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From rawkintr...@apache.org
Subject [10/29] mahout git commit: remove permalinks where not appropriate
Date Wed, 26 Apr 2017 03:04:52 GMT
http://git-wip-us.apache.org/repos/asf/mahout/blob/660036eb/website/programming_guide/tutorials/classify-a-doc-from-the-shell.md
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+---
+layout: mahoutdoc
+title: Text Classification Example
+theme:
+    name: mahout2
+---
+
+#Building a text classifier in Mahout's Spark Shell
+
+This tutorial will take you through the steps used to train a Multinomial Naive Bayes model
and create a text classifier based on that model using the mahout spark-shell.
+
+## Prerequisites
+This tutorial assumes that you have your Spark environment variables set for the mahout
spark-shell see: [Playing with Mahout's Shell](http://mahout.apache.org/users/sparkbindings/play-with-shell.html).
As well we assume that Mahout is running in cluster mode (i.e. with the MAHOUT_LOCAL
environment variable **unset**) as we'll be reading and writing to HDFS.
+
+*As of Mahout v. 0.10.0, we are still reliant on the MapReduce versions of mahout seqwiki
and mahout seq2sparse to extract and vectorize our text.  A* [*Spark implementation
of seq2sparse*](https://issues.apache.org/jira/browse/MAHOUT-1663) *is in the works for Mahout
v. 0.11.* However, to download the Wikipedia dataset, extract the bodies of the documentation,
label each document and vectorize the text into TF-IDF vectors, we can simpmly run the [wikipedia-classifier.sh](https://github.com/apache/mahout/blob/master/examples/bin/classify-wikipedia.sh)
example.
+
+    Please select a number to choose the corresponding task to run
+    1. CBayes (may require increased heap space on yarn)
+    2. BinaryCBayes
+    3. clean -- cleans up the work area in /tmp/mahout-work-wiki
+
+Enter (2). This will download a large recent XML dump of the Wikipedia database, into a /tmp/mahout-work-wiki
directory, unzip it and  place it into HDFS.  It will run a [MapReduce job to parse the wikipedia
set](http://mahout.apache.org/users/classification/wikipedia-classifier-example.html), extracting
and labeling only pages with category tags for [United States] and [United Kingdom] (~11600
documents). It will then run mahout seq2sparse to convert the documents into TF-IDF
vectors.  The script will also a build and test a [Naive Bayes model using MapReduce](http://mahout.apache.org/users/classification/bayesian.html).
When it is completed, you should see a confusion matrix on your screen.  For this tutorial,
we will ignore the MapReduce model, and build a new model using Spark based on the vectorized
text output by seq2sparse.
+
+## Getting Started
+
+Launch the mahout spark-shell.  There is an example script: spark-document-classifier.mscala
(.mscala denotes a Mahout-Scala script which can be run similarly to an R script).   We will
be walking through this script for this tutorial but if you wanted to simply run the script,
you could just issue the command:
+
+
+For now, lets take the script apart piece by piece.  You can cut and paste the following
code blocks into the mahout spark-shell.
+
+## Imports
+
+Our Mahout Naive Bayes imports:
+
+    import org.apache.mahout.classifier.naivebayes._
+    import org.apache.mahout.classifier.stats._
+    import org.apache.mahout.nlp.tfidf._
+
+
+
+## Read in our full set from HDFS as vectorized by seq2sparse in classify-wikipedia.sh
+
+    val pathToData = "/tmp/mahout-work-wiki/"
+    val fullData = drmDfsRead(pathToData + "wikipediaVecs/tfidf-vectors")
+
+## Extract the category of each observation and aggregate those observations by category
+
+    val (labelIndex, aggregatedObservations) = SparkNaiveBayes.extractLabelsAndAggregateObservations(
+                                                                 fullData)
+
+## Build a Muitinomial Naive Bayes model and self test on the training set
+
+    val model = SparkNaiveBayes.train(aggregatedObservations, labelIndex, false)
+    val resAnalyzer = SparkNaiveBayes.test(model, fullData, false)
+    println(resAnalyzer)
+
+printing the ResultAnalyzer will display the confusion matrix.
