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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="#">3. Dataset handling</a><ul>
<li><a class="reference internal" href="#scikit-multilearn-format">3.1. scikit-multilearn format</a></li>
</ul>
</li>
<li><a class="reference internal" href="#scikit-multilearn-repository">4. scikit-multilearn repository</a><ul>
<li><a class="reference internal" href="#ARFF-files">4.1. ARFF files</a></li>
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<div class="section" id="Dataset-handling">
<h1>3. Dataset handling<a class="headerlink" href="#Dataset-handling" title="Permalink to this headline">¶</a></h1>
<p>Scikit-multilearn provides methods to load, save and manipulate
multi-label data sets in two formats:</p>
<ul class="simple">
<li>a scikit-multilearn pickle of data set in scipy sparse format</li>
<li>the traditional ARFF file format</li>
</ul>
<p>The functionality is provided in the :mod:<code class="docutils literal notranslate"><span class="pre">skmultilearn.dataset</span></code>
module.</p>
<p>Scikit-multilearn also provides a repository of most popular benchmark
data sets in the scipy sparse format and convienience functions to
access them.</p>
<div class="section" id="scikit-multilearn-format">
<h2>3.1. scikit-multilearn format<a class="headerlink" href="#scikit-multilearn-format" title="Permalink to this headline">¶</a></h2>
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<span></span>In [1]:
</pre></div>
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<span></span><span class="kn">from</span> <span class="nn">skmultilearn.dataset</span> <span class="kn">import</span> <span class="n">load_dataset_dump</span><span class="p">,</span> <span class="n">save_dataset_dump</span>
</pre></div>
</div>
</div>
<p>Loading scikit-multilearn data format is easier as it stores more
information than the ARFF file, all you need to do is specify the path
to the data set file.</p>
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<span></span><span class="n">X</span><span class="p">,</span> <span class="n">y</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_dump</span><span class="p">(</span><span class="s1">'_static/example.pkl.bz2'</span><span class="p">)</span>
</pre></div>
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<span></span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">feature_names</span><span class="p">[:</span><span class="mi">3</span><span class="p">],</span> <span class="n">label_names</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
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<span></span>(<65x19 sparse matrix of type '<class 'numpy.float64'>'
with 491 stored elements in LInked List format>,
<65x7 sparse matrix of type '<class 'numpy.int64'>'
with 217 stored elements in LInked List format>,
[('landmass', ['1', '2', '3', '4', '5', '6']),
('zone', ['1', '2', '3', '4']),
('area', 'NUMERIC')],
[('red', ['0', '1']), ('green', ['0', '1']), ('blue', ['0', '1'])])
</pre></div>
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<span></span><span class="n">save_dataset_dump</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">10</span><span class="p">,:</span><span class="mi">4</span><span class="p">],</span> <span class="n">y</span><span class="p">[:</span><span class="mi">10</span><span class="p">,</span> <span class="p">:</span><span class="mi">3</span><span class="p">],</span> <span class="n">feature_names</span><span class="p">[:</span><span class="mi">4</span><span class="p">],</span> <span class="n">label_names</span><span class="p">[:</span><span class="mi">3</span><span class="p">],</span> <span class="n">filename</span><span class="o">=</span><span class="bp">None</span><span class="p">)</span>
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<span></span>{'X': <10x4 sparse matrix of type '<class 'numpy.float64'>'
with 27 stored elements in LInked List format>,
'y': <10x3 sparse matrix of type '<class 'numpy.int64'>'
with 16 stored elements in LInked List format>,
'features': [('landmass', ['1', '2', '3', '4', '5', '6']),
('zone', ['1', '2', '3', '4']),
('area', 'NUMERIC'),
('population', 'NUMERIC')],
'labels': [('red', ['0', '1']), ('green', ['0', '1']), ('blue', ['0', '1'])]}
</pre></div>
</div>
</div>
<p>If the <code class="docutils literal notranslate"><span class="pre">filename</span></code> argument is not <code class="docutils literal notranslate"><span class="pre">None</span></code> this dictionary is saved as
a bzip2 compressed pickle and the function does not return anything.</p>
</div>
</div>
<div class="section" id="scikit-multilearn-repository">
<h1>4. scikit-multilearn repository<a class="headerlink" href="#scikit-multilearn-repository" title="Permalink to this headline">¶</a></h1>
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<span></span><span class="kn">from</span> <span class="nn">skmultilearn.dataset</span> <span class="kn">import</span> <span class="n">available_data_sets</span>
</pre></div>
</div>
</div>
<p>The following benchmark data sets, originally provided in the <a class="reference external" href="http://mulan.sourceforge.net/datasets-mlc.html">MULAN
data repository</a> are
provided in <code class="docutils literal notranslate"><span class="pre">train</span></code>, <code class="docutils literal notranslate"><span class="pre">test</span></code>, and <code class="docutils literal notranslate"><span class="pre">undivided</span></code> variants. The
