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Subject svn commit: r944380 [11/24] - in /websites/staging/mahout/trunk/content: ./ developers/ general/ users/basics/ users/classification/ users/clustering/ users/dim-reduction/ users/mapreduce/ users/mapreduce/classification/ users/mapreduce/clustering/ use...
Date Thu, 19 Mar 2015 21:21:47 GMT
Added: websites/staging/mahout/trunk/content/users/mapreduce/classification/neural-network.html
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--- websites/staging/mahout/trunk/content/users/mapreduce/classification/neural-network.html (added)
+++ websites/staging/mahout/trunk/content/users/mapreduce/classification/neural-network.html Thu Mar 19 21:21:45 2015
@@ -0,0 +1,288 @@
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+<h1 id="neural-networks">Neural Networks</h1>
+<p>Neural Networks are a means for classifying multi dimensional objects. We
+concentrate on implementing back propagation networks with one hidden layer
+as these networks have been covered by the <a href="http://www.cs.stanford.edu/people/ang/papers/nips06-mapreducemulticore.pdf">2006 NIPS map reduce paper</a>
+. Those networks are capable of learning not only linear separating hyper
+planes but arbitrary decision boundaries.</p>
+<p><a name="NeuralNetwork-Strategyforparallelbackpropagationnetwork"></a></p>
+<h2 id="strategy-for-parallel-backpropagation-network">Strategy for parallel backpropagation network</h2>
+<p><a name="NeuralNetwork-Designofimplementation"></a></p>
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Added: websites/staging/mahout/trunk/content/users/mapreduce/classification/partial-implementation.html
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--- websites/staging/mahout/trunk/content/users/mapreduce/classification/partial-implementation.html (added)
+++ websites/staging/mahout/trunk/content/users/mapreduce/classification/partial-implementation.html Thu Mar 19 21:21:45 2015
@@ -0,0 +1,411 @@
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+      <li><a 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   &lt;--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>     
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+++ websites/staging/mahout/trunk/content/users/mapreduce/classification/random-forests.html Thu Mar 19 21:21:45 2015
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+    <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>
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