+
+## Read in the dictionary and document frequency count from HDFS
+
+    val dictionary = sdc.sequenceFile(pathToData + "wikipediaVecs/dictionary.file-0",
+                                      classOf[Text],
+                                      classOf[IntWritable])
+    val documentFrequencyCount = sdc.sequenceFile(pathToData + "wikipediaVecs/df-count",
+                                                  classOf[IntWritable],
+                                                  classOf[LongWritable])
+
+    // setup the dictionary and document frequency count as maps
+    val dictionaryRDD = dictionary.map {
+                                    case (wKey, wVal) => wKey.asInstanceOf[Text]
+                                                             .toString() -> wVal.get()

+                                       }
+
+    val documentFrequencyCountRDD = documentFrequencyCount.map {
+                                            case (wKey, wVal) => wKey.asInstanceOf[IntWritable]
+                                                                     .get() -> wVal.get()

+                                                               }
+
+    val dictionaryMap = dictionaryRDD.collect.map(x => x._1.toString -> x._2.toInt).toMap
+    val dfCountMap = documentFrequencyCountRDD.collect.map(x => x._1.toInt -> x._2.toLong).toMap
+
+## Define a function to tokenize and vectorize new text using our current dictionary
+
+For this simple example, our function vectorizeDocument(...) will tokenize a new document
into unigrams using native Java String methods and vectorize using our dictionary and document
frequencies. You could also use a [Lucene](https://lucene.apache.org/core/) analyzer for bigrams,
trigrams, etc., and integrate Apache [Tika](https://tika.apache.org/) to extract text from
different document types (PDF, PPT, XLS, etc.).  Here, however we will keep it simple, stripping
and tokenizing our text using regexs and native String methods.
+
+    def vectorizeDocument(document: String,
+                            dictionaryMap: Map[String,Int],
+                            dfMap: Map[Int,Long]): Vector = {
+        val wordCounts = document.replaceAll("[^\\p{L}\\p{Nd}]+", " ")
+                                    .toLowerCase
+                                    .split(" ")
+                                    .groupBy(identity)
+                                    .mapValues(_.length)
+        val vec = new RandomAccessSparseVector(dictionaryMap.size)
+        val totalDFSize = dfMap(-1)
+        val docSize = wordCounts.size
+        for (word <- wordCounts) {
+            val term = word._1
+            if (dictionaryMap.contains(term)) {
+                val tfidf: TermWeight = new TFIDF()
+                val termFreq = word._2
+                val dictIndex = dictionaryMap(term)
+                val docFreq = dfCountMap(dictIndex)
+                val currentTfIdf = tfidf.calculate(termFreq,
+                                                   docFreq.toInt,
+                                                   docSize,
+                                                   totalDFSize.toInt)
+                vec.setQuick(dictIndex, currentTfIdf)
+            }
+        }
+        vec
+    }
+
+## Setup our classifier
+
+    val labelMap = model.labelIndex
+    val numLabels = model.numLabels
+    val reverseLabelMap = labelMap.map(x => x._2 -> x._1)
+
+    // instantiate the correct type of classifier
+    val classifier = model.isComplementary match {
+        case true => new ComplementaryNBClassifier(model)
+        case _ => new StandardNBClassifier(model)
+    }
+
+## Define an argmax function
+
+The label with the highest score wins the classification for a given document.
+
+    def argmax(v: Vector): (Int, Double) = {
+        var bestIdx: Int = Integer.MIN_VALUE
+        var bestScore: Double = Integer.MIN_VALUE.asInstanceOf[Int].toDouble
+        for(i <- 0 until v.size) {
+            if(v(i) > bestScore){
+                bestScore = v(i)
+                bestIdx = i
+            }
+        }
+        (bestIdx, bestScore)
+    }
+
+## Define our TF(-IDF) vector classifier
+
+    def classifyDocument(clvec: Vector) : String = {
+        val cvec = classifier.classifyFull(clvec)
+        val (bestIdx, bestScore) = argmax(cvec)
+        reverseLabelMap(bestIdx)
+    }
+
+## Two sample news articles: United States Football and United Kingdom Football
+
+    // A random United States football article
+    // http://www.reuters.com/article/2015/01/28/us-nfl-superbowl-security-idUSKBN0L12JR20150128
+    val UStextToClassify = new String("(Reuters) - Super Bowl security officials acknowledge"
+
+        " the NFL championship game represents a high profile target on a world stage but
are" +
+        " unaware of any specific credible threats against Sunday's showcase. In advance
of" +
+        " one of the world's biggest single day sporting events, Homeland Security Secretary"
+
+        " Jeh Johnson was in Glendale on Wednesday to review security preparations and tour"
+
+        " University of Phoenix Stadium where the Seattle Seahawks and New England Patriots"
+
+        " will battle. Deadly shootings in Paris and arrest of suspects in Belgium, Greece
and" +
+        " Germany heightened fears of more attacks around the world and social media accounts"
+
+        " linked to Middle East militant groups have carried a number of threats to attack"
+
+        " high-profile U.S. events. There is no specific credible threat, said Johnson, who"
+
+        " has appointed a federal coordination team to work with local, state and federal"
+
+        " agencies to ensure safety of fans, players and other workers associated with the"
+
+        " Super Bowl. I'm confident we will have a safe and secure and successful event."
