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<div class="tex2jax_ignore mathjax_ignore section" id="datasets">
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<h1>Datasets<a class="headerlink" href="#datasets" title="Permalink to this headline">¶</a></h1>
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<p>QuaPy makes available several datasets that have been used in
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quantification literature, as well as an interface to allow
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anyone import their custom datasets.</p>
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<p>A <em>Dataset</em> object in QuaPy is roughly a pair of <em>LabelledCollection</em> objects,
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one playing the role of the training set, another the test set.
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<em>LabelledCollection</em> is a data class consisting of the (iterable)
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instances and labels. This class handles most of the sampling functionality in QuaPy.
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Take a look at the following code:</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
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<span class="kn">import</span> <span class="nn">quapy.functional</span> <span class="k">as</span> <span class="nn">F</span>
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<span class="n">instances</span> <span class="o">=</span> <span class="p">[</span>
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<span class="s1">'1st positive document'</span><span class="p">,</span> <span class="s1">'2nd positive document'</span><span class="p">,</span>
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<span class="s1">'the only negative document'</span><span class="p">,</span>
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<span class="s1">'1st neutral document'</span><span class="p">,</span> <span class="s1">'2nd neutral document'</span><span class="p">,</span> <span class="s1">'3rd neutral document'</span>
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<span class="p">]</span>
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<span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
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<span class="n">data</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="p">(</span><span class="n">instances</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
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<span class="nb">print</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">strprev</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">prevalence</span><span class="p">(),</span> <span class="n">prec</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
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</pre></div>
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</div>
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<p>Output the class prevalences (showing 2 digit precision):</p>
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<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="mf">0.17</span><span class="p">,</span> <span class="mf">0.50</span><span class="p">,</span> <span class="mf">0.33</span><span class="p">]</span>
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</pre></div>
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</div>
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<p>One can easily produce new samples at desired class prevalences:</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">sample_size</span> <span class="o">=</span> <span class="mi">10</span>
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<span class="n">prev</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">]</span>
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<span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="n">sample_size</span><span class="p">,</span> <span class="o">*</span><span class="n">prev</span><span class="p">)</span>
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<span class="nb">print</span><span class="p">(</span><span class="s1">'instances:'</span><span class="p">,</span> <span class="n">sample</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
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<span class="nb">print</span><span class="p">(</span><span class="s1">'labels:'</span><span class="p">,</span> <span class="n">sample</span><span class="o">.</span><span class="n">labels</span><span class="p">)</span>
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<span class="nb">print</span><span class="p">(</span><span class="s1">'prevalence:'</span><span class="p">,</span> <span class="n">F</span><span class="o">.</span><span class="n">strprev</span><span class="p">(</span><span class="n">sample</span><span class="o">.</span><span class="n">prevalence</span><span class="p">(),</span> <span class="n">prec</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
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</pre></div>
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</div>
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<p>Which outputs:</p>
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<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">instances</span><span class="p">:</span> <span class="p">[</span><span class="s1">'the only negative document'</span> <span class="s1">'2nd positive document'</span>
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<span class="s1">'2nd positive document'</span> <span class="s1">'2nd neutral document'</span> <span class="s1">'1st positive document'</span>
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<span class="s1">'the only negative document'</span> <span class="s1">'the only negative document'</span>
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<span class="s1">'the only negative document'</span> <span class="s1">'2nd positive document'</span>
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<span class="s1">'1st positive document'</span><span class="p">]</span>
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<span class="n">labels</span><span class="p">:</span> <span class="p">[</span><span class="mi">0</span> <span class="mi">2</span> <span class="mi">2</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">0</span> <span class="mi">2</span> <span class="mi">2</span><span class="p">]</span>
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<span class="n">prevalence</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.40</span><span class="p">,</span> <span class="mf">0.10</span><span class="p">,</span> <span class="mf">0.50</span><span class="p">]</span>
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</pre></div>
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</div>
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<p>Samples can be made consistent across different runs (e.g., to test
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different methods on the same exact samples) by sampling and retaining
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the indexes, that can then be used to generate the sample:</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">index</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sampling_index</span><span class="p">(</span><span class="n">sample_size</span><span class="p">,</span> <span class="o">*</span><span class="n">prev</span><span class="p">)</span>
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<span class="k">for</span> <span class="n">method</span> <span class="ow">in</span> <span class="n">methods</span><span class="p">:</span>
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<span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">index</span><span class="p">)</span>
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<span class="o">...</span>
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</pre></div>
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</div>
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<p>QuaPy also implements the artificial sampling protocol that produces (via a
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Python’s generator) a series of <em>LabelledCollection</em> objects with equidistant
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prevalences ranging across the entire prevalence spectrum in the simplex space, e.g.:</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">sample</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">artificial_sampling_generator</span><span class="p">(</span><span class="n">sample_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">n_prevalences</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
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<span class="nb">print</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">strprev</span><span class="p">(</span><span class="n">sample</span><span class="o">.</span><span class="n">prevalence</span><span class="p">(),</span> <span class="n">prec</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
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</pre></div>
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</div>
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<p>produces one sampling for each (valid) combination of prevalences originating from
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splitting the range [0,1] into n_prevalences=5 points (i.e., [0, 0.25, 0.5, 0.75, 1]),
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that is:</p>
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<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="mf">0.00</span><span class="p">,</span> <span class="mf">0.00</span><span class="p">,</span> <span class="mf">1.00</span><span class="p">]</span>
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<span class="p">[</span><span class="mf">0.00</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">]</span>
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<span class="p">[</span><span class="mf">0.00</span><span class="p">,</span> <span class="mf">0.50</span><span class="p">,</span> <span class="mf">0.50</span><span class="p">]</span>
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<span class="p">[</span><span class="mf">0.00</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">]</span>
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<span class="p">[</span><span class="mf">0.00</span><span class="p">,</span> <span class="mf">1.00</span><span class="p">,</span> <span class="mf">0.00</span><span class="p">]</span>
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<span class="p">[</span><span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.00</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">]</span>
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<span class="o">...</span>
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<span class="p">[</span><span class="mf">1.00</span><span class="p">,</span> <span class="mf">0.00</span><span class="p">,</span> <span class="mf">0.00</span><span class="p">]</span>
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</pre></div>
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</div>
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<p>See the <a class="reference external" href="https://github.com/HLT-ISTI/QuaPy/wiki/Evaluation">Evaluation wiki</a> for
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further details on how to use the artificial sampling protocol to properly
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evaluate a quantification method.</p>
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<div class="section" id="reviews-datasets">
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<h2>Reviews Datasets<a class="headerlink" href="#reviews-datasets" title="Permalink to this headline">¶</a></h2>
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<p>Three datasets of reviews about Kindle devices, Harry Potter’s series, and
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the well-known IMDb movie reviews can be fetched using a unified interface.
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For example:</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
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<span class="n">data</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="s1">'kindle'</span><span class="p">)</span>
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</pre></div>
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</div>
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<p>These datasets have been used in:</p>
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<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Esuli</span><span class="p">,</span> <span class="n">A</span><span class="o">.</span><span class="p">,</span> <span class="n">Moreo</span><span class="p">,</span> <span class="n">A</span><span class="o">.</span><span class="p">,</span> <span class="o">&</span> <span class="n">Sebastiani</span><span class="p">,</span> <span class="n">F</span><span class="o">.</span> <span class="p">(</span><span class="mi">2018</span><span class="p">,</span> <span class="n">October</span><span class="p">)</span><span class="o">.</span>
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<span class="n">A</span> <span class="n">recurrent</span> <span class="n">neural</span> <span class="n">network</span> <span class="k">for</span> <span class="n">sentiment</span> <span class="n">quantification</span><span class="o">.</span>
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<span class="n">In</span> <span class="n">Proceedings</span> <span class="n">of</span> <span class="n">the</span> <span class="mi">27</span><span class="n">th</span> <span class="n">ACM</span> <span class="n">International</span> <span class="n">Conference</span> <span class="n">on</span>
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<span class="n">Information</span> <span class="ow">and</span> <span class="n">Knowledge</span> <span class="n">Management</span> <span class="p">(</span><span class="n">pp</span><span class="o">.</span> <span class="mi">1775</span><span class="o">-</span><span class="mi">1778</span><span class="p">)</span><span class="o">.</span>
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</pre></div>
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</div>
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<p>The list of reviews ids is available in:</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">REVIEWS_SENTIMENT_DATASETS</span>
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</pre></div>
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</div>
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<p>Some statistics of the fhe available datasets are summarized below:</p>
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<table class="colwidths-auto docutils align-default">
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<thead>
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<tr class="row-odd"><th class="head"><p>Dataset</p></th>
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<th class="text-align:center head"><p>classes</p></th>
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<th class="text-align:center head"><p>train size</p></th>
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<th class="text-align:center head"><p>test size</p></th>
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<th class="text-align:center head"><p>train prev</p></th>
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<th class="text-align:center head"><p>test prev</p></th>
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<th class="head"><p>type</p></th>
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</tr>
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</thead>
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<tbody>
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<tr class="row-even"><td><p>hp</p></td>
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<td class="text-align:center"><p>2</p></td>
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<td class="text-align:center"><p>9533</p></td>
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<td class="text-align:center"><p>18399</p></td>
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<td class="text-align:center"><p>[0.018, 0.982]</p></td>
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<td class="text-align:center"><p>[0.065, 0.935]</p></td>
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<td><p>text</p></td>
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</tr>
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<tr class="row-odd"><td><p>kindle</p></td>
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<td class="text-align:center"><p>2</p></td>
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<td class="text-align:center"><p>3821</p></td>
|
||||
<td class="text-align:center"><p>21591</p></td>
|
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<td class="text-align:center"><p>[0.081, 0.919]</p></td>
|
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<td class="text-align:center"><p>[0.063, 0.937]</p></td>
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<td><p>text</p></td>
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</tr>
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<tr class="row-even"><td><p>imdb</p></td>
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<td class="text-align:center"><p>2</p></td>
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<td class="text-align:center"><p>25000</p></td>
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<td class="text-align:center"><p>25000</p></td>
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<td class="text-align:center"><p>[0.500, 0.500]</p></td>
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<td class="text-align:center"><p>[0.500, 0.500]</p></td>
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<td><p>text</p></td>
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</tr>
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</tbody>
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</table>
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</div>
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<div class="section" id="twitter-sentiment-datasets">
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<h2>Twitter Sentiment Datasets<a class="headerlink" href="#twitter-sentiment-datasets" title="Permalink to this headline">¶</a></h2>
|
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<p>11 Twitter datasets for sentiment analysis.
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Text is not accessible, and the documents were made available
|
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in tf-idf format. Each dataset presents two splits: a train/val
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split for model selection purposes, and a train+val/test split
|
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for model evaluation. The following code exemplifies how to load
|
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a twitter dataset for model selection.</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
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<span class="n">data</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_twitter</span><span class="p">(</span><span class="s1">'gasp'</span><span class="p">,</span> <span class="n">for_model_selection</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
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</pre></div>
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</div>
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<p>The datasets were used in:</p>
|
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<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Gao</span><span class="p">,</span> <span class="n">W</span><span class="o">.</span><span class="p">,</span> <span class="o">&</span> <span class="n">Sebastiani</span><span class="p">,</span> <span class="n">F</span><span class="o">.</span> <span class="p">(</span><span class="mi">2015</span><span class="p">,</span> <span class="n">August</span><span class="p">)</span><span class="o">.</span>
|
||||
<span class="n">Tweet</span> <span class="n">sentiment</span><span class="p">:</span> <span class="n">From</span> <span class="n">classification</span> <span class="n">to</span> <span class="n">quantification</span><span class="o">.</span>
|
||||
<span class="n">In</span> <span class="mi">2015</span> <span class="n">IEEE</span><span class="o">/</span><span class="n">ACM</span> <span class="n">International</span> <span class="n">Conference</span> <span class="n">on</span> <span class="n">Advances</span> <span class="ow">in</span>
|
||||
<span class="n">Social</span> <span class="n">Networks</span> <span class="n">Analysis</span> <span class="ow">and</span> <span class="n">Mining</span> <span class="p">(</span><span class="n">ASONAM</span><span class="p">)</span> <span class="p">(</span><span class="n">pp</span><span class="o">.</span> <span class="mi">97</span><span class="o">-</span><span class="mi">104</span><span class="p">)</span><span class="o">.</span> <span class="n">IEEE</span><span class="o">.</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>Three of the datasets (semeval13, semeval14, and semeval15) share the
|
||||
same training set (semeval), meaning that the training split one would get
|
||||
when requesting any of them is the same. The dataset “semeval” can only
|
||||
be requested with “for_model_selection=True”.
