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svn commit: r944380 [9/24] - in /websites/staging/mahout/trunk/content: ./
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users/dim-reduction/ users/mapreduce/ users/mapreduce/classification/
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+ <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>
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+ <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>
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Added: websites/staging/mahout/trunk/content/users/mapreduce/classification/classifyingyourdata.html
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--- websites/staging/mahout/trunk/content/users/mapreduce/classification/classifyingyourdata.html (added)
+++ websites/staging/mahout/trunk/content/users/mapreduce/classification/classifyingyourdata.html Thu Mar 19 21:21:45 2015
@@ -0,0 +1,293 @@
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+<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>
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+<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
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