+
+        " Sunday's game has been given a Special Event Assessment Rating (SEAR) 1 rating,
the" +
+        " same as in previous years, except for the year after the Sept. 11, 2001 attacks,
when" +
+        " a higher level was declared. But security will be tight and visible around Super"
+
+        " Bowl-related events as well as during the game itself. All fans will pass through"
+
+        " metal detectors and pat downs. Over 4,000 private security personnel will be deployed"
+
+        " and the almost 3,000 member Phoenix police force will be on Super Bowl duty. Nuclear"
+
+        " device sniffing teams will be deployed and a network of Bio-Watch detectors will
be" +
+        " set up to provide a warning in the event of a biological attack. The Department
of" +
+        " Homeland Security (DHS) said in a press release it had held special cyber-security"
+
+        " and anti-sniper training sessions. A U.S. official said the Transportation Security"
+
+        " Administration, which is responsible for screening airline passengers, will add"
+
+        " screeners and checkpoint lanes at airports. Federal air marshals, behavior detection"
+
+        " officers and dog teams will help to secure transportation systems in the area.
We" +
+        " will be ramping it (security) up on Sunday, there is no doubt about that, said
Federal"+
+        " Coordinator Matthew Allen, the DHS point of contact for planning and support. I
have" +
+        " every confidence the public safety agencies that represented in the planning process"
+
+        " are going to have their best and brightest out there this weekend and we will have"
+
+        " a very safe Super Bowl.")
+
+    // A random United Kingdom football article
+    // http://www.reuters.com/article/2015/01/26/manchester-united-swissquote-idUSL6N0V52RZ20150126
+    val UKtextToClassify = new String("(Reuters) - Manchester United have signed a sponsorship"
+
+        " deal with online financial trading company Swissquote, expanding the commercial"
+
+        " partnerships that have helped to make the English club one of the richest teams
in" +
+        " world soccer. United did not give a value for the deal, the club's first in the
sector," +
+        " but said on Monday it was a multi-year agreement. The Premier League club, 20 times"
+
+        " English champions, claim to have 659 million followers around the globe, making
the" +
+        " United name attractive to major brands like Chevrolet cars and sportswear group
+        " Swissquote said the global deal would allow it to use United's popularity in Asia
to" +
+        " help it meet its targets for expansion in China. Among benefits from the deal,"
+
+        " Swissquote's clients will have a chance to meet United players and get behind the
scenes" +
+        " at the Old Trafford stadium. Swissquote is a Geneva-based online trading company
that" +
+        " allows retail investors to buy and sell foreign exchange, equities, bonds and other
asset" +
+        " classes. Like other retail FX brokers, Swissquote was left nursing losses on the
Swiss" +
+        " franc after Switzerland's central bank stunned markets this month by abandoning
its cap" +
+        " on the currency. The fallout from the abrupt move put rival and West Ham United
shirt" +
+        " sponsor Alpari UK into administration. Swissquote itself was forced to book a 25
million" +
+        " Swiss francs ($28 million) provision for its clients who were left out of pocket" + + " following the franc's surge. United's ability to grow revenues off the pitch has made" + + " them the second richest club in the world behind Spain's Real Madrid, despite a" + + " downturn in their playing fortunes. United Managing Director Richard Arnold said" + + " there was still lots of scope for United to develop sponsorships in other areas of" + + " business. The last quoted statistics that we had showed that of the top 25 sponsorship" + + " categories, we were only active in 15 of those, Arnold told Reuters. I think there is a" + + " huge potential still for the club, and the other thing we have seen is there is very" + + " significant growth even within categories. United have endured a tricky transition" + + " following the retirement of manager Alex Ferguson in 2013, finishing seventh in the" + + " Premier League last season and missing out on a place in the lucrative Champions League." + + " ($1 = 0.8910 Swiss francs) (Writing by Neil Maidment, additional reporting by Jemima"
+
+        " Kelly; editing by Keith Weir)")
+