|
||||
The lists of the Twitter dataset’s ids can be consulted in:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># a list of 11 dataset ids that can be used for model selection or model evaluation</span>
|
||||
<span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">TWITTER_SENTIMENT_DATASETS_TEST</span>
|
||||
|
||||
<span class="c1"># 9 dataset ids in which "semeval13", "semeval14", and "semeval15" are replaced with "semeval"</span>
|
||||
<span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">TWITTER_SENTIMENT_DATASETS_TRAIN</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>Some details can be found below:</p>
|
||||
<table class="colwidths-auto docutils align-default">
|
||||
<thead>
|
||||
<tr class="row-odd"><th class="head"><p>Dataset</p></th>
|
||||
<th class="text-align:center head"><p>classes</p></th>
|
||||
<th class="text-align:center head"><p>train size</p></th>
|
||||
<th class="text-align:center head"><p>test size</p></th>
|
||||
<th class="text-align:center head"><p>features</p></th>
|
||||
<th class="text-align:center head"><p>train prev</p></th>
|
||||
<th class="text-align:center head"><p>test prev</p></th>
|
||||
<th class="head"><p>type</p></th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
<tr class="row-even"><td><p>gasp</p></td>
|
||||
<td class="text-align:center"><p>3</p></td>
|
||||
<td class="text-align:center"><p>8788</p></td>
|
||||
<td class="text-align:center"><p>3765</p></td>
|
||||
<td class="text-align:center"><p>694582</p></td>
|
||||
<td class="text-align:center"><p>[0.421, 0.496, 0.082]</p></td>
|
||||
<td class="text-align:center"><p>[0.407, 0.507, 0.086]</p></td>
|
||||
<td><p>sparse</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p>hcr</p></td>
|
||||
<td class="text-align:center"><p>3</p></td>
|
||||
<td class="text-align:center"><p>1594</p></td>
|
||||
<td class="text-align:center"><p>798</p></td>
|
||||
<td class="text-align:center"><p>222046</p></td>
|
||||
<td class="text-align:center"><p>[0.546, 0.211, 0.243]</p></td>
|
||||
<td class="text-align:center"><p>[0.640, 0.167, 0.193]</p></td>
|
||||
<td><p>sparse</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p>omd</p></td>
|
||||
<td class="text-align:center"><p>3</p></td>
|
||||
<td class="text-align:center"><p>1839</p></td>
|
||||
<td class="text-align:center"><p>787</p></td>
|
||||
<td class="text-align:center"><p>199151</p></td>
|
||||
<td class="text-align:center"><p>[0.463, 0.271, 0.266]</p></td>
|
||||
<td class="text-align:center"><p>[0.437, 0.283, 0.280]</p></td>
|
||||
<td><p>sparse</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p>sanders</p></td>
|
||||
<td class="text-align:center"><p>3</p></td>
|
||||
<td class="text-align:center"><p>2155</p></td>
|
||||
<td class="text-align:center"><p>923</p></td>
|
||||
<td class="text-align:center"><p>229399</p></td>
|
||||
<td class="text-align:center"><p>[0.161, 0.691, 0.148]</p></td>
|
||||
<td class="text-align:center"><p>[0.164, 0.688, 0.148]</p></td>
|
||||
<td><p>sparse</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p>semeval13</p></td>
|
||||
<td class="text-align:center"><p>3</p></td>
|
||||
<td class="text-align:center"><p>11338</p></td>
|
||||
<td class="text-align:center"><p>3813</p></td>
|
||||
<td class="text-align:center"><p>1215742</p></td>
|
||||
<td class="text-align:center"><p>[0.159, 0.470, 0.372]</p></td>
|
||||
<td class="text-align:center"><p>[0.158, 0.430, 0.412]</p></td>
|
||||
<td><p>sparse</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p>semeval14</p></td>
|
||||
<td class="text-align:center"><p>3</p></td>
|
||||
<td class="text-align:center"><p>11338</p></td>
|
||||
<td class="text-align:center"><p>1853</p></td>
|
||||
<td class="text-align:center"><p>1215742</p></td>
|
||||
<td class="text-align:center"><p>[0.159, 0.470, 0.372]</p></td>
|
||||
<td class="text-align:center"><p>[0.109, 0.361, 0.530]</p></td>
|
||||
<td><p>sparse</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p>semeval15</p></td>
|
||||
<td class="text-align:center"><p>3</p></td>
|
||||
<td class="text-align:center"><p>11338</p></td>
|
||||
<td class="text-align:center"><p>2390</p></td>
|
||||
<td class="text-align:center"><p>1215742</p></td>
|
||||
<td class="text-align:center"><p>[0.159, 0.470, 0.372]</p></td>
|
||||
<td class="text-align:center"><p>[0.153, 0.413, 0.434]</p></td>
|
||||
<td><p>sparse</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p>semeval16</p></td>
|
||||
<td class="text-align:center"><p>3</p></td>
|
||||
<td class="text-align:center"><p>8000</p></td>
|
||||
<td class="text-align:center"><p>2000</p></td>
|
||||
<td class="text-align:center"><p>889504</p></td>
|
||||
<td class="text-align:center"><p>[0.157, 0.351, 0.492]</p></td>
|
||||
<td class="text-align:center"><p>[0.163, 0.341, 0.497]</p></td>
|
||||
<td><p>sparse</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p>sst</p></td>
|
||||
<td class="text-align:center"><p>3</p></td>
|
||||
<td class="text-align:center"><p>2971</p></td>
|
||||
<td class="text-align:center"><p>1271</p></td>
|
||||
<td class="text-align:center"><p>376132</p></td>
|
||||
<td class="text-align:center"><p>[0.261, 0.452, 0.288]</p></td>
|
||||
<td class="text-align:center"><p>[0.207, 0.481, 0.312]</p></td>
|
||||
<td><p>sparse</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p>wa</p></td>
|
||||
<td class="text-align:center"><p>3</p></td>
|
||||
<td class="text-align:center"><p>2184</p></td>
|
||||
<td class="text-align:center"><p>936</p></td>
|
||||
<td class="text-align:center"><p>248563</p></td>
|
||||
<td class="text-align:center"><p>[0.305, 0.414, 0.281]</p></td>
|
||||
<td class="text-align:center"><p>[0.282, 0.446, 0.272]</p></td>
|
||||
<td><p>sparse</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p>wb</p></td>
|
||||
<td class="text-align:center"><p>3</p></td>
|
||||
<td class="text-align:center"><p>4259</p></td>
|
||||
<td class="text-align:center"><p>1823</p></td>
|
||||
<td class="text-align:center"><p>404333</p></td>
|
||||
<td class="text-align:center"><p>[0.270, 0.392, 0.337]</p></td>
|
||||
<td class="text-align:center"><p>[0.274, 0.392, 0.335]</p></td>
|
||||
<td><p>sparse</p></td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
<div class="section" id="uci-machine-learning">
|
||||
<h2>UCI Machine Learning<a class="headerlink" href="#uci-machine-learning" title="Permalink to this headline">¶</a></h2>
|
||||
<p>A set of 32 datasets from the <a class="reference external" href="https://archive.ics.uci.edu/ml/datasets.php">UCI Machine Learning repository</a>
|
||||
used in:</p>
|
||||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Pérez</span><span class="o">-</span><span class="n">Gállego</span><span class="p">,</span> <span class="n">P</span><span class="o">.</span><span class="p">,</span> <span class="n">Quevedo</span><span class="p">,</span> <span class="n">J</span><span class="o">.</span> <span class="n">R</span><span class="o">.</span><span class="p">,</span> <span class="o">&</span> <span class="k">del</span> <span class="n">Coz</span><span class="p">,</span> <span class="n">J</span><span class="o">.</span> <span class="n">J</span><span class="o">.</span> <span class="p">(</span><span class="mi">2017</span><span class="p">)</span><span class="o">.</span>
|
||||
<span class="n">Using</span> <span class="n">ensembles</span> <span class="k">for</span> <span class="n">problems</span> <span class="k">with</span> <span class="n">characterizable</span> <span class="n">changes</span>
|
||||
<span class="ow">in</span> <span class="n">data</span> <span class="n">distribution</span><span class="p">:</span> <span class="n">A</span> <span class="n">case</span> <span class="n">study</span> <span class="n">on</span> <span class="n">quantification</span><span class="o">.</span>
|
||||
<span class="n">Information</span> <span class="n">Fusion</span><span class="p">,</span> <span class="mi">34</span><span class="p">,</span> <span class="mi">87</span><span class="o">-</span><span class="mf">100.</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>The list does not exactly coincide with that used in Pérez-Gállego et al. 2017
|
||||
since we were unable to find the datasets with ids “diabetes” and “phoneme”.</p>
|
||||
<p>These dataset can be loaded by calling, e.g.:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_UCIDataset</span><span class="p">(</span><span class="s1">'yeast'</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>This call will return a <em>Dataset</em> object in which the training and
|
||||
test splits are randomly drawn, in a stratified manner, from the whole
|
||||
collection at 70% and 30%, respectively. The <em>verbose=True</em> option indicates
|
||||
that the dataset description should be printed in standard output.
|
||||
The original data is not split,
|
||||
and some papers submit the entire collection to a kFCV validation.
|
||||
In order to accommodate with these practices, one could first instantiate
|
||||
the entire collection, and then creating a generator that will return one
|
||||
training+test dataset at a time, following a kFCV protocol:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<span class="n">collection</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_UCILabelledCollection</span><span class="p">(</span><span class="s2">"yeast"</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">Dataset</span><span class="o">.</span><span class="n">kFCV</span><span class="p">(</span><span class="n">collection</span><span class="p">,</span> <span class="n">nfolds</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">nrepeats</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
|
||||
<span class="o">...</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>Above code will allow to conduct a 2x5FCV evaluation on the “yeast” dataset.</p>
|
||||
<p>All datasets come in numerical form (dense matrices); some statistics
|
||||
are summarized below.</p>
|
||||
<table class="colwidths-auto docutils align-default">
|
||||
<thead>
|
||||
<tr class="row-odd"><th class="head"><p>Dataset</p></th>
|
||||
<th class="text-align:center head"><p>classes</p></th>
|
||||
<th class="text-align:center head"><p>instances</p></th>
|
||||
<th class="text-align:center head"><p>features</p></th>
|
||||
<th class="text-align:center head"><p>prev</p></th>
|
||||
<th class="head"><p>type</p></th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
<tr class="row-even"><td><p>acute.a</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>120</p></td>
|
||||
<td class="text-align:center"><p>6</p></td>
|
||||
<td class="text-align:center"><p>[0.508, 0.492]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p>acute.b</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>120</p></td>
|
||||
<td class="text-align:center"><p>6</p></td>
|
||||
<td class="text-align:center"><p>[0.583, 0.417]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p>balance.1</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>625</p></td>
|
||||
<td class="text-align:center"><p>4</p></td>
|
||||
<td class="text-align:center"><p>[0.539, 0.461]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p>balance.2</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>625</p></td>
|
||||
<td class="text-align:center"><p>4</p></td>
|
||||
<td class="text-align:center"><p>[0.922, 0.078]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p>balance.3</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>625</p></td>
|
||||
<td class="text-align:center"><p>4</p></td>
|
||||
<td class="text-align:center"><p>[0.539, 0.461]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p>breast-cancer</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>683</p></td>
|
||||
<td class="text-align:center"><p>9</p></td>
|
||||
<td class="text-align:center"><p>[0.350, 0.650]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p>cmc.1</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>1473</p></td>
|
||||
<td class="text-align:center"><p>9</p></td>
|
||||
<td class="text-align:center"><p>[0.573, 0.427]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p>cmc.2</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>1473</p></td>
|
||||
<td class="text-align:center"><p>9</p></td>
|
||||
<td class="text-align:center"><p>[0.774, 0.226]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p>cmc.3</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>1473</p></td>
|
||||
<td class="text-align:center"><p>9</p></td>
|
||||
<td class="text-align:center"><p>[0.653, 0.347]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p>ctg.1</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>2126</p></td>
|
||||
<td class="text-align:center"><p>22</p></td>
|
||||
<td class="text-align:center"><p>[0.222, 0.778]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p>ctg.2</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>2126</p></td>
|
||||
<td class="text-align:center"><p>22</p></td>
|
||||
<td class="text-align:center"><p>[0.861, 0.139]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p>ctg.3</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>2126</p></td>
|
||||
<td class="text-align:center"><p>22</p></td>
|
||||
<td class="text-align:center"><p>[0.917, 0.083]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p>german</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>1000</p></td>
|
||||
<td class="text-align:center"><p>24</p></td>
|
||||
<td class="text-align:center"><p>[0.300, 0.700]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p>haberman</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>306</p></td>
|
||||
<td class="text-align:center"><p>3</p></td>
|
||||
<td class="text-align:center"><p>[0.735, 0.265]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p>ionosphere</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>351</p></td>
|
||||
<td class="text-align:center"><p>34</p></td>
|
||||
<td class="text-align:center"><p>[0.641, 0.359]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p>iris.1</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>150</p></td>
|
||||
<td class="text-align:center"><p>4</p></td>
|
||||
<td class="text-align:center"><p>[0.667, 0.333]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p>iris.2</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>150</p></td>
|
||||
<td class="text-align:center"><p>4</p></td>
|
||||
<td class="text-align:center"><p>[0.667, 0.333]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p>iris.3</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>150</p></td>
|
||||
<td class="text-align:center"><p>4</p></td>
|
||||
<td class="text-align:center"><p>[0.667, 0.333]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p>mammographic</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>830</p></td>
|
||||
<td class="text-align:center"><p>5</p></td>
|
||||
<td class="text-align:center"><p>[0.514, 0.486]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p>pageblocks.5</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>5473</p></td>
|
||||
<td class="text-align:center"><p>10</p></td>
|
||||
<td class="text-align:center"><p>[0.979, 0.021]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p>semeion</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>1593</p></td>
|
||||
<td class="text-align:center"><p>256</p></td>
|
||||
<td class="text-align:center"><p>[0.901, 0.099]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p>sonar</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>208</p></td>
|
||||
<td class="text-align:center"><p>60</p></td>
|
||||
<td class="text-align:center"><p>[0.534, 0.466]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p>spambase</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>4601</p></td>
|
||||
<td class="text-align:center"><p>57</p></td>
|
||||
<td class="text-align:center"><p>[0.606, 0.394]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p>spectf</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>267</p></td>
|
||||
<td class="text-align:center"><p>44</p></td>
|
||||
<td class="text-align:center"><p>[0.794, 0.206]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p>tictactoe</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>958</p></td>
|
||||
<td class="text-align:center"><p>9</p></td>
|
||||
<td class="text-align:center"><p>[0.653, 0.347]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p>transfusion</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>748</p></td>
|
||||
<td class="text-align:center"><p>4</p></td>
|
||||
<td class="text-align:center"><p>[0.762, 0.238]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p>wdbc</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>569</p></td>
|
||||
<td class="text-align:center"><p>30</p></td>
|
||||
<td class="text-align:center"><p>[0.627, 0.373]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p>wine.1</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>178</p></td>
|
||||
<td class="text-align:center"><p>13</p></td>
|
||||
<td class="text-align:center"><p>[0.669, 0.331]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p>wine.2</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>178</p></td>
|
||||
<td class="text-align:center"><p>13</p></td>
|
||||
<td class="text-align:center"><p>[0.601, 0.399]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p>wine.3</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>178</p></td>
|
||||
<td class="text-align:center"><p>13</p></td>
|
||||
<td class="text-align:center"><p>[0.730, 0.270]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p>wine-q-red</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>1599</p></td>
|
||||
<td class="text-align:center"><p>11</p></td>
|
||||
<td class="text-align:center"><p>[0.465, 0.535]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-odd"><td><p>wine-q-white</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>4898</p></td>
|
||||
<td class="text-align:center"><p>11</p></td>
|
||||
<td class="text-align:center"><p>[0.335, 0.665]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
<tr class="row-even"><td><p>yeast</p></td>
|
||||
<td class="text-align:center"><p>2</p></td>
|
||||
<td class="text-align:center"><p>1484</p></td>
|
||||
<td class="text-align:center"><p>8</p></td>
|
||||
<td class="text-align:center"><p>[0.711, 0.289]</p></td>
|
||||
<td><p>dense</p></td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<div class="section" id="issues">
|
||||
<h3>Issues:<a class="headerlink" href="#issues" title="Permalink to this headline">¶</a></h3>
|
||||
<p>All datasets will be downloaded automatically the first time they are requested, and
|
||||
stored in the <em>quapy_data</em> folder for faster further reuse.
