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href="http://lucene.apache.org/";>Lucene</a></li> + <li><a href="http://hadoop.apache.org/";>Hadoop</a></li> + </ul> + </div> +</div> + + <div id="content-wrap" class="clearfix"> + <div id="main"> + <h1 id="classifying-with-random-forests">Classifying with random forests</h1> +<p><a name="PartialImplementation-Introduction"></a></p> +<h1 id="introduction">Introduction</h1> +<p>This quick start page shows how to build a decision forest using the +partial implementation. This tutorial also explains how to use the decision +forest to classify new data. +Partial Decision Forests is a mapreduce implementation where each mapper +builds a subset of the forest using only the data available in its +partition. This allows building forests using large datasets as long as +each partition can be loaded in-memory.</p> +<p><a name="PartialImplementation-Steps"></a></p> +<h1 id="steps">Steps</h1> +<p><a name="PartialImplementation-Downloadthedata"></a></p> +<h2 id="download-the-data">Download the data</h2> +<ul> +<li>The current implementation is compatible with the UCI repository file +format. In this example we'll use the NSL-KDD dataset because its large +enough to show the performances of the partial implementation. +You can download the dataset here http://nsl.cs.unb.ca/NSL-KDD/ +You can either download the full training set "KDDTrain+.ARFF", or a 20% +subset "KDDTrain+_20Percent.ARFF" (we'll use the full dataset in this +tutorial) and the test set "KDDTest+.ARFF".</li> +<li>Open the train and test files and remove all the lines that begin with +'@'. All those lines are at the top of the files. Actually you can keep +those lines somewhere, because they'll help us describe the dataset to +Mahout</li> +<li>Put the data in HDFS: {code} +$HADOOP_HOME/bin/hadoop fs -mkdir testdata +$HADOOP_HOME/bin/hadoop fs -put <PATH TO DATA> testdata{code}</li> +</ul> +<p><a name="PartialImplementation-BuildtheJobfiles"></a></p> +<h2 id="build-the-job-files">Build the Job files</h2> +<ul> +<li>In $MAHOUT_HOME/ run: {code}mvn clean install -DskipTests{code}</li> +</ul> +<p><a name="PartialImplementation-Generateafiledescriptorforthedataset:"></a></p> +<h2 id="generate-a-file-descriptor-for-the-dataset">Generate a file descriptor for the dataset:</h2> +<p>run the following command:</p> +<div class="codehilite"><pre>$<span class="n">HADOOP_HOME</span><span class="o">/</span><span class="n">bin</span><span class="o">/</span><span class="n">hadoop</span> <span class="n">jar</span> +</pre></div> + + +<p>$MAHOUT_HOME/core/target/mahout-core-<VERSION>-job.jar +org.apache.mahout.classifier.df.tools.Describe -p testdata/KDDTrain+.arff +-f testdata/KDDTrain+.info -d N 3 C 2 N C 4 N C 8 N 2 C 19 N L</p> +<p>The "N 3 C 2 N C 4 N C 8 N 2 C 19 N L" string describes all the attributes +of the data. In this cases, it means 1 numerical(N) attribute, followed by +3 Categorical(C) attributes, ...L indicates the label. You can also use 'I' +to ignore some attributes</p> +<p><a name="PartialImplementation-Runtheexample"></a></p> +<h2 id="run-the-example">Run the example</h2> +<div class="codehilite"><pre>$<span class="n">HADOOP_HOME</span><span class="o">/</span><span class="n">bin</span><span class="o">/</span><span class="n">hadoop</span> <span class="n">jar</span> +</pre></div> + + +<p>$MAHOUT_HOME/examples/target/mahout-examples-<version>-job.jar +org.apache.mahout.classifier.df.mapreduce.BuildForest +-Dmapred.max.split.size=1874231 -d testdata/KDDTrain+.arff -ds +testdata/KDDTrain+.info -sl 5 -p -t 100 -o nsl-forest</p> +<p>which builds 100 trees (-t argument) using the partial implementation (-p). +Each tree is built using 5 random selected attribute per node (-sl +argument) and the example outputs the decision tree in the "nsl-forest" +directory (-o). +The number of partitions is controlled by the -Dmapred.max.split.size +argument that indicates to Hadoop the max. size of each partition, in this +case 1/10 of the size of the dataset. Thus 10 partitions will be used. +IMPORTANT: using less partitions should give better classification results, +but needs a lot of memory. So if the Jobs are failing, try increasing the +number of partitions. +* The example outputs the Build Time and the oob error estimation</p> +<div class="codehilite"><pre>10<span class="o">/</span>03<span class="o">/</span>13 17<span class="p">:</span>57<span class="p">:</span>29 <span class="n">INFO</span> <span class="n">mapreduce</span><span class="p">.