+## Vectorize and classify our documents
+
+    val usVec = vectorizeDocument(UStextToClassify, dictionaryMap, dfCountMap)
+    val ukVec = vectorizeDocument(UKtextToClassify, dictionaryMap, dfCountMap)
+
+    println("Classifying the news article about superbowl security (united states)")
+    classifyDocument(usVec)
+
+    println("Classifying the news article about Manchester United (united kingdom)")
+    classifyDocument(ukVec)
+
+## Tie everything together in a new method to classify text
+
+    def classifyText(txt: String): String = {
+        val v = vectorizeDocument(txt, dictionaryMap, dfCountMap)
+        classifyDocument(v)
+    }
+
+## Now we can simply call our classifyText(...) method on any String
+
+    classifyText("Hello world from Queens")
+    classifyText("Hello world from London")
+
+## Model persistance
+
+You can save the model to HDFS:
+
+    model.dfsWrite("/path/to/model")
+
+And retrieve it with:
+
+
+The trained model can now be embedded in an external application.
\ No newline at end of file

http://git-wip-us.apache.org/repos/asf/mahout/blob/660036eb/website/programming_guide/tutorials/how-to-build-an-app.md
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+---
+layout: mahoutdoc
+title: Mahout Samsara In Core
+theme:
+    name: mahout2
+---
+#How to create and App using 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](https://github.com/pferrel/3-input-cooc)
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:
+
+* Install sbt (simple build tool) 0.13.x for [Mac](http://www.scala-sbt.org/release/tutorial/Installing-sbt-on-Mac.html),
[Linux](http://www.scala-sbt.org/release/tutorial/Installing-sbt-on-Linux.html) or [manual
instalation](http://www.scala-sbt.org/release/tutorial/Manual-Installation.html).
+* Install [Spark 1.1.1](https://spark.apache.org/docs/1.1.1/spark-standalone.html). Don't
forget to setup SPARK_HOME
to setup MAHOUT_HOME and MAHOUT_LOCAL
+
+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.
+
+For example, here is data/purchase.csv:
+
+    u1,iphone
+    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.
+
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)](http://mahout.apache.org/users/algorithms/intro-cooccurrence-spark.html)
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,
+          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
+    }
+
+
+Now that we have the data read in we can perform the cooccurrence calculation.
+
+    // actions.map creates an array of just the IndeedDatasets
+    val indicatorMatrices = SimilarityAnalysis.cooccurrencesIDSs(
+      actions.map(a => a._2))
+
+All we need to do now is write the indicators.
+
+    // 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)
+
+
+The writeIndicators method uses the default write function dfsWrite.
+
+    /**
+     * 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")
+
+
+##Build
+Building the examples from project's root folder:
+
+    $sbt pack + +This will automatically set up some launcher scripts for the driver. To run execute + +$ 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.
+
+![image](http://mahout.apache.org/images/debug-config.png)
+
+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.
+
+![image](http://mahout.apache.org/images/debug-config-2.png)
+
+
+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.
+
+##The Mahout Shell
+
+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.
+
+To make the CooccurrenceDriver.scala into a script make the following changes:
+
+* You won't need the context, since it is created when the shell is launched, comment that
line out.
+* Replace the logger.info lines with println
+* Remove the package info since it's not needed, this will produce the file in path/to/3-input-cooc/bin/CooccurrenceDriver.mscala.

+
+Note the extension .mscala to indicate we are using Mahout's scala extensions for math,
otherwise known as [Mahout-Samsara](http://mahout.apache.org/users/environment/out-of-core-reference.html)
+
+To run the code make sure the output does not exist already
+
+    $rm -r /path/to/3-input-cooc/data/indicators + +Launch the Mahout + Spark shell: + +$ mahout spark-shell
+
+You'll see the Mahout splash:
+
+
+                         _                 _
+             _ __ ___   __ _| |__   ___  _   _| |_
+            | '_  _ \ / _ | '_ \ / _ \| | | | __|
+            | | | | | | (_| | | | | (_) | |_| | |_
+            |_| |_| |_|\__,_|_| |_|\___/ \__,_|\__|  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.
platform... using builtin-java classes where applicable
+    Created spark context..
