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<title>scikit-multilearn: Multi-Label Classification in Python — Multi-Label Classification for Python</title>
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<li><a class="reference internal" href="#">4. Using the MEKA wrapper</a><ul>
<li><a class="reference internal" href="#Setting-up-MEKA">4.1. Setting up MEKA</a></li>
<li><a class="reference internal" href="#Using-MEKA-via-scikit-multilearn">4.2. Using MEKA via scikit-multilearn</a></li>
<li><a class="reference internal" href="#Citing-meka">4.3. Citing meka</a></li>
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<div class="section" id="Using-the-MEKA-wrapper">
<h1>4. Using the MEKA wrapper<a class="headerlink" href="#Using-the-MEKA-wrapper" title="Permalink to this headline">¶</a></h1>
<p>The <a class="reference external" href="http://waikato.github.io/meka/">MEKA</a> project provides an open
source implementation of methods for multi-label learning and
evaluation. In multi-label classification, we want to predict multiple
output variables for each input instance.</p>
<p>MEKA is based on the <a class="reference external" href="http://www.cs.waikato.ac.nz/ml/weka/">WEKA</a>
Machine Learning Toolkit; it includes dozens of multi-label methods from
the scientific literature, as well as a wrapper to the related
<a class="reference external" href="http://mulan.sourceforge.net/">MULAN</a> framework.</p>
<p>An introduction to multi-label classification and MEKA is given in a
<a class="reference external" href="http://jmlr.org/papers/volume17/12-164/12-164.pdf">JMLR MLOSS-track
paper</a>. Note that
while MEKA is GPL-licensed, using this wrapper does not incur GPL
limitations on your code.</p>
<div class="section" id="Setting-up-MEKA">
<h2>4.1. Setting up MEKA<a class="headerlink" href="#Setting-up-MEKA" title="Permalink to this headline">¶</a></h2>
<p>In order to use the scikit-multilearn interface to MEKA you need to have
JAVA and MEKA installed. Paths to both are passed to the class’s
constructor. <strong>The current version supports meka 1.9.1+</strong></p>
<p>The currently officially supported MEKA version is
<a class="reference external" href="https://github.com/Waikato/meka/releases/tag/meka-1.9.2">1.9.2</a>.</p>
<p>You can download it using the :fun:<code class="docutils literal notranslate"><span class="pre">download_meka</span></code>, the function
returns path to MEKA classes.</p>
<div class="nbinput docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre>
<span></span>In [1]:
</pre></div>
</div>
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">skmultilearn.ext</span> <span class="k">import</span> <span class="n">download_meka</span>
<span class="n">meka_classpath</span> <span class="o">=</span> <span class="n">download_meka</span><span class="p">()</span>
<span class="n">meka_classpath</span>
</pre></div>
</div>
</div>
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MEKA 1.9.2 found, not downloading
</pre></div></div>
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<span></span>Out[1]:
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<span></span>'/home/niedakh/scikit_ml_learn_data/meka/meka-release-1.9.2/lib/'
</pre></div>
</div>
</div>
<p>If you want to use a different version, just pass the version number as
an argument to <code class="docutils literal notranslate"><span class="pre">download_meka</span></code>.</p>
<p>Note that you will need to have <code class="docutils literal notranslate"><span class="pre">liac-arff</span></code> installed if you want to
use the MEKA wrapper, you can get them using: <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">liac-arff</span></code>.</p>
<p>You will also need Java. You can pass a path to the Java binary in MEKA
wrapper constructor. In Python 2.7 the pip package <code class="docutils literal notranslate"><span class="pre">whichcraft</span></code> is
used to detect the location of java executables if no path is provided
to the constructor. You can install it via <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">whichcraft</span></code>.
