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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="#">1. Getting started with scikit-multilearn</a></li>
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<div class="section" id="Getting-started-with-scikit-multilearn">
<h1>1. Getting started with scikit-multilearn<a class="headerlink" href="#Getting-started-with-scikit-multilearn" title="Permalink to this headline">¶</a></h1>
<p>Scikit-multilearn is a BSD-licensed library for multi-label
classification that is built on top of the well-known scikit-learn
ecosystem.</p>
<p>To install it just run the command:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ pip install scikit-multilearn
</pre></div>
</div>
<p>Scikit-multilearn works with Python 2 and 3 on Windows, Linux and OSX.
The module name is <code class="docutils literal notranslate"><span class="pre">skmultilearn</span></code>.</p>
<div class="nbinput nblast docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre>
<span></span>In [1]:
</pre></div>
</div>
<div class="input_area highlight-ipython2 notranslate"><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">skmultilearn.dataset</span> <span class="kn">import</span> <span class="n">load_dataset</span>
</pre></div>
</div>
</div>
<p>Let’s load up some data. In this tutorial we will be working with the
<code class="docutils literal notranslate"><span class="pre">emotions</span></code> data set introduced in <a class="reference external" href="http://cos">emotions</a>.</p>
<div class="nbinput docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre>
<span></span>In [2]:
</pre></div>
</div>
<div class="input_area highlight-ipython2 notranslate"><div class="highlight"><pre>
<span></span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">feature_names</span><span class="p">,</span> <span class="n">label_names</span> <span class="o">=</span> <span class="n">load_dataset</span><span class="p">(</span><span class="s1">'emotions'</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">'emotions'</span><span class="p">,</span> <span class="s1">'test'</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="nboutput nblast docutils container">
<div class="prompt empty docutils container">
</div>
<div class="output_area docutils container">
<div class="highlight"><pre>
emotions:train - exists, not redownloading
emotions:test - exists, not redownloading
</pre></div></div>
</div>
<p>The <code class="docutils literal notranslate"><span class="pre">feature_names</span></code> variable contains list of pairs (feature name,
type) that were provided in the original data set. In the case of
<code class="docutils literal notranslate"><span class="pre">emotions</span></code> data the authors write:</p>
<blockquote>
<div>The extracted features fall into two categories: rhythmic and
timbre.</div></blockquote>
<p>Let’s take a look at the first few features:</p>
<div class="nbinput docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre>
<span></span>In [3]:
</pre></div>
</div>
<div class="input_area highlight-ipython2 notranslate"><div class="highlight"><pre>
<span></span><span class="n">feature_names</span><span class="p">[:</span><span class="mi">10</span><span class="p">]</span>
</pre></div>
</div>
</div>
<div class="nboutput nblast docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre>
<span></span>Out[3]:
</pre></div>
</div>
<div class="output_area highlight-none notranslate"><div class="highlight"><pre>
<span></span>[(u'Mean_Acc1298_Mean_Mem40_Centroid', u'NUMERIC'),
(u'Mean_Acc1298_Mean_Mem40_Rolloff', u'NUMERIC'),
(u'Mean_Acc1298_Mean_Mem40_Flux', u'NUMERIC'),
(u'Mean_Acc1298_Mean_Mem40_MFCC_0', u'NUMERIC'),
(u'Mean_Acc1298_Mean_Mem40_MFCC_1', u'NUMERIC'),
(u'Mean_Acc1298_Mean_Mem40_MFCC_2', u'NUMERIC'),
(u'Mean_Acc1298_Mean_Mem40_MFCC_3', u'NUMERIC'),
(u'Mean_Acc1298_Mean_Mem40_MFCC_4', u'NUMERIC'),
(u'Mean_Acc1298_Mean_Mem40_MFCC_5', u'NUMERIC'),
(u'Mean_Acc1298_Mean_Mem40_MFCC_6', u'NUMERIC')]
</pre></div>
</div>
</div>
<p>The <code class="docutils literal notranslate"><span class="pre">label_names</span></code> variable contains list of pairs (label name, type)
of labels that were used to annotate the music. The paper states that:</p>
<blockquote>
<div>The Tellegen-Watson-Clark model was employed for labeling the data
with emotions. The sound clips were annotated by three male experts
of age 20, 25 and 30 from the School of Music Studies</div></blockquote>
<p>The labels counts in the training data are as follows:</p>
<table border="1" class="docutils">
<colgroup>
<col width="21%" />
<col width="47%" />
<col width="33%" />
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">Label</th>
<th class="head">Description</th>
<th class="head"># Examples</th>
</tr>
</thead>
<tbody valign="top">
<tr class="row-even"><td>L1</td>
<td>amazed-surprised</td>
<td>173</td>
</tr>
<tr class="row-odd"><td>L2</td>
<td>happy-pleased</td>
<td>166</td>
</tr>
<tr class="row-even"><td>L3</td>
<td>relaxing-calm</td>
<td>264</td>
</tr>
<tr class="row-odd"><td>L4</td>
<td>quiet-still</td>
<td>148</td>
</tr>
<tr class="row-even"><td>L5</td>
<td>sad-lonely</td>
<td>168</td>
</tr>
<tr class="row-odd"><td>L6</td>
<td>angry-fearful</td>
<td>189</td>
</tr>
</tbody>
</table>
<p>Let’s see the contents of <code class="docutils literal notranslate"><span class="pre">label_names</span></code>:</p>
<div class="nbinput docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre>
<span></span>In [4]:
</pre></div>
</div>
<div class="input_area highlight-ipython2 notranslate"><div class="highlight"><pre>
<span></span><span class="n">label_names</span>
</pre></div>
</div>
</div>
<div class="nboutput nblast docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre>
<span></span>Out[4]:
</pre></div>
</div>