+    Mahout distributed context is available as "implicit val sdc".
+    mahout>
+
+
+    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>
+
+To run the driver type:
+
+    mahout> CooccurrenceDriver.main(args = Array(""))
+
+You'll get some stats printed:
+
+    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.

http://git-wip-us.apache.org/repos/asf/mahout/blob/660036eb/website/programming_guide/tutorials/play-with-shell.md
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diff --git a/website/programming_guide/tutorials/play-with-shell.md b/website/programming_guide/tutorials/play-with-shell.md
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+---
+layout: mahoutdoc
+title: Mahout Samsara In Core
+theme:
+    name: mahout2
+---
+# Playing with Mahout's Spark Shell
+
+This tutorial will show you how to play with Mahout's scala DSL for linear algebra and its
Spark shell. **Please keep in mind that this code is still in a very early experimental stage**.
+
+_(Edited for 0.10.2)_
+
+## Intro
+
+We'll use an excerpt of a publicly available [dataset about cereals](http://lib.stat.cmu.edu/DASL/Datafiles/Cereals.html).
The dataset tells the protein, fat, carbohydrate and sugars (in milligrams) contained in a
set of cereals, as well as a customer rating for the cereals. Our aim for this example is
to fit a linear model which infers the customer rating from the ingredients.
+
+
+Name                    | protein | fat | carbo | sugars | rating
+:-----------------------|:--------|:----|:------|:-------|:---------
+Apple Cinnamon Cheerios | 2       | 2   | 10.5  | 10     | 29.509541
+Cap'n'Crunch            | 1       | 2   | 12    | 12     | 18.042851
+Cocoa Puffs             | 1       | 1   | 12    | 13     | 22.736446
+Froot Loops             | 2       |	1   | 11    | 13     | 32.207582
+Honey Graham Ohs        | 1       |	2   | 12    | 11     | 21.871292
+Wheaties Honey Gold     | 2       | 1   | 16    |  8     | 36.187559
+Cheerios                | 6       |	2   | 17    |  1     | 50.764999
+Clusters                | 3       |	2   | 13    |  7     | 40.400208
+Great Grains Pecan      | 3       | 3   | 13    |  4     | 45.811716
+
+
+## Installing Mahout & Spark on your local machine
+
+We describe how to do a quick toy setup of Spark & Mahout on your local machine, so that
you can run this example and play with the shell.
+
and unpack the archive file
+ 1. Change to the directory where you unpacked Spark and type sbt/sbt assembly to build
it
+ 1. Create a directory for Mahout somewhere on your machine, change to there and checkout
the master branch of Apache Mahout from GitHub git clone https://github.com/apache/mahout
mahout
+ 1. Change to the mahout directory and build mahout using mvn -DskipTests clean
install
+
+## Starting Mahout's Spark shell
+
+ 1. Goto the directory where you unpacked Spark and type sbin/start-all.sh to locally
start Spark
+ 1. Open a browser, point it to [http://localhost:8080/](http://localhost:8080/) to check
whether Spark successfully started. Copy the url of the spark master at the top of the page
(it starts with **spark://**)
+ 1. Define the following environment variables: <pre class="codehilite">export MAHOUT_HOME=[directory
into which you checked out Mahout]
+export SPARK_HOME=[directory where you unpacked Spark]
+export MASTER=[url of the Spark master]
+</pre>
+ 1. Finally, change to the directory where you unpacked Mahout and type bin/mahout spark-shell,

+you should see the shell starting and get the prompt mahout> . Check
+[FAQ](http://mahout.apache.org/users/sparkbindings/faq.html) for further troubleshooting.
+
+## Implementation
+
+We'll use the shell to interactively play with the data and incrementally implement a simple
[linear regression](https://en.wikipedia.org/wiki/Linear_regression) algorithm. Let's first
load the dataset. Usually, we wouldn't need Mahout unless we processed a large dataset stored
in a distributed filesystem. But for the sake of this example, we'll use our tiny toy dataset
and "pretend" it was too big to fit onto a single machine.
