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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="breiman-example">Breiman Example</h1> +<h4 id="introduction">Introduction</h4> +<p>This page describes how to run the Breiman example, which implements the test procedure described in <a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.23.3999&rep=rep1&type=pdf";>Leo Breiman's paper</a>. The basic algorithm is as follows :</p> +<ul> +<li>repeat <em>I</em> iterations</li> +<li>in each iteration do</li> +<li>keep 10% of the dataset apart as a testing set </li> +<li>build two forests using the training set, one with <em>m = int(log2(M) + 1)</em> (called Random-Input) and one with <em>m = 1</em> (called Single-Input)</li> +<li>choose the forest that gave the lowest oob error estimation to compute +the test set error</li> +<li>compute the test set error using the Single Input Forest (test error), +this demonstrates that even with <em>m = 1</em>, Decision Forests give comparable +results to greater values of <em>m</em></li> +<li>compute the mean testset error using every tree of the chosen forest +(tree error). This should indicate how well a single Decision Tree performs</li> +<li>compute the mean test error for all iterations</li> +<li>compute the mean tree error for all iterations</li> +</ul> +<h4 id="running-the-example">Running the Example</h4> +<p>The current implementation is compatible with the <a href="http://archive.ics.uci.edu/ml/";>UCI repository</a> file format. We'll show how to run this example on two datasets:</p> +<p>First, we deal with <a href="http://archive.ics.uci.edu/ml/datasets/Glass+Identification";>Glass Identification</a>: download the <a href="http://archive.ics.uci.edu/ml/machine-learning-databases/glass/glass.data";>dataset</a> file called <strong>glass.data</strong> and store it onto your local machine. Next, we must generate the descriptor file <strong>glass.info</strong> for this dataset with the following command:</p> +<div class="codehilite"><pre><span class="n">bin</span><span class="o">/</span><span class="n">mahout</span> <span class="n">org</span><span class="p">.</span><span class="n">apache</span><span class="p">.</span><span class="n">mahout</span><span class="p">.</span><span class="n">classifier</span><span class="p">.</span><span class="n">df</span><span class="p">.</span><span class="n">tools</span><span class="p">.</span><span class="n">Describe</span> <span class="o">-</span><span class="n">p</span> <span class="o">/</span><span class="n">path</span><span class="o">/</span><span class="n">to</span><span class="o">/</span><span class="n">glass</span><span class="p">.</span><span class="n">data</span> <span class="o">-</span><span class="n">f</span> <span class="o">/</span><span class="n">path</span><span class="o">/</span><span class="n">to</span><span class="o">/</span><span class="n">glass</span><span class="p">.</span><span class="n">info</span> <span class="o">-</span><span class= "n">d</span> <span class="n">I</span> 9 <span class="n">N</span> <span class="n">L</span> +</pre></div> + + +<p>Substitute <em>/path/to/</em> with the folder where you downloaded the dataset, the argument "I 9 N L" indicates the nature of the variables. Here it means 1 +ignored (I) attribute, followed by 9 numerical(N) attributes, followed by +the label (L).</p> +<p>Finally, we build and evaluate our random forest classifier as follows:</p> +<div class="codehilite"><pre><span class="n">bin</span><span class="o">/</span><span class="n">mahout</span> <span class="n">org</span><span class="p">.</span><span class="n">apache</span><span class="p">.</span><span class="n">mahout</span><span class="p">.</span><span class="n">classifier</span><span class="p">.</span><span class="n">df</span><span class="p">.</span><span class="n">BreimanExample</span> <span class="o">-</span><span class="n">d</span> <span class="o">/</span><span class="n">path</span><span class="o">/</span><span class="n">to</span><span class="o">/</span><span class="n">glass</span><span class="p">.</span><span class="n">data</span> <span class="o">-</span><span class="n">ds</span> <span class="o">/</span><span class="n">path</span><span class="o">/</span><span class="n">to</span><span class="o">/</span><span class="n">glass</span><span class="p">.