|
||||
However, some datasets require special actions that at the moment are not fully
|
||||
automated.</p>
|
||||
<ul class="simple">
|
||||
<li><p>Datasets with ids “ctg.1”, “ctg.2”, and “ctg.3” (<em>Cardiotocography Data Set</em>) load
|
||||
an Excel file, which requires the user to install the <em>xlrd</em> Python module in order
|
||||
to open it.</p></li>
|
||||
<li><p>The dataset with id “pageblocks.5” (<em>Page Blocks Classification (5)</em>) needs to
|
||||
open a “unix compressed file” (extension .Z), which is not directly doable with
|
||||
standard Pythons packages like gzip or zip. This file would need to be uncompressed using
|
||||
OS-dependent software manually. Information on how to do it will be printed the first
|
||||
time the dataset is invoked.</p></li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
<div class="section" id="adding-custom-datasets">
|
||||
<h2>Adding Custom Datasets<a class="headerlink" href="#adding-custom-datasets" title="Permalink to this headline">¶</a></h2>
|
||||
<p>QuaPy provides data loaders for simple formats dealing with
|
||||
text, following the format:</p>
|
||||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">class</span><span class="o">-</span><span class="nb">id</span> \<span class="n">t</span> <span class="n">first</span> <span class="n">document</span><span class="s1">'s pre-processed text </span><span class="se">\n</span>
|
||||
<span class="n">class</span><span class="o">-</span><span class="nb">id</span> \<span class="n">t</span> <span class="n">second</span> <span class="n">document</span><span class="s1">'s pre-processed text </span><span class="se">\n</span>
|
||||
<span class="o">...</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>and sparse representations of the form:</p>
|
||||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">{</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="ow">or</span> <span class="o">+</span><span class="mi">1</span><span class="p">}</span> <span class="n">col</span><span class="p">(</span><span class="nb">int</span><span class="p">):</span><span class="n">val</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span> <span class="n">col</span><span class="p">(</span><span class="nb">int</span><span class="p">):</span><span class="n">val</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span> <span class="o">...</span> \<span class="n">n</span>
|
||||
<span class="o">...</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>The code in charge in loading a LabelledCollection is:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@classmethod</span>
|
||||
<span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span><span class="nb">str</span><span class="p">,</span> <span class="n">loader_func</span><span class="p">:</span><span class="n">callable</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="o">*</span><span class="n">loader_func</span><span class="p">(</span><span class="n">path</span><span class="p">))</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>indicating that any <em>loader_func</em> (e.g., a user-defined one) which
|
||||
returns valid arguments for initializing a <em>LabelledCollection</em> object will allow
|
||||
to load any collection. In particular, the <em>LabelledCollection</em> receives as
|
||||
arguments the instances (as an iterable) and the labels (as an iterable) and,
|
||||
additionally, the number of classes can be specified (it would otherwise be
|
||||
inferred from the labels, but that requires at least one positive example for
|
||||
all classes to be present in the collection).</p>
|
||||
<p>The same <em>loader_func</em> can be passed to a Dataset, along with two
|
||||
paths, in order to create a training and test pair of <em>LabelledCollection</em>,
|
||||
e.g.:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<span class="n">train_path</span> <span class="o">=</span> <span class="s1">'../my_data/train.dat'</span>
|
||||
<span class="n">test_path</span> <span class="o">=</span> <span class="s1">'../my_data/test.dat'</span>
|
||||
<span class="k">def</span> <span class="nf">my_custom_loader</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
|
||||
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">fin</span><span class="p">:</span>
|
||||
<span class="o">...</span>
|
||||
<span class="k">return</span> <span class="n">instances</span><span class="p">,</span> <span class="n">labels</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">Dataset</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">train_path</span><span class="p">,</span> <span class="n">test_path</span><span class="p">,</span> <span class="n">my_custom_loader</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<div class="section" id="data-processing">
|
||||
<h3>Data Processing<a class="headerlink" href="#data-processing" title="Permalink to this headline">¶</a></h3>
|
||||
<p>QuaPy implements a number of preprocessing functions in the package <em>qp.data.preprocessing</em>, including:</p>
|
||||
<ul class="simple">
|
||||
<li><p><em>text2tfidf</em>: tfidf vectorization</p></li>
|
||||
<li><p><em>reduce_columns</em>: reducing the number of columns based on term frequency</p></li>
|
||||
<li><p><em>standardize</em>: transforms the column values into z-scores (i.e., subtract the mean and normalizes by the standard deviation, so
|
||||
that the column values have zero mean and unit variance).</p></li>
|
||||
<li><p><em>index</em>: transforms textual tokens into lists of numeric ids)</p></li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
|
||||
<div class="clearer"></div>
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
<div class="sphinxsidebarwrapper">
|
||||
<h3><a href="index.html">Table of Contents</a></h3>
|
||||
<ul>
|
||||
<li><a class="reference internal" href="#">Datasets</a><ul>
|
||||
<li><a class="reference internal" href="#reviews-datasets">Reviews Datasets</a></li>
|
||||
<li><a class="reference internal" href="#twitter-sentiment-datasets">Twitter Sentiment Datasets</a></li>
|
||||
<li><a class="reference internal" href="#uci-machine-learning">UCI Machine Learning</a><ul>
|
||||
<li><a class="reference internal" href="#issues">Issues:</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><a class="reference internal" href="#adding-custom-datasets">Adding Custom Datasets</a><ul>
|
||||
<li><a class="reference internal" href="#data-processing">Data Processing</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
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|
||||
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|
||||
|
||||
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|
||||
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|
||||
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<div class="tex2jax_ignore mathjax_ignore section" id="evaluation">
|
||||
<h1>Evaluation<a class="headerlink" href="#evaluation" title="Permalink to this headline">¶</a></h1>
|
||||
<p>Quantification is an appealing tool in scenarios of dataset shift,
|
||||
and particularly in scenarios of prior-probability shift.
|
||||
That is, the interest in estimating the class prevalences arises
|
||||
under the belief that those class prevalences might have changed
|
||||
with respect to the ones observed during training.
|
||||
In other words, one could simply return the training prevalence
|
||||
as a predictor of the test prevalence if this change is assumed
|
||||
to be unlikely (as is the case in general scenarios of
|
||||
machine learning governed by the iid assumption).
|
||||
In brief, quantification requires dedicated evaluation protocols,
|
||||
which are implemented in QuaPy and explained here.</p>
|
||||
<div class="section" id="error-measures">
|
||||
<h2>Error Measures<a class="headerlink" href="#error-measures" title="Permalink to this headline">¶</a></h2>
|
||||
<p>The module quapy.error implements the following error measures for quantification:</p>
|
||||
<ul class="simple">
|
||||
<li><p><em>mae</em>: mean absolute error</p></li>
|
||||
<li><p><em>mrae</em>: mean relative absolute error</p></li>
|
||||
<li><p><em>mse</em>: mean squared error</p></li>
|
||||
<li><p><em>mkld</em>: mean Kullback-Leibler Divergence</p></li>
|
||||
<li><p><em>mnkld</em>: mean normalized Kullback-Leibler Divergence</p></li>
|
||||
</ul>
|
||||
<p>Functions <em>ae</em>, <em>rae</em>, <em>se</em>, <em>kld</em>, and <em>nkld</em> are also available,
|
||||
which return the individual errors (i.e., without averaging the whole).</p>
|
||||
<p>Some errors of classification are also available:</p>
|
||||
<ul class="simple">
|
||||
<li><p><em>acce</em>: accuracy error (1-accuracy)</p></li>
|
||||
<li><p><em>f1e</em>: F-1 score error (1-F1 score)</p></li>
|
||||
</ul>
|
||||
<p>The error functions implement the following interface, e.g.:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">mae</span><span class="p">(</span><span class="n">true_prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>in which the first argument is a ndarray containing the true
|
||||
prevalences, and the second argument is another ndarray with
|
||||
the estimations produced by some method.</p>
|
||||
<p>Some error functions, e.g., <em>mrae</em>, <em>mkld</em>, and <em>mnkld</em>, are
|
||||
smoothed for numerical stability. In those cases, there is a
|
||||
third argument, e.g.:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">mrae</span><span class="p">(</span><span class="n">true_prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> <span class="o">...</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>indicating the value for the smoothing parameter epsilon.
|
||||
Traditionally, this value is set to 1/(2T) in past literature,
|
||||
with T the sampling size. One could either pass this value
|
||||
to the function each time, or to set a QuaPy’s environment
|
||||
variable <em>SAMPLE_SIZE</em> once, and ommit this argument
|
||||
thereafter (recommended);
|
||||
e.g.:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'SAMPLE_SIZE'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">100</span> <span class="c1"># once for all</span>
|
||||
<span class="n">true_prev</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">])</span> <span class="c1"># let's assume 3 classes</span>
|
||||
<span class="n">estim_prev</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">])</span>
|
||||
<span class="n">error</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">ae_</span><span class="o">.</span><span class="n">mrae</span><span class="p">(</span><span class="n">true_prev</span><span class="p">,</span> <span class="n">estim_prev</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'mrae(</span><span class="si">{</span><span class="n">true_prev</span><span class="si">}</span><span class="s1">, </span><span class="si">{</span><span class="n">estim_prev</span><span class="si">}</span><span class="s1">) = </span><span class="si">{</span><span class="n">error</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>will print:</p>
|
||||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mrae</span><span class="p">([</span><span class="mf">0.500</span><span class="p">,</span> <span class="mf">0.300</span><span class="p">,</span> <span class="mf">0.200</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.100</span><span class="p">,</span> <span class="mf">0.300</span><span class="p">,</span> <span class="mf">0.600</span><span class="p">])</span> <span class="o">=</span> <span class="mf">0.914</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>Finally, it is possible to instantiate QuaPy’s quantification
|
||||
error functions from strings using, e.g.:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">error_function</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">ae_</span><span class="o">.</span><span class="n">from_name</span><span class="p">(</span><span class="s1">'mse'</span><span class="p">)</span>
|
||||
<span class="n">error</span> <span class="o">=</span> <span class="n">error_function</span><span class="p">(</span><span class="n">true_prev</span><span class="p">,</span> <span class="n">estim_prev</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="section" id="evaluation-protocols">
|
||||
<h2>Evaluation Protocols<a class="headerlink" href="#evaluation-protocols" title="Permalink to this headline">¶</a></h2>
|
||||
<p>QuaPy implements the so-called “artificial sampling protocol”,
|
||||
according to which a test set is used to generate samplings at
|
||||
desired prevalences of fixed size and covering the full spectrum
|
||||
of prevalences. This protocol is called “artificial” in contrast
|
||||
to the “natural prevalence sampling” protocol that,
|
||||
despite introducing some variability during sampling, approximately
|
||||
preserves the training class prevalence.</p>
|
||||
<p>In the artificial sampling procol, the user specifies the number
|
||||
of (equally distant) points to be generated from the interval [0,1].</p>
|
||||
<p>For example, if n_prevpoints=11 then, for each class, the prevalences
|
||||
[0., 0.1, 0.2, …, 1.] will be used. This means that, for two classes,
|
||||
the number of different prevalences will be 11 (since, once the prevalence
|
||||
of one class is determined, the other one is constrained). For 3 classes,
|
||||
the number of valid combinations can be obtained as 11 + 10 + … + 1 = 66.
|
||||
In general, the number of valid combinations that will be produced for a given
|
||||
value of n_prevpoints can be consulted by invoking
|
||||
quapy.functional.num_prevalence_combinations, e.g.:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">quapy.functional</span> <span class="k">as</span> <span class="nn">F</span>
|
||||
<span class="n">n_prevpoints</span> <span class="o">=</span> <span class="mi">21</span>
|
||||
<span class="n">n_classes</span> <span class="o">=</span> <span class="mi">4</span>
|
||||
<span class="n">n</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">num_prevalence_combinations</span><span class="p">(</span><span class="n">n_prevpoints</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">n_repeats</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>in this example, n=1771. Note the last argument, n_repeats, that
|
||||
informs of the number of examples that will be generated for any
|
||||
valid combination (typical values are, e.g., 1 for a single sample,
|
||||
or 10 or higher for computing standard deviations of performing statistical
|
||||
significance tests).</p>
|
||||
<p>One can instead work the other way around, i.e., one could set a
|
||||
maximum budged of evaluations and get the number of prevalence points that
|
||||
will generate a number of evaluations close, but not higher, than
|
||||
the fixed budget. This can be achieved with the function
|
||||
quapy.functional.get_nprevpoints_approximation, e.g.:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">budget</span> <span class="o">=</span> <span class="mi">5000</span>
|
||||
<span class="n">n_prevpoints</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">get_nprevpoints_approximation</span><span class="p">(</span><span class="n">budget</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">n_repeats</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">n</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">num_prevalence_combinations</span><span class="p">(</span><span class="n">n_prevpoints</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">n_repeats</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'by setting n_prevpoints=</span><span class="si">{</span><span class="n">n_prevpoints</span><span class="si">}</span><span class="s1"> the number of evaluations for </span><span class="si">{</span><span class="n">n_classes</span><span class="si">}</span><span class="s1"> classes will be </span><span class="si">{</span><span class="n">n</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>that will print:</p>
|
||||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">by</span> <span class="n">setting</span> <span class="n">n_prevpoints</span><span class="o">=</span><span class="mi">30</span> <span class="n">the</span> <span class="n">number</span> <span class="n">of</span> <span class="n">evaluations</span> <span class="k">for</span> <span class="mi">4</span> <span class="n">classes</span> <span class="n">will</span> <span class="n">be</span> <span class="mi">4960</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>The cost of evaluation will depend on the values of <em>n_prevpoints</em>, <em>n_classes</em>,
|
||||
and <em>n_repeats</em>. Since it might sometimes be cumbersome to control the overall
|
||||
cost of an experiment having to do with the number of combinations that
|
||||
will be generated for a particular setting of these arguments (particularly
|
||||
when <em>n_classes>2</em>), evaluation functions
|
||||
typically allow the user to rather specify an <em>evaluation budget</em>, i.e., a maximum
|
||||
number of samplings to generate. By specifying this argument, one could avoid
|
||||
specifying <em>n_prevpoints</em>, and the value for it that would lead to a closer
|
||||
number of evaluation budget, without surpassing it, will be automatically set.</p>
|
||||
<p>The following script shows a full example in which a PACC model relying
|
||||
on a Logistic Regressor classifier is
|
||||
tested on the <em>kindle</em> dataset by means of the artificial prevalence
|
||||
sampling protocol on samples of size 500, in terms of various
|
||||
evaluation metrics.</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<span class="kn">import</span> <span class="nn">quapy.functional</span> <span class="k">as</span> <span class="nn">F</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
|
||||
|
||||
<span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'SAMPLE_SIZE'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">500</span>
|
||||
|
||||
<span class="n">dataset</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="s1">'kindle'</span><span class="p">)</span>
|
||||
<span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">preprocessing</span><span class="o">.</span><span class="n">text2tfidf</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="n">training</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">training</span>
|
||||
<span class="n">test</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span>
|
||||
|
||||
<span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">()</span>
|
||||
<span class="n">pacc</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">method</span><span class="o">.</span><span class="n">aggregative</span><span class="o">.</span><span class="n">PACC</span><span class="p">(</span><span class="n">lr</span><span class="p">)</span>
|
||||
|
||||
<span class="n">pacc</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">training</span><span class="p">)</span>
|
||||
|
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">evaluation</span><span class="o">.</span><span class="n">artificial_sampling_report</span><span class="p">(</span>
|
||||
<span class="n">pacc</span><span class="p">,</span> <span class="c1"># the quantification method</span>
|
||||
<span class="n">test</span><span class="p">,</span> <span class="c1"># the test set on which the method will be evaluated</span>
|
||||
<span class="n">sample_size</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'SAMPLE_SIZE'</span><span class="p">],</span> <span class="c1">#indicates the size of samples to be drawn</span>
|
||||
<span class="n">n_prevpoints</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> <span class="c1"># how many prevalence points will be extracted from the interval [0, 1] for each category</span>
|
||||
<span class="n">n_repetitions</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="c1"># number of times each prevalence will be used to generate a test sample</span>
|
||||
<span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="c1"># indicates the number of parallel workers (-1 indicates, as in sklearn, all CPUs)</span>
|
||||
<span class="n">random_seed</span><span class="o">=</span><span class="mi">42</span><span class="p">,</span> <span class="c1"># setting a random seed allows to replicate the test samples across runs</span>
|
||||
<span class="n">error_metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">'mae'</span><span class="p">,</span> <span class="s1">'mrae'</span><span class="p">,</span> <span class="s1">'mkld'</span><span class="p">],</span> <span class="c1"># specify the evaluation metrics</span>
|
||||
<span class="n">verbose</span><span class="o">=</span><span class="kc">True</span> <span class="c1"># set to True to show some standard-line outputs</span>
|
||||
<span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>The resulting report is a pandas’ dataframe that can be directly printed.