+
+*Note: You can incrementally follow the example by copy-and-pasting the code into your running
Mahout shell.*
+
+Mahout's linear algebra DSL has an abstraction called *DistributedRowMatrix (DRM)* which
models a matrix that is partitioned by rows and stored in the memory of a cluster of machines.
We use dense() to create a dense in-memory matrix from our toy dataset and use drmParallelize
to load it into the cluster, "mimicking" a large, partitioned dataset.
+
+<div class="codehilite"><pre>
+val drmData = drmParallelize(dense(
+  (2, 2, 10.5, 10, 29.509541),  // Apple Cinnamon Cheerios
+  (1, 2, 12,   12, 18.042851),  // Cap'n'Crunch
+  (1, 1, 12,   13, 22.736446),  // Cocoa Puffs
+  (2, 1, 11,   13, 32.207582),  // Froot Loops
+  (1, 2, 12,   11, 21.871292),  // Honey Graham Ohs
+  (2, 1, 16,   8,  36.187559),  // Wheaties Honey Gold
+  (6, 2, 17,   1,  50.764999),  // Cheerios
+  (3, 2, 13,   7,  40.400208),  // Clusters
+  (3, 3, 13,   4,  45.811716)), // Great Grains Pecan
+  numPartitions = 2);
+</pre></div>
+
+Have a look at this matrix. The first four columns represent the ingredients
+(our features) and the last column (the rating) is the target variable for
+our regression. [Linear regression](https://en.wikipedia.org/wiki/Linear_regression)
+assumes that the **target variable** $$\mathbf{y}$$ is generated by the
+linear combination of **the feature matrix** $$\mathbf{X}$$ with the
+**parameter vector** $$\boldsymbol{\beta}$$ plus the
+ **noise** $$\boldsymbol{\varepsilon}$$, summarized in the formula
+$$\mathbf{y}=\mathbf{X}\boldsymbol{\beta}+\boldsymbol{\varepsilon}$$.
+Our goal is to find an estimate of the parameter vector
+$$\boldsymbol{\beta}$$ that explains the data very well.
+
+As a first step, we extract $$\mathbf{X}$$ and $$\mathbf{y}$$ from our data matrix. We
get *X* by slicing: we take all rows (denoted by ::) and the first four columns, which
have the ingredients in milligrams as content. Note that the result is again a DRM. The shell
will not execute this code yet, it saves the history of operations and defers the execution
until we really access a result. **Mahout's DSL automatically optimizes and parallelizes all
operations on DRMs and runs them on Apache Spark.**
+
+<div class="codehilite"><pre>
+val drmX = drmData(::, 0 until 4)
+</pre></div>
+
+Next, we extract the target variable vector *y*, the fifth column of the data matrix. We
assume this one fits into our driver machine, so we fetch it into memory using collect:
+
+<div class="codehilite"><pre>
+val y = drmData.collect(::, 4)
+</pre></div>
+
+Now we are ready to think about a mathematical way to estimate the parameter vector *β*.
A simple textbook approach is [ordinary least squares (OLS)](https://en.wikipedia.org/wiki/Ordinary_least_squares),
which minimizes the sum of residual squares between the true target variable and the prediction
of the target variable. In OLS, there is even a closed form expression for estimating $$\boldsymbol{\beta}$$
as
+$$\left(\mathbf{X}^{\top}\mathbf{X}\right)^{-1}\mathbf{X}^{\top}\mathbf{y}$$.
+
+The first thing which we compute for this is  $$\mathbf{X}^{\top}\mathbf{X}$$. The code
for doing this in Mahout's scala DSL maps directly to the mathematical formula. The operation
.t() transposes a matrix and analogous to R %*% denotes matrix multiplication.
+
+<div class="codehilite"><pre>
+val drmXtX = drmX.t %*% drmX
+</pre></div>
+
+The same is true for computing $$\mathbf{X}^{\top}\mathbf{y}$$. We can simply type the
math in scala expressions into the shell. Here, *X* lives in the cluster, while is *y* in
the memory of the driver, and the result is a DRM again.