</span><span class="n">info</span> <span class="o">-</span><span class="nb">i</span> 10 <span class="o">-</span><spa n class="n">t</span> 100 +</pre></div> + + +<p>which builds 100 trees (-t argument) and repeats the test 10 iterations (-i +argument) </p> +<p>The example outputs the following results:</p> +<ul> +<li>Selection error: mean test error for the selected forest on all iterations</li> +<li>Single Input error: mean test error for the single input forest on all +iterations</li> +<li>One Tree error: mean single tree error on all iterations</li> +<li>Mean Random Input Time: mean build time for random input forests on all +iterations</li> +<li>Mean Single Input Time: mean build time for single input forests on all +iterations</li> +</ul> +<p>We can repeat this for a <a href="http://archive.ics.uci.edu/ml/datasets/Connectionist+Bench+%28Sonar,+Mines+vs.+Rocks%29";>Sonar</a> usecase: download the <a href="http://archive.ics.uci.edu/ml/machine-learning-databases/undocumented/connectionist-bench/sonar/sonar.all-data";>dataset</a> file called <strong>sonar.all-data</strong> and store it onto your local machine. Generate the descriptor file <strong>sonar.info</strong> for this dataset with the following command:</p> +<div class="codehilite"><pre><span class="n">bin</span><span class="o">/</span><span class="n">mahout</span> <span class="n">org</span><span class="p">.</span><span class="n">apache</span><span class="p">.</span><span class="n">mahout</span><span class="p">.</span><span class="n">classifier</span><span class="p">.</span><span class="n">df</span><span class="p">.</span><span class="n">tools</span><span class="p">.</span><span class="n">Describe</span> <span class="o">-</span><span class="n">p</span> <span class="o">/</span><span class="n">path</span><span class="o">/</span><span class="n">to</span><span class="o">/</span><span class="n">sonar</span><span class="p">.</span><span class="n">all</span><span class="o">-</span><span class="n">data</span> <span class="o">-</span><span class="n">f</span> <span class="o">/</span><span class="n">path</span><span class="o">/</span><span class="n">to</span><span class="o">/</span><span class="n">sonar</span><span class="p">.</span><span class="n ">info</span> <span class="o">-</span><span class="n">d</span> 60 <span class="n">N</span> <span class="n">L</span> +</pre></div> + + +<p>The argument "60 N L" means 60 numerical(N) attributes, followed by the label (L). Analogous to the previous case, we run the evaluation as follows:</p> +<div class="codehilite"><pre><span class="n">bin</span><span class="o">/</span><span class="n">mahout</span> <span class="n">org</span><span class="p">.</span><span class="n">apache</span><span class="p">.</span><span class="n">mahout</span><span class="p">.</span><span class="n">classifier</span><span class="p">.</span><span class="n">df</span><span class="p">.</span><span class="n">BreimanExample</span> <span class="o">-</span><span class="n">d</span> <span class="o">/</span><span class="n">path</span><span class="o">/</span><span class="n">to</span><span class="o">/</span><span class="n">sonar</span><span class="p">.</span><span class="n">all</span><span class="o">-</span><span class="n">data</span> <span class="o">-</span><span class="n">ds</span> <span class="o">/</span><span class="n">path</span><span class="o">/</span><span class="n">to</span><span class="o">/</span><span class="n">sonar</span><span class="p">.</span><span class="n">info</span> <span class="o">-</span><span c lass="nb">i</span> 10 <span class="o">-</span><span class="n">t</span> 100 +</pre></div> + </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"> + <p><a name="ClassDiscovery-ClassDiscovery"></a></p> +<h1 id="class-discovery">Class Discovery</h1> +<p>See http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-417.pdf</p> +<p>CDGA uses a Genetic Algorithm to discover a classification rule for a given +dataset. +A dataset can be seen as a table:</p> +<table> +<tr><th> </th><th>attribute 1</th><th>attribute 2</th><th>...</th><th>attribute N</th></tr> +<tr><td>row 1</td><td>value1</td><td>value2</td><td>...</td><td>valueN</td></tr> +<tr><td>row 2</td><td>value1</td><td>value2</td><td>...</td><td>valueN</td></tr> +<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr> +<tr><td>row M</td><td>value1</td><td>value2</td><td>...</td><td>valueN</td></tr> +</table> + +<p>An attribute can be numerical, for example a "temperature" attribute, or +categorical, for example a "color" attribute. For classification purposes, +one of the categorical attributes is designated as a <em>label</em>, which means +that its value defines the <em>class</em> of the rows. +A classification rule can be represented as follows: +<table> +<tr><th> </th><th>attribute 1</th><th>attribute 2</th><th>...