|
||||
Here, we set some display options from pandas just to make the output clearer;
|
||||
note also that the estimated prevalences are shown as strings using the
|
||||
function strprev function that simply converts a prevalence into a
|
||||
string representing it, with a fixed decimal precision (default 3):</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
|
||||
<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s1">'display.expand_frame_repr'</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
|
||||
<span class="n">pd</span><span class="o">.</span><span class="n">set_option</span><span class="p">(</span><span class="s2">"precision"</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
|
||||
<span class="n">df</span><span class="p">[</span><span class="s1">'estim-prev'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'estim-prev'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">strprev</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>The output should look like:</p>
|
||||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="n">true</span><span class="o">-</span><span class="n">prev</span> <span class="n">estim</span><span class="o">-</span><span class="n">prev</span> <span class="n">mae</span> <span class="n">mrae</span> <span class="n">mkld</span>
|
||||
<span class="mi">0</span> <span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">]</span> <span class="p">[</span><span class="mf">0.000</span><span class="p">,</span> <span class="mf">1.000</span><span class="p">]</span> <span class="mf">0.000</span> <span class="mf">0.000</span> <span class="mf">0.000e+00</span>
|
||||
<span class="mi">1</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">]</span> <span class="p">[</span><span class="mf">0.091</span><span class="p">,</span> <span class="mf">0.909</span><span class="p">]</span> <span class="mf">0.009</span> <span class="mf">0.048</span> <span class="mf">4.426e-04</span>
|
||||
<span class="mi">2</span> <span class="p">[</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">]</span> <span class="p">[</span><span class="mf">0.163</span><span class="p">,</span> <span class="mf">0.837</span><span class="p">]</span> <span class="mf">0.037</span> <span class="mf">0.114</span> <span class="mf">4.633e-03</span>
|
||||
<span class="mi">3</span> <span class="p">[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">]</span> <span class="p">[</span><span class="mf">0.283</span><span class="p">,</span> <span class="mf">0.717</span><span class="p">]</span> <span class="mf">0.017</span> <span class="mf">0.041</span> <span class="mf">7.383e-04</span>
|
||||
<span class="mi">4</span> <span class="p">[</span><span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">]</span> <span class="p">[</span><span class="mf">0.366</span><span class="p">,</span> <span class="mf">0.634</span><span class="p">]</span> <span class="mf">0.034</span> <span class="mf">0.070</span> <span class="mf">2.412e-03</span>
|
||||
<span class="mi">5</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">]</span> <span class="p">[</span><span class="mf">0.459</span><span class="p">,</span> <span class="mf">0.541</span><span class="p">]</span> <span class="mf">0.041</span> <span class="mf">0.082</span> <span class="mf">3.387e-03</span>
|
||||
<span class="mi">6</span> <span class="p">[</span><span class="mf">0.6</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">]</span> <span class="p">[</span><span class="mf">0.565</span><span class="p">,</span> <span class="mf">0.435</span><span class="p">]</span> <span class="mf">0.035</span> <span class="mf">0.073</span> <span class="mf">2.535e-03</span>
|
||||
<span class="mi">7</span> <span class="p">[</span><span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">]</span> <span class="p">[</span><span class="mf">0.654</span><span class="p">,</span> <span class="mf">0.346</span><span class="p">]</span> <span class="mf">0.046</span> <span class="mf">0.108</span> <span class="mf">4.701e-03</span>
|
||||
<span class="mi">8</span> <span class="p">[</span><span class="mf">0.8</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">]</span> <span class="p">[</span><span class="mf">0.725</span><span class="p">,</span> <span class="mf">0.275</span><span class="p">]</span> <span class="mf">0.075</span> <span class="mf">0.235</span> <span class="mf">1.515e-02</span>
|
||||
<span class="mi">9</span> <span class="p">[</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]</span> <span class="p">[</span><span class="mf">0.858</span><span class="p">,</span> <span class="mf">0.142</span><span class="p">]</span> <span class="mf">0.042</span> <span class="mf">0.229</span> <span class="mf">7.740e-03</span>
|
||||
<span class="mi">10</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">]</span> <span class="p">[</span><span class="mf">0.945</span><span class="p">,</span> <span class="mf">0.055</span><span class="p">]</span> <span class="mf">0.055</span> <span class="mf">27.357</span> <span class="mf">5.219e-02</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>One can get the averaged scores using standard pandas’
|
||||
functions, i.e.:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>will produce the following output:</p>
|
||||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">true</span><span class="o">-</span><span class="n">prev</span> <span class="mf">0.500</span>
|
||||
<span class="n">mae</span> <span class="mf">0.035</span>
|
||||
<span class="n">mrae</span> <span class="mf">2.578</span>
|
||||
<span class="n">mkld</span> <span class="mf">0.009</span>
|
||||
<span class="n">dtype</span><span class="p">:</span> <span class="n">float64</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>Other evaluation functions include:</p>
|
||||
<ul class="simple">
|
||||
<li><p><em>artificial_sampling_eval</em>: that computes the evaluation for a
|
||||
given evaluation metric, returning the average instead of a dataframe.</p></li>
|
||||
<li><p><em>artificial_sampling_prediction</em>: that returns two np.arrays containing the
|
||||
true prevalences and the estimated prevalences.</p></li>
|
||||
</ul>
|
||||
<p>See the documentation for further details.</p>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
|
||||
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|
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|
||||
<li><a class="reference internal" href="#error-measures">Error Measures</a></li>
|
||||
<li><a class="reference internal" href="#evaluation-protocols">Evaluation Protocols</a></li>
|
||||
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|
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|
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<div class="section" id="installation">
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||||
<h1>Installation<a class="headerlink" href="#installation" title="Permalink to this headline">¶</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>
|
||||
<div class="section" id="requirements">
|
||||
<h2>Requirements<a class="headerlink" href="#requirements" title="Permalink to this headline">¶</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>
|
||||
</div>
|
||||
<div class="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 headline">¶</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>
|
||||
</div>
|
||||
</div>
|
||||
|
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|
||||
<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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<li class="nav-item nav-item-this"><a href="">Quantification Methods</a></li>
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<div class="tex2jax_ignore mathjax_ignore section" id="quantification-methods">
|
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<h1>Quantification Methods<a class="headerlink" href="#quantification-methods" title="Permalink to this headline">¶</a></h1>
|
||||
<p>Quantification methods can be categorized as belonging to
|
||||
<em>aggregative</em> and <em>non-aggregative</em> groups.
|
||||
Most methods included in QuaPy at the moment are of type <em>aggregative</em>
|
||||
(though we plan to add many more methods in the near future), i.e.,
|
||||
are methods characterized by the fact that
|
||||
quantification is performed as an aggregation function of the individual
|
||||
products of classification.</p>
|
||||
<p>Any quantifier in QuaPy shoud extend the class <em>BaseQuantifier</em>,
|
||||
and implement some abstract methods:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span> <span class="nd">@abstractmethod</span>
|
||||
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span> <span class="o">...</span>
|
||||
|
||||
<span class="nd">@abstractmethod</span>
|
||||
<span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span> <span class="o">...</span>
|
||||
|
||||
<span class="nd">@abstractmethod</span>
|
||||
<span class="k">def</span> <span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">):</span> <span class="o">...</span>
|
||||
|
||||
<span class="nd">@abstractmethod</span>
|
||||
<span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> <span class="o">...</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>The meaning of those functions should be familiar to those
|
||||
used to work with scikit-learn since the class structure of QuaPy
|
||||
is directly inspired by scikit-learn’s <em>Estimators</em>. Functions
|
||||
<em>fit</em> and <em>quantify</em> are used to train the model and to provide
|
||||
class estimations (the reason why
|
||||
scikit-learn’ structure has not been adopted <em>as is</em> in QuaPy responds to
|
||||
the fact that scikit-learn’s <em>predict</em> function is expected to return
|
||||
one output for each input element –e.g., a predicted label for each
|
||||
instance in a sample– while in quantification the output for a sample
|
||||
is one single array of class prevalences), while functions <em>set_params</em>
|
||||
and <em>get_params</em> allow a
|
||||
<a class="reference external" href="https://github.com/HLT-ISTI/QuaPy/wiki/Model-Selection">model selector</a>
|
||||
to automate the process of hyperparameter search.</p>
|
||||
<div class="section" id="aggregative-methods">
|
||||
<h2>Aggregative Methods<a class="headerlink" href="#aggregative-methods" title="Permalink to this headline">¶</a></h2>
|
||||
<p>All quantification methods are implemented as part of the
|
||||
<em>qp.method</em> package. In particular, <em>aggregative</em> methods are defined in
|
||||
<em>qp.method.aggregative</em>, and extend <em>AggregativeQuantifier(BaseQuantifier)</em>.
|
||||
The methods that any <em>aggregative</em> quantifier must implement are:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span> <span class="nd">@abstractmethod</span>
|
||||
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">fit_learner</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> <span class="o">...</span>
|
||||
|
||||
<span class="nd">@abstractmethod</span>
|
||||
<span class="k">def</span> <span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span> <span class="o">...</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>since, as mentioned before, aggregative methods base their prediction on the
|
||||
individual predictions of a classifier. Indeed, a default implementation
|
||||
of <em>BaseQuantifier.quantify</em> is already provided, which looks like:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span> <span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
|
||||
<span class="n">classif_predictions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">preclassify</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">aggregate</span><span class="p">(</span><span class="n">classif_predictions</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>Aggregative quantifiers are expected to maintain a classifier (which is
|
||||
accessed through the <em>@property</em> <em>learner</em>). This classifier is
|
||||
given as input to the quantifier, and can be already fit
|
||||
on external data (in which case, the <em>fit_learner</em> argument should
|
||||
be set to False), or be fit by the quantifier’s fit (default).</p>
|
||||
<p>Another class of <em>aggregative</em> methods are the <em>probabilistic</em>
|
||||
aggregative methods, that should inherit from the abstract class
|
||||
<em>AggregativeProbabilisticQuantifier(AggregativeQuantifier)</em>.
|
||||
The particularity of <em>probabilistic</em> aggregative methods (w.r.t.
|
||||
non-probabilistic ones), is that the default quantifier is defined
|
||||
in terms of the posterior probabilities returned by a probabilistic
|
||||
classifier, and not by the crisp decisions of a hard classifier; i.e.:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span> <span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
|
||||
<span class="n">classif_posteriors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">posterior_probabilities</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">aggregate</span><span class="p">(</span><span class="n">classif_posteriors</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>One advantage of <em>aggregative</em> methods (either probabilistic or not)
|
||||
is that the evaluation according to any sampling procedure (e.g.,
|
||||
the <a class="reference external" href="https://github.com/HLT-ISTI/QuaPy/wiki/Evaluation">artificial sampling protocol</a>)
|
||||
can be achieved very efficiently, since the entire set can be pre-classified
|
||||
once, and the quantification estimations for different samples can directly
|
||||
reuse these predictions, without requiring to classify each element every time.
|
||||
QuaPy leverages this property to speed-up any procedure having to do with
|
||||
quantification over samples, as is customarily done in model selection or
|
||||
in evaluation.</p>
|
||||
<div class="section" id="the-classify-count-variants">
|
||||
<h3>The Classify & Count variants<a class="headerlink" href="#the-classify-count-variants" title="Permalink to this headline">¶</a></h3>
|
||||
<p>QuaPy implements the four CC variants, i.e.:</p>
|
||||
<ul class="simple">
|
||||
<li><p><em>CC</em> (Classify & Count), the simplest aggregative quantifier; one that
|
||||
simply relies on the label predictions of a classifier to deliver class estimates.</p></li>
|
||||
<li><p><em>ACC</em> (Adjusted Classify & Count), the adjusted variant of CC.</p></li>
|
||||
<li><p><em>PCC</em> (Probabilistic Classify & Count), the probabilistic variant of CC that
|
||||
relies on the soft estimations (or posterior probabilities) returned by a (probabilistic) classifier.</p></li>
|
||||
<li><p><em>PACC</em> (Probabilistic Adjusted Classify & Count), the adjusted variant of PCC.</p></li>
|
||||
</ul>
|
||||
<p>The following code serves as a complete example using CC equipped
|
||||
with a SVM as the classifier:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<span class="kn">import</span> <span class="nn">quapy.functional</span> <span class="k">as</span> <span class="nn">F</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">LinearSVC</span>
|
||||
|
||||
<span class="n">dataset</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_twitter</span><span class="p">(</span><span class="s1">'hcr'</span><span class="p">,</span> <span class="n">pickle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">training</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">training</span>
|
||||
<span class="n">test</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span>
|
||||
|
||||
<span class="c1"># instantiate a classifier learner, in this case a SVM</span>
|
||||
<span class="n">svm</span> <span class="o">=</span> <span class="n">LinearSVC</span><span class="p">()</span>
|
||||
|
||||
<span class="c1"># instantiate a Classify & Count with the SVM</span>
|
||||
<span class="c1"># (an alias is available in qp.method.aggregative.ClassifyAndCount)</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">method</span><span class="o">.</span><span class="n">aggregative</span><span class="o">.</span><span class="n">CC</span><span class="p">(</span><span class="n">svm</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">training</span><span class="p">)</span>
|
||||
<span class="n">estim_prevalence</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>The same code could be used to instantiate an ACC, by simply replacing
|
||||
the instantiation of the model with:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">method</span><span class="o">.</span><span class="n">aggregative</span><span class="o">.</span><span class="n">ACC</span><span class="p">(</span><span class="n">svm</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>Note that the adjusted variants (ACC and PACC) need to estimate
|
||||
some parameters for performing the adjustment (e.g., the
|
||||
<em>true positive rate</em> and the <em>false positive rate</em> in case of
|
||||
binary classification) that are estimated on a validation split
|
||||
of the labelled set. In this case, the <strong>init</strong> method of
|
||||
ACC defines an additional parameter, <em>val_split</em> which, by
|
||||
default, is set to 0.4 and so, the 40% of the labelled data
|
||||
will be used for estimating the parameters for adjusting the
|
||||
predictions. This parameters can also be set with an integer,
|
||||
indicating that the parameters should be estimated by means of
|
||||
<em>k</em>-fold cross-validation, for which the integer indicates the
|
||||
number <em>k</em> of folds. Finally, <em>val_split</em> can be set to a
|
||||
specific held-out validation set (i.e., an instance of <em>LabelledCollection</em>).</p>
|
||||
<p>The specification of <em>val_split</em> can be
|
||||
postponed to the invokation of the fit method (if <em>val_split</em> was also
|
||||
set in the constructor, the one specified at fit time would prevail),
|
||||
e.g.:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">method</span><span class="o">.</span><span class="n">aggregative</span><span class="o">.</span><span class="n">ACC</span><span class="p">(</span><span class="n">svm</span><span class="p">)</span>
|
||||
<span class="c1"># perform 5-fold cross validation for estimating ACC's parameters</span>
|
||||
<span class="c1"># (overrides the default val_split=0.4 in the constructor)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>The following code illustrates the case in which PCC is used:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">method</span><span class="o">.</span><span class="n">aggregative</span><span class="o">.</span><span class="n">PCC</span><span class="p">(</span><span class="n">svm</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">training</span><span class="p">)</span>
|
||||
<span class="n">estim_prevalence</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'classifier:'</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">learner</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>In this case, QuaPy will print:</p>
|
||||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">The</span> <span class="n">learner</span> <span class="n">LinearSVC</span> <span class="n">does</span> <span class="ow">not</span> <span class="n">seem</span> <span class="n">to</span> <span class="n">be</span> <span class="n">probabilistic</span><span class="o">.</span> <span class="n">The</span> <span class="n">learner</span> <span class="n">will</span> <span class="n">be</span> <span class="n">calibrated</span><span class="o">.</span>
|
||||
<span class="n">classifier</span><span class="p">:</span> <span class="n">CalibratedClassifierCV</span><span class="p">(</span><span class="n">base_estimator</span><span class="o">=</span><span class="n">LinearSVC</span><span class="p">(),</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>The first output indicates that the learner (<em>LinearSVC</em> in this case)
|
||||
is not a probabilistic classifier (i.e., it does not implement the
|
||||
<em>predict_proba</em> method) and so, the classifier will be converted to
|
||||
a probabilistic one through <a class="reference external" href="https://scikit-learn.org/stable/modules/calibration.html">calibration</a>.
|
||||
As a result, the classifier that is printed in the second line points
|
||||
to a <em>CalibratedClassifier</em> instance. Note that calibration can only
|
||||
be applied to hard classifiers when <em>fit_learner=True</em>; an exception
|
||||
will be raised otherwise.</p>
|
||||
<p>Lastly, everything we said aboud ACC and PCC
|
||||
applies to PACC as well.</p>
|
||||
</div>
|
||||
<div class="section" id="expectation-maximization-emq">
|
||||
<h3>Expectation Maximization (EMQ)<a class="headerlink" href="#expectation-maximization-emq" title="Permalink to this headline">¶</a></h3>
|
||||
<p>The Expectation Maximization Quantifier (EMQ), also known as
|
||||
the SLD, is available at <em>qp.method.aggregative.EMQ</em> or via the
|
||||
alias <em>qp.method.aggregative.ExpectationMaximizationQuantifier</em>.
