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<section id="installation">
<h1>Installation<a class="headerlink" href="#installation" title="Permalink to this heading">¶</a></h1>
<p>QuaPy can be easily installed via <cite>pip</cite></p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">quapy</span>
</pre></div>
</div>
<p>See <a class="reference external" href="https://pypi.org/project/QuaPy/">pip page</a> for older versions.</p>
<section id="requirements">
<h2>Requirements<a class="headerlink" href="#requirements" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p>scikit-learn, numpy, scipy</p></li>
<li><p>pytorch (for QuaNet)</p></li>
<li><p>svmperf patched for quantification (see below)</p></li>
<li><p>joblib</p></li>
<li><p>tqdm</p></li>
<li><p>pandas, xlrd</p></li>
<li><p>matplotlib</p></li>
</ul>
</section>
<section id="svm-perf-with-quantification-oriented-losses">
<h2>SVM-perf with quantification-oriented losses<a class="headerlink" href="#svm-perf-with-quantification-oriented-losses" title="Permalink to this heading">¶</a></h2>
<p>In order to run experiments involving SVM(Q), SVM(KLD), SVM(NKLD),
SVM(AE), or SVM(RAE), you have to first download the
<a class="reference external" href="http://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html">svmperf</a>
package, apply the patch
<a class="reference external" href="https://github.com/HLT-ISTI/QuaPy/blob/master/svm-perf-quantification-ext.patch">svm-perf-quantification-ext.patch</a>,
and compile the sources.
The script
<a class="reference external" href="https://github.com/HLT-ISTI/QuaPy/blob/master/prepare_svmperf.sh">prepare_svmperf.sh</a>,
does all the job. Simply run:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">./</span><span class="n">prepare_svmperf</span><span class="o">.</span><span class="n">sh</span>
</pre></div>
</div>
<p>The resulting directory <cite>./svm_perf_quantification</cite> contains the
patched version of <cite>svmperf</cite> with quantification-oriented losses.</p>
<p>The
<a class="reference external" href="https://github.com/HLT-ISTI/QuaPy/blob/master/svm-perf-quantification-ext.patch">svm-perf-quantification-ext.patch</a>
is an extension of the patch made available by
<a class="reference external" href="https://dl.acm.org/doi/abs/10.1145/2700406?casa_token=8D2fHsGCVn0AAAAA:ZfThYOvrzWxMGfZYlQW_y8Cagg-o_l6X_PcF09mdETQ4Tu7jK98mxFbGSXp9ZSO14JkUIYuDGFG0">Esuli et al. 2015</a>
that allows SVMperf to optimize for
the <cite>Q</cite> measure as proposed by
<a class="reference external" href="https://www.sciencedirect.com/science/article/abs/pii/S003132031400291X">Barranquero et al. 2015</a>
and for the <cite>KLD</cite> and <cite>NKLD</cite> as proposed by
<a class="reference external" href="https://dl.acm.org/doi/abs/10.1145/2700406?casa_token=8D2fHsGCVn0AAAAA:ZfThYOvrzWxMGfZYlQW_y8Cagg-o_l6X_PcF09mdETQ4Tu7jK98mxFbGSXp9ZSO14JkUIYuDGFG0">Esuli et al. 2015</a>
for quantification.
This patch extends the former by also allowing SVMperf to optimize for
<cite>AE</cite> and <cite>RAE</cite>.</p>
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<h3><a href="index.html">Table of Contents</a></h3>
<ul>
<li><a class="reference internal" href="#">Installation</a><ul>
<li><a class="reference internal" href="#requirements">Requirements</a></li>
<li><a class="reference internal" href="#svm-perf-with-quantification-oriented-losses">SVM-perf with quantification-oriented losses</a></li>
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