</th><th>attribute N</th></tr> +<tr><td>weight</td><td>w1</td><td>w2</td><td>...</td><td>wN</td></tr> +<tr><td>operator</td><td>op1</td><td>op2</td><td>...</td><td>opN</td></tr> +<tr><td>value</td><td>value1</td><td>value2</td><td>...</td><td>valueN</td></tr> +</table></p> +<p>For a given <em>target</em> class and a weight <em>threshold</em>, the classification +rule can be read :</p> +<div class="codehilite"><pre><span class="k">for</span> <span class="n">each</span> <span class="n">row</span> <span class="n">of</span> <span class="n">the</span> <span class="n">dataset</span> + <span class="k">if</span> <span class="p">(</span><span class="n">rule</span><span class="p">.</span><span class="n">w1</span> <span class="o"><</span> <span class="n">threshold</span> <span class="o">||</span> <span class="p">(</span><span class="n">rule</span><span class="p">.</span><span class="n">w1</span> <span class="o">></span><span class="p">=</span> <span class="n">threshold</span> <span class="o">&&</span> <span class="n">row</span><span class="p">.</span><span class="n">value1</span> <span class="n">rule</span><span class="p">.</span><span class="n">op1</span> +</pre></div> + + +<p>rule.value1)) && + (rule.w2 < threshold || (rule.w2 >= threshold && row.value2 rule.op2 +rule.value2)) && + ... + (rule.wN < threshold || (rule.wN >= threshold && row.valueN rule.opN +rule.valueN)) then + row is part of the target class</p> +<p><em>Important:</em> The label attribute is not evaluated by the rule.</p> +<p>The threshold parameter allows some conditions of the rule to be skipped if +their weight is too small. The operators available depend on the attribute +types: +<em> for a numerical attributes, the available operators are '<' and '>=' +</em> for categorical attributes, the available operators are '!=' and '=='</p> +<p>The "threshold" and "target" are user defined parameters, and because the +label is always a categorical attribute, the target is the (zero based) +index of the class label value in all the possible values of the label. For +example, if the label attribute can have the following values (blue, brown, +green), then a target of 1 means the "blue" class.</p> +<p>For example, we have the following dataset (the label attribute is "Eyes +Color"): +<table> +<tr><th> </th><th>Age</th><th>Eyes Color</th><th>Hair Color</th></tr> +<tr><td>row 1</td><td>16</td><td>brown</td><td>dark</td></tr> +<tr><td>row 2</td><td>25</td><td>green</td><td>light</td></tr> +<tr><td>row 3</td><td>12</td><td>blue</td><td>light</td></tr> +and a classification rule: +<tr><td>weight</td><td>0</td><td>1</td></tr> +<tr><td>operator</td><td><</td><td>!=</td></tr> +<tr><td>value</td><td>20</td><td>light</td></tr> +and the following parameters: threshold = 1 and target = 0 (brown). +</table></p> +<p>This rule can be read as follows:</p> +<div class="codehilite"><pre><span class="k">for</span> <span class="n">each</span> <span class="n">row</span> <span class="n">of</span> <span class="n">the</span> <span class="n">dataset</span> + <span class="k">if</span> <span class="p">(</span>0 <span class="o"><</span> 1 <span class="o">||</span> <span class="p">(</span>0 <span class="o">></span><span class="p">=</span> 1 <span class="o">&&</span> <span class="n">row</span><span class="p">.</span><span class="n">value1</span> <span class="o"><</span> 20<span class="p">))</span> <span class="o">&&</span> + <span class="p">(</span>1 <span class="o"><</span> 1 <span class="o">||</span> <span class="p">(</span>1 <span class="o">></span><span class="p">=</span> 1 <span class="o">&&</span> <span class="n">row</span><span class="p">.</span><span class="n">value2</span> !<span class="p">=</span> <span class="n">light</span><span class="p">))</span> <span class="n">then</span> + <span class="n">row</span> <span class="n">is</span> <span class="n">part</span> <span class="n">of</span> <span class="n">the</span> "<span class="n">brown</span> <span class="n">Eye</span> <span class="n">Color</span>" <span class="n">class</span> +</pre></div> + + +<p>Please note how the rule skipped the label attribute (Eye Color), and how +the first condition is ignored because its weight is < threshold.