|
||||
The method is described in:</p>
|
||||
<p><em>Saerens, M., Latinne, P., and Decaestecker, C. (2002). Adjusting the outputs of a classifier
|
||||
to new a priori probabilities: A simple procedure. Neural Computation, 14(1):21–41.</em></p>
|
||||
<p>EMQ works with a probabilistic classifier (if the classifier
|
||||
given as input is a hard one, a calibration will be attempted).
|
||||
Although this method was originally proposed for improving the
|
||||
posterior probabilities of a probabilistic classifier, and not
|
||||
for improving the estimation of prior probabilities, EMQ ranks
|
||||
almost always among the most effective quantifiers in the
|
||||
experiments we have carried out.</p>
|
||||
<p>An example of use can be found below:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
|
||||
|
||||
<span class="n">dataset</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_twitter</span><span class="p">(</span><span class="s1">'hcr'</span><span class="p">,</span> <span class="n">pickle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">method</span><span class="o">.</span><span class="n">aggregative</span><span class="o">.</span><span class="n">EMQ</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">())</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="p">)</span>
|
||||
<span class="n">estim_prevalence</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="section" id="hellinger-distance-y-hdy">
|
||||
<h3>Hellinger Distance y (HDy)<a class="headerlink" href="#hellinger-distance-y-hdy" title="Permalink to this headline">¶</a></h3>
|
||||
<p>The method HDy is described in:</p>
|
||||
<p><em>Implementation of the method based on the Hellinger Distance y (HDy) proposed by
|
||||
González-Castro, V., Alaiz-Rodrı́guez, R., and Alegre, E. (2013). Class distribution
|
||||
estimation based on the Hellinger distance. Information Sciences, 218:146–164.</em></p>
|
||||
<p>It is implemented in <em>qp.method.aggregative.HDy</em> (also accessible
|
||||
through the allias <em>qp.method.aggregative.HellingerDistanceY</em>).
|
||||
This method works with a probabilistic classifier (hard classifiers
|
||||
can be used as well and will be calibrated) and requires a validation
|
||||
set to estimate parameter for the mixture model. Just like
|
||||
ACC and PACC, this quantifier receives a <em>val_split</em> argument
|
||||
in the constructor (or in the fit method, in which case the previous
|
||||
value is overridden) that can either be a float indicating the proportion
|
||||
of training data to be taken as the validation set (in a random
|
||||
stratified split), or a validation set (i.e., an instance of
|
||||
<em>LabelledCollection</em>) itself.</p>
|
||||
<p>HDy was proposed as a binary classifier and the implementation
|
||||
provided in QuaPy accepts only binary datasets.</p>
|
||||
<p>The following code shows an example of use:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
|
||||
|
||||
<span class="c1"># load a binary dataset</span>
|
||||
<span class="n">dataset</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="s1">'hp'</span><span class="p">,</span> <span class="n">pickle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">preprocessing</span><span class="o">.</span><span class="n">text2tfidf</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">method</span><span class="o">.</span><span class="n">aggregative</span><span class="o">.</span><span class="n">HDy</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">())</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="p">)</span>
|
||||
<span class="n">estim_prevalence</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="section" id="explicit-loss-minimization">
|
||||
<h3>Explicit Loss Minimization<a class="headerlink" href="#explicit-loss-minimization" title="Permalink to this headline">¶</a></h3>
|
||||
<p>The Explicit Loss Minimization (ELM) represent a family of methods
|
||||
based on structured output learning, i.e., quantifiers relying on
|
||||
classifiers that have been optimized targeting a
|
||||
quantification-oriented evaluation measure.</p>
|
||||
<p>In QuaPy, the following methods, all relying on Joachim’s
|
||||
<a class="reference external" href="https://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html">SVMperf</a>
|
||||
implementation, are available in <em>qp.method.aggregative</em>:</p>
|
||||
<ul class="simple">
|
||||
<li><p>SVMQ (SVM-Q) is a quantification method optimizing the metric <em>Q</em> defined
|
||||
in <em>Barranquero, J., Díez, J., and del Coz, J. J. (2015). Quantification-oriented learning based
|
||||
on reliable classifiers. Pattern Recognition, 48(2):591–604.</em></p></li>
|
||||
<li><p>SVMKLD (SVM for Kullback-Leibler Divergence) proposed in <em>Esuli, A. and Sebastiani, F. (2015).
|
||||
Optimizing text quantifiers for multivariate loss functions.
|
||||
ACM Transactions on Knowledge Discovery and Data, 9(4):Article 27.</em></p></li>
|
||||
<li><p>SVMNKLD (SVM for Normalized Kullback-Leibler Divergence) proposed in <em>Esuli, A. and Sebastiani, F. (2015).
|
||||
Optimizing text quantifiers for multivariate loss functions.
|
||||
ACM Transactions on Knowledge Discovery and Data, 9(4):Article 27.</em></p></li>
|
||||
<li><p>SVMAE (SVM for Mean Absolute Error)</p></li>
|
||||
<li><p>SVMRAE (SVM for Mean Relative Absolute Error)</p></li>
|
||||
</ul>
|
||||
<p>the last two methods (SVMAE and SVMRAE) have been implemented in
|
||||
QuaPy in order to make available ELM variants for what nowadays
|
||||
are considered the most well-behaved evaluation metrics in quantification.</p>
|
||||
<p>In order to make these models work, you would need to run the script
|
||||
<em>prepare_svmperf.sh</em> (distributed along with QuaPy) that
|
||||
downloads <em>SVMperf</em>’ source code, applies a patch that
|
||||
implements the quantification oriented losses, and compiles the
|
||||
sources.</p>
|
||||
<p>If you want to add any custom loss, you would need to modify
|
||||
the source code of <em>SVMperf</em> in order to implement it, and
|
||||
assign a valid loss code to it. Then you must re-compile
|
||||
the whole thing and instantiate the quantifier in QuaPy
|
||||
as follows:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># you can either set the path to your custom svm_perf_quantification implementation</span>
|
||||
<span class="c1"># in the environment variable, or as an argument to the constructor of ELM</span>
|
||||
<span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'SVMPERF_HOME'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'./path/to/svm_perf_quantification'</span>
|
||||
|
||||
<span class="c1"># assign an alias to your custom loss and the id you have assigned to it</span>
|
||||
<span class="n">svmperf</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">classification</span><span class="o">.</span><span class="n">svmperf</span><span class="o">.</span><span class="n">SVMperf</span>
|
||||
<span class="n">svmperf</span><span class="o">.</span><span class="n">valid_losses</span><span class="p">[</span><span class="s1">'mycustomloss'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">28</span>
|
||||
|
||||
<span class="c1"># instantiate the ELM method indicating the loss</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">method</span><span class="o">.</span><span class="n">aggregative</span><span class="o">.</span><span class="n">ELM</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">'mycustomloss'</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>All ELM are binary quantifiers since they rely on <em>SVMperf</em>, that
|
||||
currently supports only binary classification.
|
||||
ELM variants (any binary quantifier in general) can be extended
|
||||
to operate in single-label scenarios trivially by adopting a
|
||||
“one-vs-all” strategy (as, e.g., in
|
||||
<em>Gao, W. and Sebastiani, F. (2016). From classification to quantification in tweet sentiment
|
||||
analysis. Social Network Analysis and Mining, 6(19):1–22</em>).
|
||||
In QuaPy this is possible by using the <em>OneVsAll</em> class:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="n">SVMQ</span><span class="p">,</span> <span class="n">OneVsAll</span>
|
||||
|
||||
<span class="c1"># load a single-label dataset (this one contains 3 classes)</span>
|
||||
<span class="n">dataset</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_twitter</span><span class="p">(</span><span class="s1">'hcr'</span><span class="p">,</span> <span class="n">pickle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># let qp know where svmperf is</span>
|
||||
<span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'SVMPERF_HOME'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'../svm_perf_quantification'</span>
|
||||
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">OneVsAll</span><span class="p">(</span><span class="n">SVMQ</span><span class="p">(),</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># run them on parallel</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="p">)</span>
|
||||
<span class="n">estim_prevalence</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="section" id="meta-models">
|
||||
<h2>Meta Models<a class="headerlink" href="#meta-models" title="Permalink to this headline">¶</a></h2>
|
||||
<p>By <em>meta</em> models we mean quantification methods that are defined on top of other
|
||||
quantification methods, and that thus do not squarely belong to the aggregative nor
|
||||
the non-aggregative group (indeed, <em>meta</em> models could use quantifiers from any of those
|
||||
groups).
|
||||
<em>Meta</em> models are implemented in the <em>qp.method.meta</em> module.</p>
|
||||
<div class="section" id="ensembles">
|
||||
<h3>Ensembles<a class="headerlink" href="#ensembles" title="Permalink to this headline">¶</a></h3>
|
||||
<p>QuaPy implements (some of) the variants proposed in:</p>
|
||||
<ul class="simple">
|
||||
<li><p><em>Pérez-Gállego, P., Quevedo, J. R., & del Coz, J. J. (2017).
|
||||
Using ensembles for problems with characterizable changes in data distribution: A case study on quantification.
|
||||
Information Fusion, 34, 87-100.</em></p></li>
|
||||
<li><p><em>Pérez-Gállego, P., Castano, A., Quevedo, J. R., & del Coz, J. J. (2019).
|
||||
Dynamic ensemble selection for quantification tasks.
|
||||
Information Fusion, 45, 1-15.</em></p></li>
|
||||
</ul>
|
||||
<p>The following code shows how to instantiate an Ensemble of 30 <em>Adjusted Classify & Count</em> (ACC)
|
||||
quantifiers operating with a <em>Logistic Regressor</em> (LR) as the base classifier, and using the
|
||||
<em>average</em> as the aggregation policy (see the original article for further details).
|
||||
The last parameter indicates to use all processors for parallelization.</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="n">ACC</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.method.meta</span> <span class="kn">import</span> <span class="n">Ensemble</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
|
||||
|
||||
<span class="n">dataset</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_UCIDataset</span><span class="p">(</span><span class="s1">'haberman'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">Ensemble</span><span class="p">(</span><span class="n">quantifier</span><span class="o">=</span><span class="n">ACC</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">()),</span> <span class="n">size</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">policy</span><span class="o">=</span><span class="s1">'ave'</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="p">)</span>
|
||||
<span class="n">estim_prevalence</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>Other aggregation policies implemented in QuaPy include:</p>
|
||||
<ul class="simple">
|
||||
<li><p>‘ptr’ for applying a dynamic selection based on the training prevalence of the ensemble’s members</p></li>
|
||||
<li><p>‘ds’ for applying a dynamic selection based on the Hellinger Distance</p></li>
|
||||
<li><p><em>any valid quantification measure</em> (e.g., ‘mse’) for performing a static selection based on
|
||||
the performance estimated for each member of the ensemble in terms of that evaluation metric.</p></li>
|
||||
</ul>
|
||||
<p>When using any of the above options, it is important to set the <em>red_size</em> parameter, which
|
||||
informs of the number of members to retain.</p>
|
||||
<p>Please, check the <a class="reference external" href="https://github.com/HLT-ISTI/QuaPy/wiki/Model-Selection">model selection</a>
|
||||
wiki if you want to optimize the hyperparameters of ensemble for classification or quantification.</p>
|
||||
</div>
|
||||
<div class="section" id="the-quanet-neural-network">
|
||||
<h3>The QuaNet neural network<a class="headerlink" href="#the-quanet-neural-network" title="Permalink to this headline">¶</a></h3>
|
||||
<p>QuaPy offers an implementation of QuaNet, a deep learning model presented in:</p>
|
||||
<p><em>Esuli, A., Moreo, A., & Sebastiani, F. (2018, October).
|
||||
A recurrent neural network for sentiment quantification.
|
||||
In Proceedings of the 27th ACM International Conference on
|
||||
Information and Knowledge Management (pp. 1775-1778).</em></p>
|
||||
<p>This model requires <em>torch</em> to be installed.
|
||||
QuaNet also requires a classifier that can provide embedded representations
|
||||
of the inputs.
|
||||
In the original paper, QuaNet was tested using an LSTM as the base classifier.
|
||||
In the following example, we show an instantiation of QuaNet that instead uses CNN as a probabilistic classifier, taking its last layer representation as the document embedding:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.method.meta</span> <span class="kn">import</span> <span class="n">QuaNet</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.classification.neural</span> <span class="kn">import</span> <span class="n">NeuralClassifierTrainer</span><span class="p">,</span> <span class="n">CNNnet</span>
|
||||
|
||||
<span class="c1"># use samples of 100 elements</span>
|
||||
<span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'SAMPLE_SIZE'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">100</span>
|
||||
|
||||
<span class="c1"># load the kindle dataset as text, and convert words to numerical indexes</span>
|
||||
<span class="n">dataset</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="s1">'kindle'</span><span class="p">,</span> <span class="n">pickle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">preprocessing</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># the text classifier is a CNN trained by NeuralClassifierTrainer</span>
|
||||
<span class="n">cnn</span> <span class="o">=</span> <span class="n">CNNnet</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">vocabulary_size</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">n_classes</span><span class="p">)</span>
|
||||
<span class="n">learner</span> <span class="o">=</span> <span class="n">NeuralClassifierTrainer</span><span class="p">(</span><span class="n">cnn</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># train QuaNet</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">QuaNet</span><span class="p">(</span><span class="n">learner</span><span class="p">,</span> <span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'SAMPLE_SIZE'</span><span class="p">],</span> <span class="n">device</span><span class="o">=</span><span class="s1">'cuda'</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="p">)</span>
|
||||
<span class="n">estim_prevalence</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
|
||||
<div class="clearer"></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="sphinxsidebar" role="navigation" aria-label="main navigation">
|
||||
<div class="sphinxsidebarwrapper">
|
||||
<h3><a href="index.html">Table of Contents</a></h3>
|
||||
<ul>
|
||||
<li><a class="reference internal" href="#">Quantification Methods</a><ul>
|
||||
<li><a class="reference internal" href="#aggregative-methods">Aggregative Methods</a><ul>
|
||||
<li><a class="reference internal" href="#the-classify-count-variants">The Classify & Count variants</a></li>
|
||||
<li><a class="reference internal" href="#expectation-maximization-emq">Expectation Maximization (EMQ)</a></li>
|
||||
<li><a class="reference internal" href="#hellinger-distance-y-hdy">Hellinger Distance y (HDy)</a></li>
|
||||
<li><a class="reference internal" href="#explicit-loss-minimization">Explicit Loss Minimization</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><a class="reference internal" href="#meta-models">Meta Models</a><ul>
|
||||
<li><a class="reference internal" href="#ensembles">Ensembles</a></li>
|
||||
<li><a class="reference internal" href="#the-quanet-neural-network">The QuaNet neural network</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
|
||||
<h4>Previous topic</h4>
|
||||
<p class="topless"><a href="Evaluation.html"
|
||||
title="previous chapter">Evaluation</a></p>
|
||||
<h4>Next topic</h4>
|
||||
<p class="topless"><a href="Plotting.html"
|
||||
title="next chapter">Plotting</a></p>
|
||||
<div role="note" aria-label="source link">
|
||||
<h3>This Page</h3>
|
||||
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<div class="tex2jax_ignore mathjax_ignore section" id="model-selection">
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<h1>Model Selection<a class="headerlink" href="#model-selection" title="Permalink to this headline">¶</a></h1>
|
||||
<p>As a supervised machine learning task, quantification methods
|
||||
can strongly depend on a good choice of model hyper-parameters.