In Python 3 <code class="docutils literal notranslate"><span class="pre">whichcraft</span></code> is not used, java path will be found using
the standard library.</p>
</div>
<div class="section" id="Using-MEKA-via-scikit-multilearn">
<h2>4.2. Using MEKA via scikit-multilearn<a class="headerlink" href="#Using-MEKA-via-scikit-multilearn" title="Permalink to this headline">¶</a></h2>
<p>Starting from scikit-multilearn <code class="docutils literal notranslate"><span class="pre">0.0.2</span></code> the meka wrapper is available
from <code class="docutils literal notranslate"><span class="pre">skmultilearn.ext</span></code> (ext as in external) and is a fully
scikit-compatible multi-label classifier.</p>
<p>To use the interface class start with importing skmultilearn’s module,
then create an object of the <code class="docutils literal notranslate"><span class="pre">Meka</span></code> class using the constructor and
perform the standard fit & predict scenario.</p>
<p>Let’s load up some data to see how it works.</p>
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<span></span><span class="kn">from</span> <span class="nn">skmultilearn.dataset</span> <span class="k">import</span> <span class="n">load_dataset</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">load_dataset</span><span class="p">(</span><span class="s1">'scene'</span><span class="p">,</span> <span class="s1">'train'</span><span class="p">)</span>
<span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">load_dataset</span><span class="p">(</span><span class="s1">'scene'</span><span class="p">,</span> <span class="s1">'test'</span><span class="p">)</span>
</pre></div>
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scene - exists, not redownloading
scene - exists, not redownloading
</pre></div></div>
</div>
<p>Now that we have a data set let’s classify it using MEKA and WEKA! If
you are new to the MEKA and WEKA stack you can find available
classifiers under the following links:</p>
<ul class="simple">
<li><a class="reference external" href="http://weka.sourceforge.net/doc.dev/weka/classifiers/Classifier.html">WEKA base classifier
list</a></li>
<li><a class="reference external" href="http://waikato.github.io/meka/methods/">MEKA multi-label classifier
list</a></li>
</ul>
<p>Let’s start by importing :class:<code class="docutils literal notranslate"><span class="pre">Meka</span></code> and constructing a MEKA wrapper
classifier:</p>
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<span></span><span class="kn">from</span> <span class="nn">skmultilearn.ext</span> <span class="k">import</span> <span class="n">Meka</span>
<span class="n">meka</span> <span class="o">=</span> <span class="n">Meka</span><span class="p">(</span>
<span class="n">meka_classifier</span> <span class="o">=</span> <span class="s2">"meka.classifiers.multilabel.BR"</span><span class="p">,</span> <span class="c1"># Binary Relevance</span>
<span class="n">weka_classifier</span> <span class="o">=</span> <span class="s2">"weka.classifiers.bayes.NaiveBayesMultinomial"</span><span class="p">,</span> <span class="c1"># with Naive Bayes single-label classifier</span>
<span class="n">meka_classpath</span> <span class="o">=</span> <span class="n">meka_classpath</span><span class="p">,</span> <span class="c1">#obtained via download_meka</span>
<span class="n">java_command</span> <span class="o">=</span> <span class="s1">'/usr/bin/java'</span> <span class="c1"># path to java executable</span>
<span class="p">)</span>
<span class="n">meka</span>
</pre></div>
</div>
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<span></span>Meka(java_command='/usr/bin/java',
meka_classifier='meka.classifiers.multilabel.BR',
meka_classpath='/home/niedakh/scikit_ml_learn_data/meka/meka-release-1.9.2/lib/',
weka_classifier='weka.classifiers.bayes.NaiveBayesMultinomial')
</pre></div>
</div>
</div>
<p>Where:</p>
<ul class="simple">
<li><code class="docutils literal notranslate"><span class="pre">meka_classifier</span></code> is the MEKA classifier class</li>
<li><code class="docutils literal notranslate"><span class="pre">weka_classifier</span></code> is the WEKA base classifier class if used</li>
<li><code class="docutils literal notranslate"><span class="pre">java_command</span></code> is the path to java</li>
<li><code class="docutils literal notranslate"><span class="pre">meka_classpath</span></code> is the path to where meka.jar and weka.jar are
located, usually they come together in meka releases, so this points
to the <code class="docutils literal notranslate"><span class="pre">lib</span></code> subfolder of the folder where