undivided variant contains the complete data set, before the train/test
split.</p>
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<span></span><span class="nb">set</span><span class="p">([</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">available_data_sets</span><span class="p">()</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
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<span></span>{'Corel5k',
'bibtex',
'birds',
'delicious',
'emotions',
'enron',
'genbase',
'mediamill',
'medical',
'rcv1subset1',
'rcv1subset2',
'rcv1subset3',
'rcv1subset4',
'rcv1subset5',
'scene',
'tmc2007_500',
'yeast'}
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</div>
</div>
<p>Variants:</p>
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<span></span><span class="nb">set</span><span class="p">([</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">available_data_sets</span><span class="p">()</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
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</div>
</div>
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<span></span>{'test', 'train', 'undivided'}
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</div>
</div>
<p>Scikit-multilearn can automatically download the data sets for you,
similar to <a class="reference external" href="http://scikit-learn.org/stable/datasets/index.html">scikit-learn’s data set
API</a>.</p>
<p>The data is stored by default in the subfolder <code class="docutils literal notranslate"><span class="pre">scikit_ml_learn_data</span></code>
of your <code class="docutils literal notranslate"><span class="pre">SCIKIT_ML_LEARN_DATA</span></code> environment variable. If the variable
is not set, the data is stored in <code class="docutils literal notranslate"><span class="pre">~/scikit_ml_learn_data</span></code>.</p>
<p>To download a data set use the :meth:<code class="docutils literal notranslate"><span class="pre">load_dataset</span></code> function.</p>
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<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>
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<span></span><span class="n">X</span><span class="p">,</span> <span class="n">y</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">'scene'</span><span class="p">,</span> <span class="s1">'train'</span><span class="p">)</span>
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scene - exists, not redownloading
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<span></span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">feature_names</span><span class="p">[:</span><span class="mi">3</span><span class="p">],</span> <span class="n">label_names</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
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<span></span>(<1211x294 sparse matrix of type '<class 'numpy.float64'>'
with 351805 stored elements in LInked List format>,
<1211x6 sparse matrix of type '<class 'numpy.int64'>'
with 1286 stored elements in LInked List format>,
[('Att1', 'NUMERIC'), ('Att2', 'NUMERIC'), ('Att3', 'NUMERIC')],
[('Beach', ['0', '1']), ('Sunset', ['0', '1']), ('FallFoliage', ['0', '1'])])
</pre></div>
</div>
</div>
<div class="section" id="ARFF-files">
<h2>4.1. ARFF files<a class="headerlink" href="#ARFF-files" title="Permalink to this headline">¶</a></h2>
<p>The most common way for storing multi-label data is the <a class="reference external" href="https://www.cs.waikato.ac.nz/ml/weka/arff.html">ARFF file
format</a> created by
the <a class="reference external" href="https://www.cs.waikato.ac.nz/ml/weka/">WEKA</a> library. You can
find many benchmark data sets in ARFF format on the <a class="reference external" href="http://mulan.sourceforge.net/datasets-mlc.html">MULAN data
repository</a>.</p>
<p>Loading both dense and sparse ARFF files is simple in scikit-multilearn,
just use :func:<code class="docutils literal notranslate"><span class="pre">load_from_arff</span></code>, like this:</p>
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<span></span><span class="kn">from</span> <span class="nn">skmultilearn.dataset</span> <span class="kn">import</span> <span class="n">load_from_arff</span>
</pre></div>
</div>
</div>
<p>Loading multi-label ARFF files requires additional information as the
number or placement of labels, is not indicated in the format directly.</p>
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<span></span><span class="n">path_to_arff_file</span> <span class="o">=</span> <span class="s1">'_static/example.arff'</span>
<span class="n">label_count</span> <span class="o">=</span> <span class="mi">7</span>
</pre></div>
</div>
</div>
<p>Different software expects labels in different parts of the ARFF file:</p>
<ul class="simple">
<li>MEKA expects labels to appear at the beginning of the file</li>
<li>MULAN expects labels to appear at the end of the file</li>
</ul>
<p>As the <code class="docutils literal notranslate"><span class="pre">example.arff</span></code> comes from MULAN, we set the label location to
<code class="docutils literal notranslate"><span class="pre">end</span></code>.</p>
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<span></span><span class="n">label_location</span><span class="o">=</span><span class="s2">"end"</span>
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</div>
</div>
<p>There are two ways to save ARFF data: - dense, where the file contains a
