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id="twenty-newsgroups-classification-example">Twenty Newsgroups Classification Example</h2> <p><a name="TwentyNewsgroups-Introduction"></a></p> @@ -312,35 +198,40 @@ the 20 newsgroups.</p> <li> <p>If running Hadoop in cluster mode, start the hadoop daemons by executing the following commands:</p> - <pre><code> $ cd $HADOOP_HOME/bin + <div class="highlighter-rouge"><pre class="highlight"><code> $ cd $HADOOP_HOME/bin $ ./start-all.sh </code></pre> + </div> <p>Otherwise:</p> - <pre><code> $ export MAHOUT_LOCAL=true + <div class="highlighter-rouge"><pre class="highlight"><code> $ export MAHOUT_LOCAL=true </code></pre> + </div> </li> <li> <p>In the trunk directory of Mahout, compile and install Mahout:</p> - <pre><code> $ cd $MAHOUT_HOME + <div class="highlighter-rouge"><pre class="highlight"><code> $ cd $MAHOUT_HOME $ mvn -DskipTests clean install </code></pre> + </div> </li> <li> <p>Run the <a href="https://github.com/apache/mahout/blob/master/examples/bin/classify-20newsgroups.sh";>20 newsgroups example script</a> by executing:</p> - <pre><code> $ ./examples/bin/classify-20newsgroups.sh + <div class="highlighter-rouge"><pre class="highlight"><code> $ ./examples/bin/classify-20newsgroups.sh </code></pre> + </div> </li> <li> <p>You will be prompted to select a classification method algorithm:</p> - <pre><code> 1. Complement Naive Bayes + <div class="highlighter-rouge"><pre class="highlight"><code> 1. Complement Naive Bayes 2. Naive Bayes 3. Stochastic Gradient Descent </code></pre> + </div> </li> </ol> @@ -358,7 +249,7 @@ the 20 newsgroups.</p> <p>Output should look something like:</p> -<pre><code>======================================================= +<div class="highlighter-rouge"><pre class="highlight"><code>======================================================= Confusion Matrix ------------------------------------------------------- a b c d e f g h i j k l m n o p q r s t <--Classified as @@ -390,6 +281,7 @@ Accuracy 90.8596% Reliability 86.3632% Reliability (standard deviation) 0.2131 </code></pre> +</div> <p><a name="TwentyNewsgroups-ComplementaryNaiveBayes"></a></p> <h2 id="end-to-end-commands-to-build-a-cbayes-model-for-20-newsgroups">End to end commands to build a CBayes model for 20 newsgroups</h2> @@ -401,14 +293,15 @@ Reliability (standard deviation) 0.2131 <li> <p>Create a working directory for the dataset and all input/output.</p> - <pre><code> $ export WORK_DIR=/tmp/mahout-work-${USER} + <div class="highlighter-rouge"><pre class="highlight"><code> $ export WORK_DIR=/tmp/mahout-work-${USER} $ mkdir -p ${WORK_DIR} </code></pre> + </div> </li> <li> <p>Download and extract the <em>20news-bydate.tar.gz</em> from the <a href="http://people.csail.mit.edu/jrennie/20Newsgroups/20news-bydate.tar.gz";>20newsgroups dataset</a> to the working directory.</p> - <pre><code> $ curl http://people.csail.mit.edu/jrennie/20Newsgroups/20news-bydate.tar.gz + <div class="highlighter-rouge"><pre class="highlight"><code> $ curl http://people.csail.mit.edu/jrennie/20Newsgroups/20news-bydate.tar.gz -o ${WORK_DIR}/20news-bydate.tar.gz $ mkdir -p ${WORK_DIR}/20news-bydate $ cd ${WORK_DIR}/20news-bydate && tar xzf ../20news-bydate.tar.gz && cd .. && cd .. @@ -417,42 +310,46 @@ Reliability (standard deviation) 0.2131 $ hadoop dfs -put ${WORK_DIR}/20news-all ${WORK_DIR}/20news-all </code></pre> + </div> </li> <li> <p>Convert the full 20 newsgroups dataset into a < Text, Text > SequenceFile.</p> - <pre><code> $ mahout seqdirectory + <div class="highlighter-rouge"><pre class="highlight"><code> $ mahout seqdirectory -i ${WORK_DIR}/20news-all -o ${WORK_DIR}/20news-seq -ow </code></pre> + </div> </li> <li> <p>Convert and preprocesses the dataset into a < Text, VectorWritable > SequenceFile containing term frequencies for each document.</p> - <pre><code> $ mahout seq2sparse + <div class="highlighter-rouge"><pre class="highlight"><code> $ mahout seq2sparse -i ${WORK_DIR}/20news-seq -o ${WORK_DIR}/20news-vectors -lnorm -nv -wt tfidf If we wanted to use different parsing methods or transformations on the term frequency vectors we could supply different options here e.g.: -ng 2 for bigrams or -n 2 for L2 length normalization. See the [Creating vectors from text](http://mahout.apache.org/users/basics/creating-vectors-from-text.html) page for a list of all seq2sparse options. </code></pre> + </div> </li> <li> <p>Split the preprocessed dataset into training and testing sets.