<div class="output_area highlight-none notranslate"><div class="highlight"><pre>
<span></span>[(u'amazed-suprised', [u'0', u'1']),
(u'happy-pleased', [u'0', u'1']),
(u'relaxing-calm', [u'0', u'1']),
(u'quiet-still', [u'0', u'1']),
(u'sad-lonely', [u'0', u'1']),
(u'angry-aggresive', [u'0', u'1'])]
</pre></div>
</div>
</div>
<div class="nbinput nblast docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre>
<span></span>In [5]:
</pre></div>
</div>
<div class="input_area highlight-ipython2 notranslate"><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">skmultilearn.problem_transform</span> <span class="kn">import</span> <span class="n">BinaryRelevance</span>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">SVC</span>
</pre></div>
</div>
</div>
<div class="nbinput nblast docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre>
<span></span>In [6]:
</pre></div>
</div>
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<span></span><span class="n">clf</span> <span class="o">=</span> <span class="n">BinaryRelevance</span><span class="p">(</span>
<span class="n">classifier</span><span class="o">=</span><span class="n">SVC</span><span class="p">(),</span>
<span class="n">require_dense</span><span class="o">=</span><span class="p">[</span><span class="bp">False</span><span class="p">,</span> <span class="bp">True</span><span class="p">]</span>
<span class="p">)</span>
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<p>On a side note, Binary Relevance trains a classifier per each of the
labels, we can see that the classifier hasn’t been trained yet:</p>
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<span></span><span class="n">clf</span><span class="o">.</span><span class="n">classifiers</span>
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<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
<span class="ansi-red-fg">AttributeError</span> Traceback (most recent call last)
<span class="ansi-green-fg"><ipython-input-7-5aa82f5c3cc2></span> in <span class="ansi-cyan-fg"><module></span><span class="ansi-blue-fg">()</span>
<span class="ansi-green-fg">----> 1</span><span class="ansi-red-fg"> </span>clf<span class="ansi-blue-fg">.</span>classifiers
<span class="ansi-red-fg">AttributeError</span>: 'BinaryRelevance' object has no attribute 'classifiers'
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</div>
<p>Scikit-learn introduces a convention of how classifiers are organized.
The typical usage of classifier is:</p>
<ul class="simple">
<li>fit it to the data (trains the classifier and returns self)</li>
<li>predict results on new data (returns predicted results)</li>
</ul>
<p>Scikit-multilearn follows these conventions, let’s train a multi-label
classifier:</p>
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<span></span><span class="n">clf</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>
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<span></span>BinaryRelevance(classifier=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False),
require_dense=[False, True])
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<p>The base classifiers have been trained now:</p>
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<span></span><span class="n">clf</span><span class="o">.</span><span class="n">classifiers</span>
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<span></span>[SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False),
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False),
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False),
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False),
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False),
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)]
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<span></span><span class="n">prediction</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
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<span></span><span class="n">prediction</span>
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<span></span><202x6 sparse matrix of type '<type 'numpy.int64'>'
with 246 stored elements in Compressed Sparse Column format>
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<span></span><span class="c1">## Measure the quality</span>
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<span></span><span class="kn">import</span> <span class="nn">sklearn.metrics</span> <span class="kn">as</span> <span class="nn">metrics</span>
</pre></div>
</div>
</div>
<p>Scikit-learn provides a <a class="reference external" href="http://scikit-learn.org/stable/modules/classes.html#classification-metrics">set of
metrics</a>
useful for evaluating the quality of the model. They are most often used
by providing the true assignment matrix/array as the first argument, and
the prediction matrix/array as the second argument.</p>
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<span></span><span class="n">metrics</span><span class="o">.</span><span class="n">hamming_loss</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">prediction</span><span class="p">)</span>
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<span></span>0.26485148514851486
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<span></span><span class="n">metrics</span><span class="o">.</span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">prediction</span><span class="p">)</span>
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<span></span>0.14356435643564355
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</div>
</div>
</div>
</div>
</div>
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@ARTICLE{2017arXiv170201460S,
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journal = {ArXiv e-prints},
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