</p> +<p><a name="ClassDiscovery-Runningtheexample:"></a></p> +<h1 id="running-the-example">Running the example:</h1> +<p>NOTE: Substitute in the appropriate version for the Mahout JOB jar</p> +<ol> +<li>cd <MAHOUT_HOME>/examples</li> +<li>ant job</li> +<li>{code}<HADOOP_HOME>/bin/hadoop dfs -put +<MAHOUT_HOME>/examples/src/test/resources/wdbc wdbc{code}</li> +<li>{code}<HADOOP_HOME>/bin/hadoop dfs -put +<MAHOUT_HOME>/examples/src/test/resources/wdbc.infos wdbc.infos{code}</li> +<li> +<p>{code}<HADOOP_HOME>/bin/hadoop jar +<MAHOUT_HOME>/examples/build/apache-mahout-examples-0.1-dev.job +org.apache.mahout.ga.watchmaker.cd.CDGA +<MAHOUT_HOME>/examples/src/test/resources/wdbc 1 0.9 1 0.033 0.1 0 100 10</p> +<p>CDGA needs 9 parameters: +<em> param 1 : path of the directory that contains the dataset and its infos +file +</em> param 2 : target class +<em> param 3 : threshold +</em> param 4 : number of crossover points for the multi-point crossover +<em> param 5 : mutation rate +</em> param 6 : mutation range +<em> param 7 : mutation precision +</em> param 8 : population size +* param 9 : number of generations before the program stops</p> +<p>For more information about 4th parameter, please see [Multi-point Crossover|http://www.geatbx.com/docu/algindex-03.html#P616_36571] +. +For a detailed explanation about the 5th, 6th and 7th parameters, please +see [Real Valued Mutation|http://www.geatbx.com/docu/algindex-04.html#P659_42386] +.</p> +<p><em>TODO</em>: Fill in where to find the output and what it means.</p> +<p>h1. The info file: +To run properly, CDGA needs some informations about the dataset. Each +dataset should be accompanied by an .infos file that contains the needed +informations. for each attribute a corresponding line in the info file +describes it, it can be one of the following: +<em> IGNORED + if the attribute is ignored +</em> LABEL, val1, val2,... + if the attribute is the label (class), and its possible values +<em> CATEGORICAL, val1, val2,... + if the attribute is categorial (nominal), and its possible values +</em> NUMERICAL, min, max + if the attribute is numerical, and its min and max values</p> +<p>This file can be generated automaticaly using a special tool available with +CDGA.</p> +</li> +<li> +<p>the tool searches for an existing infos file (<em>must be filled by the +user</em>), in the same directory of the dataset with the same name and with +the ".infos" extension, that contain the type of the attributes: + <strong> 'N' numerical attribute + </strong> 'C' categorical attribute + <strong> 'L' label (this also a categorical attribute) + </strong> 'I' to ignore the attribute + each attribute is in a separate </p> +</li> +<li>A Hadoop job is used to parse the dataset and collect the informations. +This means that <em>the dataset can be distributed over HDFS</em>.</li> +<li>the results are written back in the same .info file, with the correct +format needed by CDGA.</li> +</ol> + </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"> + <h1 id="classifying-data-from-the-command-line">Classifying data from the command line</h1> +<p>After you've done the <a href="../basics/quickstart.html">Quickstart</a> and are familiar with the basics of Mahout, it is time to build a +classifier from your own data. The following pieces <em>may</em> be useful for in getting started:</p> +<p><a name="ClassifyingYourData-Input"></a></p> +<h1 id="input">Input</h1> +<p>For starters, you will need your data in an appropriate Vector format: See <a href="../basics/creating-vectors.html">Creating Vectors</a> as well as <a href="../basics/creating-vectors-from-text.html">Creating Vectors from Text</a>.</p> +<p><a name="ClassifyingYourData-RunningtheProcess"></a></p> +<h1 id="running-the-process">Running the Process</h1> +<ul> +<li>Logistic regression <a href="logistic-regression.html">background</a></li> +<li><a href="naivebayes.html">Naive Bayes background</a> and <a href="bayesian-commandline.html">commandline</a> options.</li> +<li><a href="complementary-naive-bayes.html">Complementary naive bayes background</a>, <a href="https://issues.apache.org/jira/browse/mahout-60.html";>design</a>, and <a href="c-bayes-commandline.html">c-bayes-commandline</a></li> +<li><a href="https://cwiki.apache.org/confluence/display/MAHOUT/Random+Forests";>Random Forests Classification</a> comes with a <a href="breiman-example.html">Breiman example</a>. There is some really great documentation +over at <a href="http://www.markhneedham.com/blog/2012/10/27/kaggle-digit-recognizer-mahout-random-forest-attempt/";>Mark Needham's blog</a>. Also checkout the description on <a href="http://shawnwan.wordpress.com/2012/06/01/mahout-0-7-random-forest-examples/";>Xiaomeng Shawn Wan +s</a> blog.</li> +</ul> + </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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