</p> - <pre><code> $ mahout split + <div class="highlighter-rouge"><pre class="highlight"><code> $ mahout split -i ${WORK_DIR}/20news-vectors/tfidf-vectors --trainingOutput ${WORK_DIR}/20news-train-vectors --testOutput ${WORK_DIR}/20news-test-vectors --randomSelectionPct 40 --overwrite --sequenceFiles -xm sequential </code></pre> + </div> </li> <li> <p>Train the classifier.</p> - <pre><code> $ mahout trainnb + <div class="highlighter-rouge"><pre class="highlight"><code> $ mahout trainnb -i ${WORK_DIR}/20news-train-vectors -el -o ${WORK_DIR}/model @@ -460,11 +357,12 @@ Reliability (standard deviation) 0.2131 -ow -c </code></pre> + </div> </li> <li> <p>Test the classifier.</p> - <pre><code> $ mahout testnb + <div class="highlighter-rouge"><pre class="highlight"><code> $ mahout testnb -i ${WORK_DIR}/20news-test-vectors -m ${WORK_DIR}/model -l ${WORK_DIR}/labelindex @@ -472,37 +370,31 @@ Reliability (standard deviation) 0.2131 -o ${WORK_DIR}/20news-testing -c </code></pre> + </div> </li> </ol> </div> -</div> - - </div> -</div> - -<div id="footer"> - <div class="container"> - <p>© 2017 Apache Mahout - with help from <a href="http://jekyllbootstrap.com"; target="_blank" title="The Definitive Jekyll Blogging Framework">Jekyll Bootstrap</a> - and <a href="http://getbootstrap.com"; target="_blank">Bootstrap</a> - </p> </div> -</div> - - +</div> + <footer class="footer bg-light"> + <div class="container text-center small"> + Copyright © 2014-2017 The Apache Software Foundation, Licensed under the Apache License, Version 2.0. + </div> +</footer> + <script src="/assets/vendor/jquery/jquery-slim.min.js"></script> + <script src="/assets/vendor/popper/popper.min.js"></script> + <script src="/assets/vendor/bootstrap/js/bootstrap.min.js"></script> + <script src="/assets/header.js"></script> + <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS-MML_HTMLorMML"; type="text/javascript"></script> -<!-- Latest compiled and minified JavaScript, requires jQuery 1.x (2.x not supported in IE8) --> -<!-- Placed at the end of the document so the pages load faster --> -<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.10.2/jquery.min.js";></script> -<script src="/assets/themes/mahout3/js/bootstrap.min.js"></script> </body> -</html> +</html>
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id="wikipedia-xml-parser-and-naive-bayes-classifier-example">Wikipedia XML parser and Naive Bayes Classifier Example</h1> + <div class="row"> + + <div class="col-lg-8"> + <h1 id="wikipedia-xml-parser-and-naive-bayes-classifier-example">Wikipedia XML parser and Naive Bayes Classifier Example</h1> <h2 id="introduction">Introduction</h2> <p>Mahout has an <a href="https://github.com/apache/mahout/blob/master/examples/bin/classify-wikipedia.sh";>example script</a> [1] which will download a recent XML dump of the (entire if desired) <a href="http://dumps.wikimedia.org/enwiki/latest/";>English Wikipedia database</a>. After running the classification script, you can use the <a href="https://github.com/apache/mahout/blob/master/examples/bin/spark-document-classifier.mscala";>document classification script</a> from the Mahout <a href="http://mahout.apache.org/users/sparkbindings/play-with-shell.html";>spark-shell</a> to vectorize and classify text from outside of the training and testing corpus using a modle built on the Wikipedia dataset.</p> @@ -286,23 +172,24 @@ <h2 id="oververview">Oververview</h2> -<p>Tou run the example simply execute the <code>$MAHOUT_HOME/examples/bin/classify-wikipedia.sh</code> script.</p> +<p>Tou run the example simply execute the <code class="highlighter-rouge">$MAHOUT_HOME/examples/bin/classify-wikipedia.sh</code> script.</p> <p>By defult the script is set to run on a medium sized Wikipedia XML dump. To run on the full set (the entire english Wikipedia) you can change the download by commenting out line 78, and uncommenting line 80 of <a href="https://github.com/apache/mahout/blob/master/examples/bin/classify-wikipedia.sh";>classify-wikipedia.sh</a> [1]. However this is not recommended unless you have the resources to do so. <em>Be sure to clean your work directory when changing datasets- option (3).