</span><span class="n">BuildForest</span><span class="p">:</span> <span class="n">Build</span> <span class="n">Time</span><span class="p">:</span> 0<span class="n">h</span> 7<span class="n">m</span> 43<span class="n">s</span> 582 +10<span class="o">/</span>03<span class="o">/</span>13 17<span class="p">:</span>57<span class="p">:</span>33 <span class="n">INFO</span> <span class="n">mapreduce</span><span class="p">.</span><span class="n">BuildForest</span><span class="p">:</span> <span class="n">oob</span> <span class="n">error</span> <span class="n">estimate</span> <span class="p">:</span> +</pre></div> + + +<p>0.002325895231517865 + 10/03/13 17:57:33 INFO mapreduce.BuildForest: Storing the forest in: +nsl-forest/forest.seq</p> +<p><a name="PartialImplementation-UsingtheDecisionForesttoClassifynewdata"></a></p> +<h2 id="using-the-decision-forest-to-classify-new-data">Using the Decision Forest to Classify new data</h2> +<p>run the following command:</p> +<div class="codehilite"><pre>$<span class="n">HADOOP_HOME</span><span class="o">/</span><span class="n">bin</span><span class="o">/</span><span class="n">hadoop</span> <span class="n">jar</span> +</pre></div> + + +<p>$MAHOUT_HOME/examples/target/mahout-examples-<version>-job.jar +org.apache.mahout.classifier.df.mapreduce.TestForest -i +nsl-kdd/KDDTest+.arff -ds nsl-kdd/KDDTrain+.info -m nsl-forest -a -mr -o +predictions</p> +<p>This will compute the predictions of "KDDTest+.arff" dataset (-i argument) +using the same data descriptor generated for the training dataset (-ds) and +the decision forest built previously (-m). Optionally (if the test dataset +contains the labels of the tuples) run the analyzer to compute the +confusion matrix (-a), and you can also store the predictions in a text +file or a directory of text files(-o). Passing the (-mr) parameter will use +Hadoop to distribute the classification.</p> +<ul> +<li> +<p>The example should output the classification time and the confusion +matrix</p> +<p>10/03/13 18:08:56 INFO mapreduce.TestForest: Classification Time: 0h 0m 6s +355 +10/03/13 18:08:56 INFO mapreduce.TestForest: +======================================================= +Summary</p> +<hr /> +<p>Correctly Classified Instances : 17657 78.3224% +Incorrectly Classified Instances : 4887 21.6776% +Total Classified Instances : 22544</p> +<p>======================================================= +Confusion Matrix</p> +<hr /> +<p>a b <--Classified as +9459 252 | 9711 a = normal +4635 8198 | 12833 b = anomaly +Default Category: unknown: 2</p> +</li> +</ul> +<p>If the input is a single file then the output will be a single text file, +in the above example 'predictions' would be one single file. If the input +if a directory containing for example two files 'a.data' and 'b.data', then +the output will be a directory 'predictions' containing two files +'a.data.out' and 'b.data.out'</p> +<p><a name="PartialImplementation-KnownIssuesandlimitations"></a></p> +<h2 id="known-issues-and-limitations">Known Issues and limitations</h2> +<p>The "Decision Forest" code is still "a work in progress", many features are +still missing. Here is a list of some known issues: +<em> For now, the training does not support multiple input files. The input +dataset must be one single file (this support will be available with the upcoming release). +Classifying new data does support multiple +input files. +</em> The tree building is done when each mapper.close() method is called. +Because the mappers don't refresh their state, the job can fail when the +dataset is big and you try to build a large number of trees.