|
||||
The process whereby those hyper-parameters are chosen is
|
||||
typically known as <em>Model Selection</em>, and typically consists of
|
||||
testing different settings and picking the one that performed
|
||||
best in a held-out validation set in terms of any given
|
||||
evaluation measure.</p>
|
||||
<div class="section" id="targeting-a-quantification-oriented-loss">
|
||||
<h2>Targeting a Quantification-oriented loss<a class="headerlink" href="#targeting-a-quantification-oriented-loss" title="Permalink to this headline">¶</a></h2>
|
||||
<p>The task being optimized determines the evaluation protocol,
|
||||
i.e., the criteria according to which the performance of
|
||||
any given method for solving is to be assessed.
|
||||
As a task on its own right, quantification should impose
|
||||
its own model selection strategies, i.e., strategies
|
||||
aimed at finding appropriate configurations
|
||||
specifically designed for the task of quantification.</p>
|
||||
<p>Quantification has long been regarded as an add-on of
|
||||
classification, and thus the model selection strategies
|
||||
customarily adopted in classification have simply been
|
||||
applied to quantification (see the next section).
|
||||
It has been argued in <em>Moreo, Alejandro, and Fabrizio Sebastiani.
|
||||
“Re-Assessing the” Classify and Count” Quantification Method.”
|
||||
arXiv preprint arXiv:2011.02552 (2020).</em>
|
||||
that specific model selection strategies should
|
||||
be adopted for quantification. That is, model selection
|
||||
strategies for quantification should target
|
||||
quantification-oriented losses and be tested in a variety
|
||||
of scenarios exhibiting different degrees of prior
|
||||
probability shift.</p>
|
||||
<p>The class
|
||||
<em>qp.model_selection.GridSearchQ</em>
|
||||
implements a grid-search exploration over the space of
|
||||
hyper-parameter combinations that evaluates each<br />
|
||||
combination of hyper-parameters
|
||||
by means of a given quantification-oriented
|
||||
error metric (e.g., any of the error functions implemented
|
||||
in <em>qp.error</em>) and according to the
|
||||
<a class="reference external" href="https://github.com/HLT-ISTI/QuaPy/wiki/Evaluation"><em>artificial sampling protocol</em></a>.</p>
|
||||
<p>The following is an example of model selection for quantification:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="n">PCC</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
|
||||
<span class="c1"># set a seed to replicate runs</span>
|
||||
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'SAMPLE_SIZE'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">500</span>
|
||||
|
||||
<span class="n">dataset</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="s1">'hp'</span><span class="p">,</span> <span class="n">tfidf</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># The model will be returned by the fit method of GridSearchQ.</span>
|
||||
<span class="c1"># Model selection will be performed with a fixed budget of 1000 evaluations</span>
|
||||
<span class="c1"># for each hyper-parameter combination. The error to optimize is the MAE for</span>
|
||||
<span class="c1"># quantification, as evaluated on artificially drawn samples at prevalences </span>
|
||||
<span class="c1"># covering the entire spectrum on a held-out portion (40%) of the training set.</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">model_selection</span><span class="o">.</span><span class="n">GridSearchQ</span><span class="p">(</span>
|
||||
<span class="n">model</span><span class="o">=</span><span class="n">PCC</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">()),</span>
|
||||
<span class="n">param_grid</span><span class="o">=</span><span class="p">{</span><span class="s1">'C'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">10</span><span class="p">),</span> <span class="s1">'class_weight'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'balanced'</span><span class="p">,</span> <span class="kc">None</span><span class="p">]},</span>
|
||||
<span class="n">sample_size</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'SAMPLE_SIZE'</span><span class="p">],</span>
|
||||
<span class="n">eval_budget</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
|
||||
<span class="n">error</span><span class="o">=</span><span class="s1">'mae'</span><span class="p">,</span>
|
||||
<span class="n">refit</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="c1"># retrain on the whole labelled set</span>
|
||||
<span class="n">val_split</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span>
|
||||
<span class="n">verbose</span><span class="o">=</span><span class="kc">True</span> <span class="c1"># show information as the process goes on</span>
|
||||
<span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="p">)</span>
|
||||
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'model selection ended: best hyper-parameters=</span><span class="si">{</span><span class="n">model</span><span class="o">.</span><span class="n">best_params_</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">best_model_</span>
|
||||
|
||||
<span class="c1"># evaluation in terms of MAE</span>
|
||||
<span class="n">results</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">evaluation</span><span class="o">.</span><span class="n">artificial_sampling_eval</span><span class="p">(</span>
|
||||
<span class="n">model</span><span class="p">,</span>
|
||||
<span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="p">,</span>
|
||||
<span class="n">sample_size</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'SAMPLE_SIZE'</span><span class="p">],</span>
|
||||
<span class="n">n_prevpoints</span><span class="o">=</span><span class="mi">101</span><span class="p">,</span>
|
||||
<span class="n">n_repetitions</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
|
||||
<span class="n">error_metric</span><span class="o">=</span><span class="s1">'mae'</span>
|
||||
<span class="p">)</span>
|
||||
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'MAE=</span><span class="si">{</span><span class="n">results</span><span class="si">:</span><span class="s1">.5f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>In this example, the system outputs:</p>
|
||||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>[GridSearchQ]: starting optimization with n_jobs=1
|
||||
[GridSearchQ]: checking hyperparams={'C': 0.0001, 'class_weight': 'balanced'} got mae score 0.24987
|
||||
[GridSearchQ]: checking hyperparams={'C': 0.0001, 'class_weight': None} got mae score 0.48135
|
||||
[GridSearchQ]: checking hyperparams={'C': 0.001, 'class_weight': 'balanced'} got mae score 0.24866
|
||||
[...]
|
||||
[GridSearchQ]: checking hyperparams={'C': 100000.0, 'class_weight': None} got mae score 0.43676
|
||||
[GridSearchQ]: optimization finished: best params {'C': 0.1, 'class_weight': 'balanced'} (score=0.19982)
|
||||
[GridSearchQ]: refitting on the whole development set
|
||||
model selection ended: best hyper-parameters={'C': 0.1, 'class_weight': 'balanced'}
|
||||
1010 evaluations will be performed for each combination of hyper-parameters
|
||||
[artificial sampling protocol] generating predictions: 100%|██████████| 1010/1010 [00:00<00:00, 5005.54it/s]
|
||||
MAE=0.20342
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>The parameter <em>val_split</em> can alternatively be used to indicate
|
||||
a validation set (i.e., an instance of <em>LabelledCollection</em>) instead
|
||||
of a proportion. This could be useful if one wants to have control
|
||||
on the specific data split to be used across different model selection
|
||||
experiments.</p>
|
||||
</div>
|
||||
<div class="section" id="targeting-a-classification-oriented-loss">
|
||||
<h2>Targeting a Classification-oriented loss<a class="headerlink" href="#targeting-a-classification-oriented-loss" title="Permalink to this headline">¶</a></h2>
|
||||
<p>Optimizing a model for quantification could rather be
|
||||
computationally costly.
|
||||
In aggregative methods, one could alternatively try to optimize
|
||||
the classifier’s hyper-parameters for classification.
|
||||
Although this is theoretically suboptimal, many articles in
|
||||
quantification literature have opted for this strategy.</p>
|
||||
<p>In QuaPy, this is achieved by simply instantiating the
|
||||
classifier learner as a GridSearchCV from scikit-learn.
|
||||
The following code illustrates how to do that:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">learner</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span>
|
||||
<span class="n">LogisticRegression</span><span class="p">(),</span>
|
||||
<span class="n">param_grid</span><span class="o">=</span><span class="p">{</span><span class="s1">'C'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="s1">'class_weight'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'balanced'</span><span class="p">,</span> <span class="kc">None</span><span class="p">]},</span>
|
||||
<span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">PCC</span><span class="p">(</span><span class="n">learner</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'model selection ended: best hyper-parameters=</span><span class="si">{</span><span class="n">model</span><span class="o">.</span><span class="n">learner</span><span class="o">.</span><span class="n">best_params_</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>In this example, the system outputs:</p>
|
||||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>model selection ended: best hyper-parameters={'C': 10000.0, 'class_weight': None}
|
||||
1010 evaluations will be performed for each combination of hyper-parameters
|
||||
[artificial sampling protocol] generating predictions: 100%|██████████| 1010/1010 [00:00<00:00, 5379.55it/s]
|
||||
MAE=0.41734
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>Note that the MAE is worse than the one we obtained when optimizing
|
||||
for quantification and, indeed, the hyper-parameters found optimal
|
||||
largely differ between the two selection modalities. The
|
||||
hyper-parameters C=10000 and class_weight=None have been found
|
||||
to work well for the specific training prevalence of the HP dataset,
|
||||
but these hyper-parameters turned out to be suboptimal when the
|
||||
class prevalences of the test set differs (as is indeed tested
|
||||
in scenarios of quantification).</p>
|
||||
<p>This is, however, not always the case, and one could, in practice,
|
||||
find examples
|
||||
in which optimizing for classification ends up resulting in a better
|
||||
quantifier than when optimizing for quantification.
|
||||
Nonetheless, this is theoretically unlikely to happen.</p>
|
||||
</div>
|
||||
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|
||||
|
||||
|
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|
||||
<h3><a href="index.html">Table of Contents</a></h3>
|
||||
<ul>
|
||||
<li><a class="reference internal" href="#">Model Selection</a><ul>
|
||||
<li><a class="reference internal" href="#targeting-a-quantification-oriented-loss">Targeting a Quantification-oriented loss</a></li>
|
||||
<li><a class="reference internal" href="#targeting-a-classification-oriented-loss">Targeting a Classification-oriented loss</a></li>
|
||||
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|
||||
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|
||||
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|
||||
|
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|
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<div class="tex2jax_ignore mathjax_ignore section" id="plotting">
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<h1>Plotting<a class="headerlink" href="#plotting" title="Permalink to this headline">¶</a></h1>
|
||||
<p>The module <em>qp.plot</em> implements some basic plotting functions
|
||||
that can help analyse the performance of a quantification method.</p>
|
||||
<p>All plotting functions receive as inputs the outcomes of
|
||||
some experiments and include, for each experiment,
|
||||
the following three main arguments:</p>
|
||||
<ul class="simple">
|
||||
<li><p><em>method_names</em> a list containing the names of the quantification methods</p></li>
|
||||
<li><p><em>true_prevs</em> a list containing matrices of true prevalences</p></li>
|
||||
<li><p><em>estim_prevs</em> a list containing matrices of estimated prevalences
|
||||
(should be of the same shape as the corresponding matrix in <em>true_prevs</em>)</p></li>
|
||||
</ul>
|
||||
<p>Note that a method (as indicated by a name in <em>method_names</em>) can
|
||||
appear more than once. This could occur when various datasets are
|
||||
involved in the experiments. In this case, all experiments for the
|
||||
method will be merged and the plot will represent the method’s
|
||||
performance across various datasets.</p>
|
||||
<p>This is a very simple example of a valid input for the plotting functions:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">method_names</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'classify & count'</span><span class="p">,</span> <span class="s1">'EMQ'</span><span class="p">,</span> <span class="s1">'classify & count'</span><span class="p">]</span>
|
||||
<span class="n">true_prevs</span> <span class="o">=</span> <span class="p">[</span>
|
||||
<span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">]]),</span>
|
||||
<span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]]),</span>
|
||||
<span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]]),</span>
|
||||
<span class="p">]</span>
|
||||
<span class="n">estim_prevs</span> <span class="o">=</span> <span class="p">[</span>
|
||||
<span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.45</span><span class="p">,</span> <span class="mf">0.55</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.6</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">]]),</span>
|
||||
<span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">]]),</span>
|
||||
<span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]]),</span>
|
||||
<span class="p">]</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>in which the <em>classify & count</em> has been tested in two datasets and
|
||||
the <em>EMQ</em> method has been tested only in one dataset. For the first
|
||||
experiment, only two (binary) quantifications have been tested,
|
||||
while for the second and third experiments three instances have
|
||||
been tested.</p>
|
||||
<p>In general, we would like to test the performance of the
|
||||
quantification methods across different scenarios showcasing
|
||||
the accuracy of the quantifier in predicting class prevalences
|
||||
for a wide range of prior distributions. This can easily be
|
||||
achieved by means of the
|
||||
<a class="reference external" href="https://github.com/HLT-ISTI/QuaPy/wiki/Evaluation">artificial sampling protocol</a>
|
||||
that is implemented in QuaPy.</p>
|
||||
<p>The following code shows how to perform one simple experiment
|
||||
in which the 4 <em>CC-variants</em>, all equipped with a linear SVM, are
|
||||
applied to one binary dataset of reviews about <em>Kindle</em> devices and
|
||||
tested across the entire spectrum of class priors (taking 21 splits
|
||||
of the interval [0,1], i.e., using prevalence steps of 0.05, and
|
||||
generating 100 random samples at each prevalence).</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="n">CC</span><span class="p">,</span> <span class="n">ACC</span><span class="p">,</span> <span class="n">PCC</span><span class="p">,</span> <span class="n">PACC</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">LinearSVC</span>
|
||||
|
||||
<span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'SAMPLE_SIZE'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">500</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">gen_data</span><span class="p">():</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">base_classifier</span><span class="p">():</span>
|
||||
<span class="k">return</span> <span class="n">LinearSVC</span><span class="p">()</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">models</span><span class="p">():</span>
|
||||
<span class="k">yield</span> <span class="n">CC</span><span class="p">(</span><span class="n">base_classifier</span><span class="p">())</span>
|
||||
<span class="k">yield</span> <span class="n">ACC</span><span class="p">(</span><span class="n">base_classifier</span><span class="p">())</span>
|
||||
<span class="k">yield</span> <span class="n">PCC</span><span class="p">(</span><span class="n">base_classifier</span><span class="p">())</span>
|
||||
<span class="k">yield</span> <span class="n">PACC</span><span class="p">(</span><span class="n">base_classifier</span><span class="p">())</span>
|
||||
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="s1">'kindle'</span><span class="p">,</span> <span class="n">tfidf</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
|
||||
|
||||
<span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">tr_prevs</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">models</span><span class="p">():</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">training</span><span class="p">)</span>
|
||||
<span class="n">true_prev</span><span class="p">,</span> <span class="n">estim_prev</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">evaluation</span><span class="o">.</span><span class="n">artificial_sampling_prediction</span><span class="p">(</span>
|
||||
<span class="n">model</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">test</span><span class="p">,</span> <span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'SAMPLE_SIZE'</span><span class="p">],</span> <span class="n">n_repetitions</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">n_prevpoints</span><span class="o">=</span><span class="mi">21</span>
|
||||
<span class="p">)</span>
|
||||
|
||||
<span class="n">method_names</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
|
||||
<span class="n">true_prevs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">true_prev</span><span class="p">)</span>
|
||||
<span class="n">estim_prevs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">estim_prev</span><span class="p">)</span>
|
||||
<span class="n">tr_prevs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">prevalence</span><span class="p">())</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">tr_prevs</span>
|
||||
|
||||
<span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">tr_prevs</span> <span class="o">=</span> <span class="n">gen_data</span><span class="p">()</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>the plots that can be generated are explained below.</p>
|
||||
<div class="section" id="diagonal-plot">
|
||||
<h2>Diagonal Plot<a class="headerlink" href="#diagonal-plot" title="Permalink to this headline">¶</a></h2>
|
||||
<p>The <em>diagonal</em> plot shows a very insightful view of the
|
||||
quantifier’s performance. It plots the predicted class
|
||||
prevalence (in the y-axis) against the true class prevalence
|
||||
(in the x-axis). Unfortunately, it is limited to binary quantification,
|
||||
although one can simply generate as many <em>diagonal</em> plots as
|
||||
classes there are by indicating which class should be considered
|
||||
the target of the plot.</p>
|
||||
<p>The following call will produce the plot:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">qp</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">binary_diagonal</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">train_prev</span><span class="o">=</span><span class="n">tr_prevs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">savepath</span><span class="o">=</span><span class="s1">'./plots/bin_diag.png'</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>the last argument is optional, and indicates the path where to save
|
||||
the plot (the file extension will determine the format – typical extensions
|
||||
are ‘.png’ or ‘.pdf’). If this path is not provided, then the plot
|
||||
will be shown but not saved.