</em></p> -<p>The step by step process for Creating a Naive Bayes Classifier for the Wikipedia XML dump is very similar to that for <a href="http://mahout.apache.org/users/classification/twenty-newsgroups.html";>creating a 20 Newsgroups Classifier</a> [4]. The only difference being that instead of running <code>$mahout seqdirectory</code> on the unzipped 20 Newsgroups file, youâll run <code>$mahout seqwiki</code> on the unzipped Wikipedia xml dump.</p> +<p>The step by step process for Creating a Naive Bayes Classifier for the Wikipedia XML dump is very similar to that for <a href="http://mahout.apache.org/users/classification/twenty-newsgroups.html";>creating a 20 Newsgroups Classifier</a> [4]. The only difference being that instead of running <code class="highlighter-rouge">$mahout seqdirectory</code> on the unzipped 20 Newsgroups file, youâll run <code class="highlighter-rouge">$mahout seqwiki</code> on the unzipped Wikipedia xml dump.</p> -<pre><code>$ mahout seqwiki +<div class="highlighter-rouge"><pre class="highlight"><code>$ mahout seqwiki </code></pre> +</div> -<p>The above command launches <code>WikipediaToSequenceFile.java</code> which accepts a text file of categories [3] and starts an MR job to parse the each document in the XML file. This process will seek to extract documents with a wikipedia category tag which (exactly, if the <code>-exactMatchOnly</code> option is set) matches a line in the category file. If no match is found and the <code>-all</code> option is set, the document will be dumped into an âunknownâ category. The documents will then be written out as a <code><Text,Text></code> sequence file of the form (K:/category/document_title , V: document).</p> +<p>The above command launches <code class="highlighter-rouge">WikipediaToSequenceFile.java</code> which accepts a text file of categories [3] and starts an MR job to parse the each document in the XML file. This process will seek to extract documents with a wikipedia category tag which (exactly, if the <code class="highlighter-rouge">-exactMatchOnly</code> option is set) matches a line in the category file. If no match is found and the <code class="highlighter-rouge">-all</code> option is set, the document will be dumped into an âunknownâ category. The documents will then be written out as a <code class="highlighter-rouge"><Text,Text></code> sequence file of the form (K:/category/document_title , V: document).</p> <p>There are 3 different example category files available to in the /examples/src/test/resources directory: country.txt, country10.txt and country2.txt. You can edit these categories to extract a different corpus from the Wikipedia dataset.</p> -<p>The CLI options for <code>seqwiki</code> are as follows:</p> +<p>The CLI options for <code class="highlighter-rouge">seqwiki</code> are as follows:</p> -<pre><code>--input (-i) input pathname String +<div class="highlighter-rouge"><pre class="highlight"><code>--input (-i) input pathname String --output (-o) the output pathname String --categories (-c) the file containing the Wikipedia categories --exactMatchOnly (-e) if set, then the Wikipedia category must match @@ -310,8 +197,9 @@ directory: country.txt, country10.txt and country2.txt. You can edit these cat --all (-all) if set select all categories --removeLabels (-rl) if set, remove [[Category:labels]] from document text after extracting label. </code></pre> +</div> -<p>After <code>seqwiki</code>, the script runs <code>seq2sparse</code>, <code>split</code>, <code>trainnb</code> and <code>testnb</code> as in the <a href="http://mahout.apache.org/users/classification/twenty-newsgroups.html";>step by step 20newsgroups example</a>. When all of the jobs have finished, a confusion matrix will be displayed.</p> +<p>After <code class="highlighter-rouge">seqwiki</code>, the script runs <code class="highlighter-rouge">seq2sparse</code>, <code class="highlighter-rouge">split</code>, <code class="highlighter-rouge">trainnb</code> and <code class="highlighter-rouge">testnb</code> as in the <a href="http://mahout.apache.org/users/classification/twenty-newsgroups.html";>step by step 20newsgroups example</a>. When all of the jobs have finished, a confusion matrix will be displayed.</p> <p>#Resourcese</p> @@ -331,32 +219,25 @@ directory: country.txt, country10.txt and country2.txt. 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