</p> + </div> + </div> +</div> + <footer class="footer" align="center"> + <div class="container"> + <p> + Copyright © 2014 The Apache Software Foundation, Licensed under + the <a href="http://www.apache.org/licenses/LICENSE-2.0";>Apache License, Version 2.0</a>. + <br /> + Apache and the Apache feather logos are trademarks of The Apache Software Foundation. + </p> + </div> + </footer> + + <script src="/js/jquery-1.9.1.min.js"></script> + <script src="/js/bootstrap.min.js"></script> + <script> + (function() { + var cx = '012254517474945470291:vhsfv7eokdc'; + var gcse = document.createElement('script'); + gcse.type = 'text/javascript'; + gcse.async = true; + gcse.src = (document.location.protocol == 'https:' ? 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href="http://lucene.apache.org/";>Lucene</a></li> + <li><a href="http://hadoop.apache.org/";>Hadoop</a></li> + </ul> + </div> +</div> + + <div id="content-wrap" class="clearfix"> + <div id="main"> + <ol> +<li></li> +</ol> +<p>The JIRA issue is <a href="https://issues.apache.org/jira/browse/MAHOUT-375";>here</a> +. </p> +<p><a name="RestrictedBoltzmannMachines-BoltzmannMachines"></a></p> +<h3 id="boltzmann-machines">Boltzmann Machines</h3> +<p>Boltzmann Machines are a type of stochastic neural networks that closely +resemble physical processes. They define a network of units with an overall +energy that is evolved over a period of time, until it reaches thermal +equilibrium. </p> +<p>However, the convergence speed of Boltzmann machines that have +unconstrained connectivity is low.</p> +<p><a name="RestrictedBoltzmannMachines-RestrictedBoltzmannMachines"></a></p> +<h3 id="restricted-boltzmann-machines">Restricted Boltzmann Machines</h3> +<p>Restricted Boltzmann Machines are a variant, that are 'restricted' in the +sense that connections between hidden units of a single layer are <em>not</em> +allowed. In addition, stacking multiple RBM's is also feasible, with the +activities of the hidden units forming the base for a higher-level RBM. The +combination of these two features renders RBM's highly usable for +parallelization. </p> +<p>In the Netflix Prize, RBM's offered distinctly orthogonal predictions to +SVD and k-NN approaches, and contributed immensely to the final solution.</p> +<p><a name="RestrictedBoltzmannMachines-RBM'sinApacheMahout"></a></p> +<h3 id="rbms-in-apache-mahout">RBM's in Apache Mahout</h3> +<p>An implementation of Restricted Boltzmann Machines is being developed for +Apache Mahout as a Google Summer of Code 2010 project. A recommender +interface will also be provided. The key aims of the implementation are: +1. Accurate - should replicate known results, including those of the Netflix +Prize +1. Fast - The implementation uses Map-Reduce, hence, it should be fast +1. Scale - Should scale to large datasets, with a design whose critical +parts don't need a dependency between the amount of memory on your cluster +systems and the size of your dataset</p> +<p>You can view the patch as it develops <a href="http://github.com/sisirkoppaka/mahout-rbm/compare/trunk...rbm";>here</a> +.</p> + </div> + </div> +</div> + <footer class="footer" align="center"> + <div class="container"> + <p> + Copyright © 2014 The Apache Software Foundation, Licensed under + the <a href="http://www.apache.org/licenses/LICENSE-2.0";>Apache License, Version 2.0</a>. + <br /> + Apache and the Apache feather logos are trademarks of The Apache Software Foundation. + </p> + </div> + </footer> + + <script src="/js/jquery-1.9.1.min.js"></script> + <script src="/js/bootstrap.min.js"></script> + <script> + (function() { + var cx = '012254517474945470291:vhsfv7eokdc'; + var gcse = document.createElement('script'); + gcse.type = 'text/javascript'; + gcse.async = true; + gcse.src = (document.location.protocol == 'https:' ? 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