<code class="docutils literal notranslate"><span class="pre">meka-<version>-realease.zip</span></code> file was unzipped. If not provided
the path is taken from environmental variable: <code class="docutils literal notranslate"><span class="pre">MEKA_CLASSPATH</span></code></li>
</ul>
<p>Now let’s train and test the classifier - we’ll se what level of hamming
loss do we get?</p>
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<span></span>In [4]:
</pre></div>
</div>
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
<span></span><span class="n">X_train</span>
</pre></div>
</div>
</div>
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<span></span>Out[4]:
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</div>
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<span></span><1211x294 sparse matrix of type '<class 'numpy.float64'>'
with 351805 stored elements in LInked List format>
</pre></div>
</div>
</div>
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<span></span>In [5]:
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
<span></span><span class="n">meka</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">predictions</span> <span class="o">=</span> <span class="n">meka</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
</pre></div>
</div>
</div>
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<span></span>In [6]:
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</div>
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<span></span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">hamming_loss</span>
</pre></div>
</div>
</div>
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<span></span>In [7]:
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
<span></span><span class="n">hamming_loss</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predictions</span><span class="p">)</span>
</pre></div>
</div>
</div>
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<span></span>Out[7]:
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<span></span>0.14659977703455965
</pre></div>
</div>
</div>
</div>
<div class="section" id="Citing-meka">
<h2>4.3. Citing meka<a class="headerlink" href="#Citing-meka" title="Permalink to this headline">¶</a></h2>
<div class="highlight-latex notranslate"><div class="highlight"><pre><span></span>@article<span class="nb">{</span>MEKA,
author = <span class="nb">{</span>Read, Jesse and Reutemann, Peter and Pfahringer, Bernhard and Holmes, Geoff<span class="nb">}</span>,
title = <span class="nb">{{</span>MEKA<span class="nb">}</span>: A Multi-label/Multi-target Extension to <span class="nb">{</span>Weka<span class="nb">}}</span>,
journal = <span class="nb">{</span>Journal of Machine Learning Research<span class="nb">}</span>,
year = <span class="nb">{</span>2016<span class="nb">}</span>,
volume = <span class="nb">{</span>17<span class="nb">}</span>,
number = <span class="nb">{</span>21<span class="nb">}</span>,
pages = <span class="nb">{</span>1--5<span class="nb">}</span>,
url = <span class="nb">{</span>http://jmlr.org/papers/v17/12-164.html<span class="nb">}</span>,
<span class="nb">}</span>
@article<span class="nb">{</span>Hall:2009:WDM:1656274.1656278,
author = <span class="nb">{</span>Hall, Mark and Frank, Eibe and Holmes, Geoffrey and Pfahringer, Bernhard and Reutemann, Peter and Witten, Ian H.<span class="nb">}</span>,
title = <span class="nb">{</span>The WEKA Data Mining Software: An Update<span class="nb">}</span>,
journal = <span class="nb">{</span>SIGKDD Explor. Newsl.<span class="nb">}</span>,
issue<span class="nb">_</span>date = <span class="nb">{</span>June 2009<span class="nb">}</span>,
volume = <span class="nb">{</span>11<span class="nb">}</span>,
number = <span class="nb">{</span>1<span class="nb">}</span>,
month = nov,
year = <span class="nb">{</span>2009<span class="nb">}</span>,
issn = <span class="nb">{</span>1931-0145<span class="nb">}</span>,
pages = <span class="nb">{</span>10--18<span class="nb">}</span>,
numpages = <span class="nb">{</span>9<span class="nb">}</span>,
url = <span class="nb">{</span>http://doi.acm.org/10.1145/1656274.1656278<span class="nb">}</span>,
doi = <span class="nb">{</span>10.1145/1656274.1656278<span class="nb">}</span>,
acmid = <span class="nb">{</span>1656278<span class="nb">}</span>,
publisher = <span class="nb">{</span>ACM<span class="nb">}</span>,
address = <span class="nb">{</span>New York, NY, USA<span class="nb">}</span>,
<span class="nb">}</span>
</pre></div>
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