indexeddataset._
import org.apache.mahout.sparkbindings._
import scala.collection.immutable.HashMap
defined module CooccurrenceDriver
mahout>

mahout> CooccurrenceDriver.main(args = Array(""))

Total number of users for all actions = 5
purchase indicator matrix:
  Number of rows for matrix = 4
  Number of columns for matrix = 5
  Number of rows after resize = 5
view indicator matrix:
  Number of rows for matrix = 4
  Number of columns for matrix = 5
  Number of rows after resize = 5
category indicator matrix:
  Number of rows for matrix = 5
  Number of columns for matrix = 7
  Number of rows after resize = 5

If you look in path/to/3-input-cooc/data/indicators you should find folders containing the indicator matrices.

How to create and App using Mahout