|
||||
The resulting plot should look like:</p>
|
||||
<p><img alt="diagonal plot on Kindle" src="_images/bin_diag.png" /></p>
|
||||
<p>Note that in this case, we are also indicating the training
|
||||
prevalence, which is plotted in the diagonal a as cyan dot.
|
||||
The color bands indicate the standard deviations of the predictions,
|
||||
and can be hidden by setting the argument <em>show_std=False</em> (see
|
||||
the complete list of arguments in the documentation).</p>
|
||||
<p>Finally, note how most quantifiers, and specially the “unadjusted”
|
||||
variants CC and PCC, are strongly biased towards the
|
||||
prevalence seen during training.</p>
|
||||
</div>
|
||||
<div class="section" id="quantification-bias">
|
||||
<h2>Quantification bias<a class="headerlink" href="#quantification-bias" title="Permalink to this headline">¶</a></h2>
|
||||
<p>This plot aims at evincing the bias that any quantifier
|
||||
displays with respect to the training prevalences by
|
||||
means of <a class="reference external" href="https://en.wikipedia.org/wiki/Box_plot">box plots</a>.
|
||||
This plot can be generated by:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">qp</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">binary_bias_global</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">savepath</span><span class="o">=</span><span class="s1">'./plots/bin_bias.png'</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>and should look like:</p>
|
||||
<p><img alt="bias plot on Kindle" src="_images/bin_bias.png" /></p>
|
||||
<p>The box plots show some interesting facts:</p>
|
||||
<ul class="simple">
|
||||
<li><p>all methods are biased towards the training prevalence but specially
|
||||
so CC and PCC (an unbiased quantifier would have a box centered at 0)</p></li>
|
||||
<li><p>the bias is always positive, indicating that all methods tend to
|
||||
overestimate the positive class prevalence</p></li>
|
||||
<li><p>CC and PCC have high variability while ACC and specially PACC exhibit
|
||||
lower variability.</p></li>
|
||||
</ul>
|
||||
<p>Again, these plots could be generated for experiments ranging across
|
||||
different datasets, and the plot will merge all data accordingly.</p>
|
||||
<p>Another illustrative example can be shown that consists of
|
||||
training different CC quantifiers trained at different
|
||||
(artificially sampled) training prevalences.
|
||||
For this example, we generate training samples of 5000
|
||||
documents containing 10%, 20%, …, 90% of positives from the
|
||||
IMDb dataset, and generate the bias plot again.
|
||||
This example can be run by rewritting the <em>gen_data()</em> function
|
||||
like this:</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">gen_data</span><span class="p">():</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="s1">'imdb'</span><span class="p">,</span> <span class="n">tfidf</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">CC</span><span class="p">(</span><span class="n">LinearSVC</span><span class="p">())</span>
|
||||
|
||||
<span class="n">method_data</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">training_prevalence</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">,</span> <span class="mi">9</span><span class="p">):</span>
|
||||
<span class="n">training_size</span> <span class="o">=</span> <span class="mi">5000</span>
|
||||
<span class="c1"># since the problem is binary, it suffices to specify the negative prevalence (the positive is constrained)</span>
|
||||
<span class="n">training</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="n">training_size</span><span class="p">,</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">training_prevalence</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">training</span><span class="p">)</span>
|
||||
<span class="n">true_prev</span><span class="p">,</span> <span class="n">estim_prev</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">evaluation</span><span class="o">.</span><span class="n">artificial_sampling_prediction</span><span class="p">(</span>
|
||||
<span class="n">model</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">sample</span><span class="p">,</span> <span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'SAMPLE_SIZE'</span><span class="p">],</span> <span class="n">n_repetitions</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">n_prevpoints</span><span class="o">=</span><span class="mi">21</span>
|
||||
<span class="p">)</span>
|
||||
<span class="c1"># method names can contain Latex syntax</span>
|
||||
<span class="n">method_name</span> <span class="o">=</span> <span class="s1">'CC$_{'</span> <span class="o">+</span> <span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="nb">int</span><span class="p">(</span><span class="mi">100</span> <span class="o">*</span> <span class="n">training_prevalence</span><span class="p">)</span><span class="si">}</span><span class="s1">'</span> <span class="o">+</span> <span class="s1">'\%}$'</span>
|
||||
<span class="n">method_data</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">method_name</span><span class="p">,</span> <span class="n">true_prev</span><span class="p">,</span> <span class="n">estim_prev</span><span class="p">,</span> <span class="n">training</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()))</span>
|
||||
|
||||
<span class="k">return</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">method_data</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>and the plot should now look like:</p>
|
||||
<p><img alt="bias plot on IMDb" src="_images/bin_bias_cc.png" /></p>
|
||||
<p>which clearly shows a negative bias for CC variants trained on
|
||||
data containing more negatives (i.e., < 50%) and positive biases
|
||||
in cases containing more positives (i.e., >50%). The CC trained
|
||||
at 50% behaves as an unbiased estimator of the positive class
|
||||
prevalence.</p>
|
||||
<p>The function <em>qp.plot.binary_bias_bins</em> allows the user to
|
||||
generate box plots broken down by bins of true test prevalence.
|
||||
To this aim, an argument <em>nbins</em> is passed which indicates
|
||||
how many isometric subintervals to take. For example
|
||||
the following plot is produced for <em>nbins=3</em>:</p>
|
||||
<p><img alt="bias plot on IMDb" src="_images/bin_bias_bin_cc.png" /></p>
|
||||
<p>Interestingly enough, the seemingly unbiased estimator (CC at 50%) happens to display
|
||||
a positive bias (or a tendency to overestimate) in cases of low prevalence
|
||||
(i.e., when the true prevalence of the positive class is below 33%),
|
||||
and a negative bias (or a tendency to underestimate) in cases of high prevalence
|
||||
(i.e., when the true prevalence is beyond 67%).</p>
|
||||
<p>Out of curiosity, the diagonal plot for this experiment looks like:</p>
|
||||
<p><img alt="diag plot on IMDb" src="_images/bin_diag_cc.png" /></p>
|
||||
<p>showing pretty clearly the dependency of CC on the prior probabilities
|
||||
of the labeled set it was trained on.</p>
|
||||
</div>
|
||||
<div class="section" id="error-by-drift">
|
||||
<h2>Error by Drift<a class="headerlink" href="#error-by-drift" title="Permalink to this headline">¶</a></h2>
|
||||
<p>Above discussed plots are useful for analyzing and comparing
|
||||
the performance of different quantification methods, but are
|
||||
limited to the binary case. The “error by drift” is a plot
|
||||
that shows the error in predictions as a function of the
|
||||
(prior probability) drift between each test sample and the
|
||||
training set. Interestingly, the error and drift can both be measured
|
||||
in terms of any evaluation measure for quantification (like the
|
||||
ones available in <em>qp.error</em>) and can thus be computed
|
||||
irrespectively of the number of classes.</p>
|
||||
<p>The following shows how to generate the plot for the 4 CC variants,
|
||||
using 10 bins for the drift
|
||||
and <em>absolute error</em> as the measure of the error (the
|
||||
drift in the x-axis is always computed in terms of <em>absolute error</em> since
|
||||
other errors are harder to interpret):</p>
|
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">qp</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">error_by_drift</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">tr_prevs</span><span class="p">,</span>
|
||||
<span class="n">error_name</span><span class="o">=</span><span class="s1">'ae'</span><span class="p">,</span> <span class="n">n_bins</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">savepath</span><span class="o">=</span><span class="s1">'./plots/err_drift.png'</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p><img alt="diag plot on IMDb" src="_images/err_drift.png" /></p>
|
||||
<p>Note that all methods work reasonably well in cases of low prevalence
|
||||
drift (i.e., any CC-variant is a good quantifier whenever the IID
|
||||
assumption is approximately preserved). The higher the drift, the worse
|
||||
those quantifiers tend to perform, although it is clear that PACC
|
||||
yields the lowest error for the most difficult cases.</p>
|
||||
<p>Remember that any plot can be generated <em>across many datasets</em>, and
|
||||
that this would probably result in a more solid comparison.
|
||||
In those cases, however, it is likely that the variances of each
|
||||
method get higher, to the detriment of the visualization.
|
||||
We recommend to set <em>show_std=False</em> in those cases
|
||||
in order to hide the color bands.</p>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
|
||||
<div class="clearer"></div>
|
||||
</div>
|
||||
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|
||||
</div>
|
||||
<div class="sphinxsidebar" role="navigation" aria-label="main navigation">
|
||||
<div class="sphinxsidebarwrapper">
|
||||
<h3><a href="index.html">Table of Contents</a></h3>
|
||||
<ul>
|
||||
<li><a class="reference internal" href="#">Plotting</a><ul>
|
||||
<li><a class="reference internal" href="#diagonal-plot">Diagonal Plot</a></li>
|
||||
<li><a class="reference internal" href="#quantification-bias">Quantification bias</a></li>
|
||||
<li><a class="reference internal" href="#error-by-drift">Error by Drift</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
|
||||
<h4>Previous topic</h4>
|
||||
<p class="topless"><a href="Methods.html"
|
||||
title="previous chapter">Quantification Methods</a></p>
|
||||
<h4>Next topic</h4>
|
||||
<p class="topless"><a href="modules.html"
|
||||
title="next chapter">quapy</a></p>
|
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|
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<h3>This Page</h3>
|
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<ul class="this-page-menu">
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<li><a href="_sources/Plotting.md.txt"
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rel="nofollow">Show Source</a></li>
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<h3 id="searchlabel">Quick search</h3>
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<form class="search" action="search.html" method="get">
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<input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
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<h3>Navigation</h3>
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<li class="right" style="margin-right: 10px">
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<a href="genindex.html" title="General Index"
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>index</a></li>
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<li class="right" >
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>modules</a> |</li>
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<li class="right" >
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<a href="modules.html" title="quapy"
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<li class="right" >
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<a href="Methods.html" title="Quantification Methods"
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>previous</a> |</li>
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<li class="nav-item nav-item-0"><a href="index.html">QuaPy 0.1.6 documentation</a> »</li>
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<div class="footer" role="contentinfo">
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© Copyright 2021, Alejandro Moreo.
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Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.2.0.
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|
@ -0,0 +1,332 @@
|
|||
# Datasets
|
||||
|
||||
QuaPy makes available several datasets that have been used in
|
||||
quantification literature, as well as an interface to allow
|
||||
anyone import their custom datasets.
|
||||
|
||||
A _Dataset_ object in QuaPy is roughly a pair of _LabelledCollection_ objects,
|
||||
one playing the role of the training set, another the test set.
|
||||
_LabelledCollection_ is a data class consisting of the (iterable)
|
||||
instances and labels. This class handles most of the sampling functionality in QuaPy.
|
||||
Take a look at the following code:
|
||||
|
||||
```python
|
||||
import quapy as qp
|
||||
import quapy.functional as F
|
||||
|
||||
instances = [
|
||||
'1st positive document', '2nd positive document',
|
||||
'the only negative document',
|
||||
'1st neutral document', '2nd neutral document', '3rd neutral document'
|
||||
]
|
||||
labels = [2, 2, 0, 1, 1, 1]
|
||||
|
||||
data = qp.data.LabelledCollection(instances, labels)
|
||||
print(F.strprev(data.prevalence(), prec=2))
|
||||
```
|
||||
|
||||
Output the class prevalences (showing 2 digit precision):
|
||||
```
|
||||
[0.17, 0.50, 0.33]
|
||||
```
|
||||
|
||||
One can easily produce new samples at desired class prevalences:
|
||||
```python
|
||||
sample_size = 10
|
||||
prev = [0.4, 0.1, 0.5]
|
||||
sample = data.sampling(sample_size, *prev)
|
||||
|
||||
print('instances:', sample.instances)
|
||||
print('labels:', sample.labels)
|
||||
print('prevalence:', F.strprev(sample.prevalence(), prec=2))
|
||||
```
|
||||
|
||||
Which outputs:
|
||||
```
|
||||
instances: ['the only negative document' '2nd positive document'
|
||||
'2nd positive document' '2nd neutral document' '1st positive document'
|
||||
'the only negative document' 'the only negative document'
|
||||
'the only negative document' '2nd positive document'
|
||||
'1st positive document']
|
||||
labels: [0 2 2 1 2 0 0 0 2 2]
|
||||
prevalence: [0.40, 0.10, 0.50]
|
||||
```
|
||||
|
||||
Samples can be made consistent across different runs (e.g., to test
|
||||
different methods on the same exact samples) by sampling and retaining
|
||||
the indexes, that can then be used to generate the sample:
|
||||
|
||||
```python
|
||||
index = data.sampling_index(sample_size, *prev)
|
||||
for method in methods:
|
||||
sample = data.sampling_from_index(index)
|
||||
...
|
||||
```
|
||||
|
||||
QuaPy also implements the artificial sampling protocol that produces (via a
|
||||
Python's generator) a series of _LabelledCollection_ objects with equidistant
|
||||
prevalences ranging across the entire prevalence spectrum in the simplex space, e.g.:
|
||||
|
||||
```python
|
||||
for sample in data.artificial_sampling_generator(sample_size=100, n_prevalences=5):
|
||||
print(F.strprev(sample.prevalence(), prec=2))
|
||||
```
|
||||
|
||||
produces one sampling for each (valid) combination of prevalences originating from
|
||||
splitting the range [0,1] into n_prevalences=5 points (i.e., [0, 0.25, 0.5, 0.75, 1]),
|
||||
that is:
|
||||
```
|
||||
[0.00, 0.00, 1.00]
|
||||
[0.00, 0.25, 0.75]
|
||||
[0.00, 0.50, 0.50]
|
||||
[0.00, 0.75, 0.25]
|
||||
[0.00, 1.00, 0.00]
|
||||
[0.25, 0.00, 0.75]
|
||||
...
|
||||
[1.00, 0.00, 0.00]
|
||||
```
|
||||
|
||||
See the [Evaluation wiki](https://github.com/HLT-ISTI/QuaPy/wiki/Evaluation) for
|
||||
further details on how to use the artificial sampling protocol to properly
|
||||
evaluate a quantification method.
|
||||
|
||||
|
||||
## Reviews Datasets
|
||||
|
||||
Three datasets of reviews about Kindle devices, Harry Potter's series, and
|
||||
the well-known IMDb movie reviews can be fetched using a unified interface.
|
||||
For example:
|
||||
|
||||
```python
|
||||
import quapy as qp
|
||||
data = qp.datasets.fetch_reviews('kindle')
|
||||
```
|
||||
|
||||
These datasets have been used in:
|
||||
```
|
||||
Esuli, A., Moreo, A., & Sebastiani, F. (2018, October).
|
||||
A recurrent neural network for sentiment quantification.
|
||||
In Proceedings of the 27th ACM International Conference on
|
||||
Information and Knowledge Management (pp. 1775-1778).