complete dump of the data set row by row, including places where the
value is 0 - sparse, as a dictionary of keys, where for each row the
non-zero elements are listed with their index</p>
<p>The example file is not sparse, that’s why we set the <code class="docutils literal notranslate"><span class="pre">load_sparse</span></code>
argument to <code class="docutils literal notranslate"><span class="pre">False</span></code></p>
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<span></span><span class="n">arff_file_is_sparse</span> <span class="o">=</span> <span class="bp">False</span>
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<span></span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">load_from_arff</span><span class="p">(</span>
<span class="n">path_to_arff_file</span><span class="p">,</span>
<span class="n">label_count</span><span class="o">=</span><span class="n">label_count</span><span class="p">,</span>
<span class="n">label_location</span><span class="o">=</span><span class="n">label_location</span><span class="p">,</span>
<span class="n">load_sparse</span><span class="o">=</span><span class="n">arff_file_is_sparse</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
<p>Or if you also want the metadata: feature and label names:</p>
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<span></span><span class="n">X</span><span class="p">,</span> <span class="n">y</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_from_arff</span><span class="p">(</span>
<span class="n">path_to_arff_file</span><span class="p">,</span>
<span class="n">label_count</span><span class="o">=</span><span class="n">label_count</span><span class="p">,</span>
<span class="n">label_location</span><span class="o">=</span><span class="n">label_location</span><span class="p">,</span>
<span class="n">load_sparse</span><span class="o">=</span><span class="n">arff_file_is_sparse</span><span class="p">,</span>
<span class="n">return_attribute_definitions</span><span class="o">=</span><span class="bp">True</span>
<span class="p">)</span>
</pre></div>
</div>
</div>
<p>As you can see scikit-multilearn encodes nominal types by default as
integers, and converts the input space to floats, while the output space
to binary indicators 0/1 represented as integers. To change this
behavior specify your own params to <code class="docutils literal notranslate"><span class="pre">load_from_arff</span></code> as described in
the API documentation.</p>
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<span></span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">feature_names</span><span class="p">[:</span><span class="mi">3</span><span class="p">],</span> <span class="n">label_names</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
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<span></span>(<65x19 sparse matrix of type '<class 'numpy.float64'>'
with 491 stored elements in LInked List format>,
<65x7 sparse matrix of type '<class 'numpy.int64'>'
with 217 stored elements in LInked List format>,
[('landmass', ['1', '2', '3', '4', '5', '6']),
('zone', ['1', '2', '3', '4']),
('area', 'NUMERIC')],
[('red', ['0', '1']), ('green', ['0', '1']), ('blue', ['0', '1'])])
</pre></div>
</div>
</div>
<p>If you want to save ARFF files, you can use the :meth:<code class="docutils literal notranslate"><span class="pre">save_arff</span></code>
function, which can both return a string containing an ARFF dump of the
data set, or save it to a provided file when the <code class="docutils literal notranslate"><span class="pre">filename</span></code> argument
is passed.</p>
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</div>
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<span></span><span class="kn">from</span> <span class="nn">skmultilearn.dataset</span> <span class="kn">import</span> <span class="n">save_to_arff</span>
</pre></div>
</div>
</div>
<p>Let’s say we want to save a subset of the data in a sparse format and
with labels at the begining of the file.</p>
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<span></span><span class="k">print</span><span class="p">(</span><span class="n">save_to_arff</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">10</span><span class="p">,:</span><span class="mi">4</span><span class="p">],</span> <span class="n">y</span><span class="p">[:</span><span class="mi">10</span><span class="p">,</span> <span class="p">:</span><span class="mi">3</span><span class="p">],</span> <span class="n">label_location</span><span class="o">=</span><span class="s1">'start'</span><span class="p">,</span> <span class="n">save_sparse</span><span class="o">=</span><span class="bp">True</span><span class="p">))</span>
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</div>
</div>
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% traindata
@RELATION "traindata: -C 3"
@ATTRIBUTE y0 {0, 1}
@ATTRIBUTE y1 {0, 1}
@ATTRIBUTE y2 {0, 1}
@ATTRIBUTE X0 NUMERIC
@ATTRIBUTE X1 NUMERIC
@ATTRIBUTE X2 NUMERIC
@ATTRIBUTE X3 NUMERIC
@DATA
{ 0 1,3 3.0,5 1001.0,6 47.0 }
{ 2 1,3 1.0,4 2.0,5 178.0,6 3.0 }
{ 0 1,2 1,3 1.0,4 3.0,5 76.0,6 2.0 }
{ 0 1,2 1,3 5.0,4 1.0 }
{ 0 1,3 4.0,5 47.0,6 1.0 }
{ 2 1,4 3.0 }
{ 0 1,2 1,3 4.0,5 121.0,6 18.0 }
{ 0 1,1 1,3 2.0,5 301.0,6 57.0 }
{ 0 1,1 1,3 4.0 }
{ 0 1,1 1,3 3.0,5 2388.0,6 20.0 }
</pre></div></div>
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@ARTICLE{2017arXiv170201460S,
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