+<div class="codehilite"><pre>
+val drmXty = drmX.t %*% y
+</pre></div>
+
+We're nearly done. The next step we take is to fetch $$\mathbf{X}^{\top}\mathbf{X}$$ and

+$$\mathbf{X}^{\top}\mathbf{y}$$ into the memory of our driver machine (we are targeting

+features matrices that are tall and skinny ,
+so we can assume that $$\mathbf{X}^{\top}\mathbf{X}$$ is small enough
+to fit in). Then, we provide them to an in-memory solver (Mahout provides
+the an analog to R's solve() for that) which computes beta, our
+OLS estimate of the parameter vector $$\boldsymbol{\beta}$$.
+
+<div class="codehilite"><pre>
+val XtX = drmXtX.collect
+val Xty = drmXty.collect(::, 0)
+
+val beta = solve(XtX, Xty)
+</pre></div>
+
+That's it! We have a implemented a distributed linear regression algorithm
+on Apache Spark. I hope you agree that we didn't have to worry a lot about
+parallelization and distributed systems. The goal of Mahout's linear algebra
+DSL is to abstract away the ugliness of programming a distributed system
+as much as possible, while still retaining decent performance and
+scalability.
+
+We can now check how well our model fits its training data.
+First, we multiply the feature matrix $$\mathbf{X}$$ by our estimate of
+$$\boldsymbol{\beta}$$. Then, we look at the difference (via L2-norm) of
+the target variable $$\mathbf{y}$$ to the fitted target variable:
+
+<div class="codehilite"><pre>
+val yFitted = (drmX %*% beta).collect(::, 0)
+(y - yFitted).norm(2)
+</pre></div>
+
+We hope that we could show that Mahout's shell allows people to interactively and incrementally
write algorithms. We have entered a lot of individual commands, one-by-one, until we got the
desired results. We can now refactor a little by wrapping our statements into easy-to-use
functions. The definition of functions follows standard scala syntax.
+
+We put all the commands for ordinary least squares into a function ols.
+
+<div class="codehilite"><pre>
+def ols(drmX: DrmLike[Int], y: Vector) =
+  solve(drmX.t %*% drmX, drmX.t %*% y)(::, 0)
+
+</pre></div>
+
+Note that DSL declares implicit collect if coersion rules require an in-core argument.
Hence, we can simply
+skip explicit collects.
+
+Next, we define a function goodnessOfFit that tells how well a model fits the target
variable:
+
+<div class="codehilite"><pre>
+def goodnessOfFit(drmX: DrmLike[Int], beta: Vector, y: Vector) = {
+  val fittedY = (drmX %*% beta).collect(::, 0)
+  (y - fittedY).norm(2)
+}
+</pre></div>
+
+So far we have left out an important aspect of a standard linear regression
+model. Usually there is a constant bias term added to the model. Without
+that, our model always crosses through the origin and we only learn the
+right angle. An easy way to add such a bias term to our model is to add a
+column of ones to the feature matrix $$\mathbf{X}$$.
+The corresponding weight in the parameter vector will then be the bias term.
+
+Here is how we add a bias column:
+
+<div class="codehilite"><pre>
+val drmXwithBiasColumn = drmX cbind 1
+</pre></div>
+
+Now we can give the newly created DRM drmXwithBiasColumn to our model fitting method
ols and see how well the resulting model fits the training data with goodnessOfFit.
You should see a large improvement in the result.
+
+<div class="codehilite"><pre>
+val betaWithBiasTerm = ols(drmXwithBiasColumn, y)
+goodnessOfFit(drmXwithBiasColumn, betaWithBiasTerm, y)
+</pre></div>
+
+As a further optimization, we can make use of the DSL's caching functionality. We use drmXwithBiasColumn
repeatedly  as input to a computation, so it might be beneficial to cache it in memory. This
is achieved by calling checkpoint(). In the end, we remove it from the cache with uncache:
+
+<div class="codehilite"><pre>
+val cachedDrmX = drmXwithBiasColumn.checkpoint()
+
+val betaWithBiasTerm = ols(cachedDrmX, y)
+val goodness = goodnessOfFit(cachedDrmX, betaWithBiasTerm, y)
+
+cachedDrmX.uncache()
+
+goodness
+</pre></div>
+
+
+Liked what you saw? Checkout Mahout's overview for the [Scala and Spark bindings](https://mahout.apache.org/users/sparkbindings/home.html).
\ No newline at end of file

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