|
||||
```
|
||||
|
||||
The list of reviews ids is available in:
|
||||
|
||||
```python
|
||||
qp.datasets.REVIEWS_SENTIMENT_DATASETS
|
||||
```
|
||||
|
||||
Some statistics of the fhe available datasets are summarized below:
|
||||
|
||||
| Dataset | classes | train size | test size | train prev | test prev | type |
|
||||
|---|:---:|:---:|:---:|:---:|:---:|---|
|
||||
| hp | 2 | 9533 | 18399 | [0.018, 0.982] | [0.065, 0.935] | text |
|
||||
| kindle | 2 | 3821 | 21591 | [0.081, 0.919] | [0.063, 0.937] | text |
|
||||
| imdb | 2 | 25000 | 25000 | [0.500, 0.500] | [0.500, 0.500] | text |
|
||||
|
||||
|
||||
## Twitter Sentiment Datasets
|
||||
|
||||
11 Twitter datasets for sentiment analysis.
|
||||
Text is not accessible, and the documents were made available
|
||||
in tf-idf format. Each dataset presents two splits: a train/val
|
||||
split for model selection purposes, and a train+val/test split
|
||||
for model evaluation. The following code exemplifies how to load
|
||||
a twitter dataset for model selection.
|
||||
|
||||
```python
|
||||
import quapy as qp
|
||||
data = qp.datasets.fetch_twitter('gasp', for_model_selection=True)
|
||||
```
|
||||
|
||||
The datasets were used in:
|
||||
|
||||
```
|
||||
Gao, W., & Sebastiani, F. (2015, August).
|
||||
Tweet sentiment: From classification to quantification.
|
||||
In 2015 IEEE/ACM International Conference on Advances in
|
||||
Social Networks Analysis and Mining (ASONAM) (pp. 97-104). IEEE.
|
||||
```
|
||||
|
||||
Three of the datasets (semeval13, semeval14, and semeval15) share the
|
||||
same training set (semeval), meaning that the training split one would get
|
||||
when requesting any of them is the same. The dataset "semeval" can only
|
||||
be requested with "for_model_selection=True".
|
||||
The lists of the Twitter dataset's ids can be consulted in:
|
||||
|
||||
```python
|
||||
# a list of 11 dataset ids that can be used for model selection or model evaluation
|
||||
qp.datasets.TWITTER_SENTIMENT_DATASETS_TEST
|
||||
|
||||
# 9 dataset ids in which "semeval13", "semeval14", and "semeval15" are replaced with "semeval"
|
||||
qp.datasets.TWITTER_SENTIMENT_DATASETS_TRAIN
|
||||
```
|
||||
|
||||
Some details can be found below:
|
||||
|
||||
| Dataset | classes | train size | test size | features | train prev | test prev | type |
|
||||
|---|:---:|:---:|:---:|:---:|:---:|:---:|---|
|
||||
| gasp | 3 | 8788 | 3765 | 694582 | [0.421, 0.496, 0.082] | [0.407, 0.507, 0.086] | sparse |
|
||||
| hcr | 3 | 1594 | 798 | 222046 | [0.546, 0.211, 0.243] | [0.640, 0.167, 0.193] | sparse |
|
||||
| omd | 3 | 1839 | 787 | 199151 | [0.463, 0.271, 0.266] | [0.437, 0.283, 0.280] | sparse |
|
||||
| sanders | 3 | 2155 | 923 | 229399 | [0.161, 0.691, 0.148] | [0.164, 0.688, 0.148] | sparse |
|
||||
| semeval13 | 3 | 11338 | 3813 | 1215742 | [0.159, 0.470, 0.372] | [0.158, 0.430, 0.412] | sparse |
|
||||
| semeval14 | 3 | 11338 | 1853 | 1215742 | [0.159, 0.470, 0.372] | [0.109, 0.361, 0.530] | sparse |
|
||||
| semeval15 | 3 | 11338 | 2390 | 1215742 | [0.159, 0.470, 0.372] | [0.153, 0.413, 0.434] | sparse |
|
||||
| semeval16 | 3 | 8000 | 2000 | 889504 | [0.157, 0.351, 0.492] | [0.163, 0.341, 0.497] | sparse |
|
||||
| sst | 3 | 2971 | 1271 | 376132 | [0.261, 0.452, 0.288] | [0.207, 0.481, 0.312] | sparse |
|
||||
| wa | 3 | 2184 | 936 | 248563 | [0.305, 0.414, 0.281] | [0.282, 0.446, 0.272] | sparse |
|
||||
| wb | 3 | 4259 | 1823 | 404333 | [0.270, 0.392, 0.337] | [0.274, 0.392, 0.335] | sparse |
|
||||
## UCI Machine Learning
|
||||
|
||||
A set of 32 datasets from the [UCI Machine Learning repository](https://archive.ics.uci.edu/ml/datasets.php)
|
||||
used in:
|
||||
|
||||
```
|
||||
Pérez-Gállego, P., Quevedo, J. R., & del Coz, J. J. (2017).
|
||||
Using ensembles for problems with characterizable changes
|
||||
in data distribution: A case study on quantification.
|
||||
Information Fusion, 34, 87-100.
|
||||
```
|
||||
|
||||
The list does not exactly coincide with that used in Pérez-Gállego et al. 2017
|
||||
since we were unable to find the datasets with ids "diabetes" and "phoneme".
|
||||
|
||||
These dataset can be loaded by calling, e.g.:
|
||||
|
||||
```python
|
||||
import quapy as qp
|
||||
data = qp.datasets.fetch_UCIDataset('yeast', verbose=True)
|
||||
```
|
||||
|
||||
This call will return a _Dataset_ object in which the training and
|
||||
test splits are randomly drawn, in a stratified manner, from the whole
|
||||
collection at 70% and 30%, respectively. The _verbose=True_ option indicates
|
||||
that the dataset description should be printed in standard output.
|
||||
The original data is not split,
|
||||
and some papers submit the entire collection to a kFCV validation.
|
||||
In order to accommodate with these practices, one could first instantiate
|
||||
the entire collection, and then creating a generator that will return one
|
||||
training+test dataset at a time, following a kFCV protocol:
|
||||
|
||||
```python
|
||||
import quapy as qp
|
||||
collection = qp.datasets.fetch_UCILabelledCollection("yeast")
|
||||
for data in qp.data.Dataset.kFCV(collection, nfolds=5, nrepeats=2):
|
||||
...
|
||||
```
|
||||
|
||||
Above code will allow to conduct a 2x5FCV evaluation on the "yeast" dataset.
|
||||
|
||||
All datasets come in numerical form (dense matrices); some statistics
|
||||
are summarized below.
|
||||
|
||||
| Dataset | classes | instances | features | prev | type |
|
||||
|---|:---:|:---:|:---:|:---:|---|
|
||||
| acute.a | 2 | 120 | 6 | [0.508, 0.492] | dense |
|
||||
| acute.b | 2 | 120 | 6 | [0.583, 0.417] | dense |
|
||||
| balance.1 | 2 | 625 | 4 | [0.539, 0.461] | dense |
|
||||
| balance.2 | 2 | 625 | 4 | [0.922, 0.078] | dense |
|
||||
| balance.3 | 2 | 625 | 4 | [0.539, 0.461] | dense |
|
||||
| breast-cancer | 2 | 683 | 9 | [0.350, 0.650] | dense |
|
||||
| cmc.1 | 2 | 1473 | 9 | [0.573, 0.427] | dense |
|
||||
| cmc.2 | 2 | 1473 | 9 | [0.774, 0.226] | dense |
|
||||
| cmc.3 | 2 | 1473 | 9 | [0.653, 0.347] | dense |
|
||||
| ctg.1 | 2 | 2126 | 22 | [0.222, 0.778] | dense |
|
||||
| ctg.2 | 2 | 2126 | 22 | [0.861, 0.139] | dense |
|
||||
| ctg.3 | 2 | 2126 | 22 | [0.917, 0.083] | dense |
|
||||
| german | 2 | 1000 | 24 | [0.300, 0.700] | dense |
|
||||
| haberman | 2 | 306 | 3 | [0.735, 0.265] | dense |
|
||||
| ionosphere | 2 | 351 | 34 | [0.641, 0.359] | dense |
|
||||
| iris.1 | 2 | 150 | 4 | [0.667, 0.333] | dense |
|
||||
| iris.2 | 2 | 150 | 4 | [0.667, 0.333] | dense |
|
||||
| iris.3 | 2 | 150 | 4 | [0.667, 0.333] | dense |
|
||||
| mammographic | 2 | 830 | 5 | [0.514, 0.486] | dense |
|
||||
| pageblocks.5 | 2 | 5473 | 10 | [0.979, 0.021] | dense |
|
||||
| semeion | 2 | 1593 | 256 | [0.901, 0.099] | dense |
|
||||
| sonar | 2 | 208 | 60 | [0.534, 0.466] | dense |
|
||||
| spambase | 2 | 4601 | 57 | [0.606, 0.394] | dense |
|
||||
| spectf | 2 | 267 | 44 | [0.794, 0.206] | dense |
|
||||
| tictactoe | 2 | 958 | 9 | [0.653, 0.347] | dense |
|
||||
| transfusion | 2 | 748 | 4 | [0.762, 0.238] | dense |
|
||||
| wdbc | 2 | 569 | 30 | [0.627, 0.373] | dense |
|
||||
| wine.1 | 2 | 178 | 13 | [0.669, 0.331] | dense |
|
||||
| wine.2 | 2 | 178 | 13 | [0.601, 0.399] | dense |
|
||||
| wine.3 | 2 | 178 | 13 | [0.730, 0.270] | dense |
|
||||
| wine-q-red | 2 | 1599 | 11 | [0.465, 0.535] | dense |
|
||||
| wine-q-white | 2 | 4898 | 11 | [0.335, 0.665] | dense |
|
||||
| yeast | 2 | 1484 | 8 | [0.711, 0.289] | dense |
|
||||
|
||||
### Issues:
|
||||
All datasets will be downloaded automatically the first time they are requested, and
|
||||
stored in the _quapy_data_ folder for faster further reuse.
|
||||
However, some datasets require special actions that at the moment are not fully
|
||||
automated.
|
||||
|
||||
* Datasets with ids "ctg.1", "ctg.2", and "ctg.3" (_Cardiotocography Data Set_) load
|
||||
an Excel file, which requires the user to install the _xlrd_ Python module in order
|
||||
to open it.
|
||||
* The dataset with id "pageblocks.5" (_Page Blocks Classification (5)_) needs to
|
||||
open a "unix compressed file" (extension .Z), which is not directly doable with
|
||||
standard Pythons packages like gzip or zip. This file would need to be uncompressed using
|
||||
OS-dependent software manually. Information on how to do it will be printed the first
|
||||
time the dataset is invoked.
|
||||
|
||||
## Adding Custom Datasets
|
||||
|
||||
QuaPy provides data loaders for simple formats dealing with
|
||||
text, following the format:
|
||||
|
||||
```
|
||||
class-id \t first document's pre-processed text \n
|
||||
class-id \t second document's pre-processed text \n
|
||||
...
|
||||
```
|
||||
|
||||
and sparse representations of the form:
|
||||
|
||||
```
|
||||
{-1, 0, or +1} col(int):val(float) col(int):val(float) ... \n
|
||||
...
|
||||
```
|
||||
|
||||
The code in charge in loading a LabelledCollection is:
|
||||
|
||||
```python
|
||||
@classmethod
|
||||
def load(cls, path:str, loader_func:callable):
|
||||
return LabelledCollection(*loader_func(path))
|
||||
```
|
||||
|
||||
indicating that any _loader_func_ (e.g., a user-defined one) which
|
||||
returns valid arguments for initializing a _LabelledCollection_ object will allow
|
||||
to load any collection. In particular, the _LabelledCollection_ receives as
|
||||
arguments the instances (as an iterable) and the labels (as an iterable) and,
|
||||
additionally, the number of classes can be specified (it would otherwise be
|
||||
inferred from the labels, but that requires at least one positive example for
|
||||
all classes to be present in the collection).
|
||||
|
||||
The same _loader_func_ can be passed to a Dataset, along with two
|
||||
paths, in order to create a training and test pair of _LabelledCollection_,
|
||||
e.g.:
|
||||
|
||||
```python
|
||||
import quapy as qp
|
||||
train_path = '../my_data/train.dat'
|
||||
test_path = '../my_data/test.dat'
|
||||
def my_custom_loader(path):
|
||||
with open(path, 'rb') as fin:
|
||||
...
|
||||
return instances, labels
|
||||
data = qp.data.Dataset.load(train_path, test_path, my_custom_loader)
|
||||
```
|
||||
|
||||
### Data Processing
|
||||
|
||||
QuaPy implements a number of preprocessing functions in the package _qp.data.preprocessing_, including:
|
||||
|
||||
* _text2tfidf_: tfidf vectorization
|
||||
* _reduce_columns_: reducing the number of columns based on term frequency
|
||||
* _standardize_: transforms the column values into z-scores (i.e., subtract the mean and normalizes by the standard deviation, so
|
||||
that the column values have zero mean and unit variance).
|
||||
* _index_: transforms textual tokens into lists of numeric ids)
|
||||
|
|
@ -0,0 +1,232 @@
|
|||
# Evaluation
|
||||
|
||||
Quantification is an appealing tool in scenarios of dataset shift,
|
||||
and particularly in scenarios of prior-probability shift.
|
||||
That is, the interest in estimating the class prevalences arises
|
||||
under the belief that those class prevalences might have changed
|
||||
with respect to the ones observed during training.
|
||||
In other words, one could simply return the training prevalence
|
||||
as a predictor of the test prevalence if this change is assumed
|
||||
to be unlikely (as is the case in general scenarios of
|
||||
machine learning governed by the iid assumption).
|
||||
In brief, quantification requires dedicated evaluation protocols,
|
||||
which are implemented in QuaPy and explained here.
|
||||
|
||||
## Error Measures
|
||||
|
||||
The module quapy.error implements the following error measures for quantification:
|
||||
* _mae_: mean absolute error
|
||||
* _mrae_: mean relative absolute error
|
||||
* _mse_: mean squared error
|
||||
* _mkld_: mean Kullback-Leibler Divergence
|
||||
* _mnkld_: mean normalized Kullback-Leibler Divergence
|
||||
|
||||
Functions _ae_, _rae_, _se_, _kld_, and _nkld_ are also available,
|
||||
which return the individual errors (i.e., without averaging the whole).
|
||||
|
||||
Some errors of classification are also available:
|
||||
* _acce_: accuracy error (1-accuracy)
|
||||
* _f1e_: F-1 score error (1-F1 score)
|
||||
|
||||
The error functions implement the following interface, e.g.:
|
||||
|
||||
```python
|
||||
mae(true_prevs, prevs_hat)
|
||||
```
|
||||
|
||||
in which the first argument is a ndarray containing the true
|
||||
prevalences, and the second argument is another ndarray with
|
||||
the estimations produced by some method.
|
||||
|
||||
Some error functions, e.g., _mrae_, _mkld_, and _mnkld_, are
|
||||
smoothed for numerical stability. In those cases, there is a
|
||||
third argument, e.g.:
|
||||
|
||||
```python
|
||||
def mrae(true_prevs, prevs_hat, eps=None): ...
|
||||
```
|
||||
|
||||
indicating the value for the smoothing parameter epsilon.
|
||||
Traditionally, this value is set to 1/(2T) in past literature,
|
||||
with T the sampling size. One could either pass this value
|
||||
to the function each time, or to set a QuaPy's environment
|
||||
variable _SAMPLE_SIZE_ once, and ommit this argument
|
||||
thereafter (recommended);
|
||||
e.g.:
|
||||
|
||||
```python
|
||||
qp.environ['SAMPLE_SIZE'] = 100 # once for all
|
||||
true_prev = np.asarray([0.5, 0.3, 0.2]) # let's assume 3 classes
|
||||
estim_prev = np.asarray([0.1, 0.3, 0.6])
|
||||
error = qp.ae_.mrae(true_prev, estim_prev)
|
||||
print(f'mrae({true_prev}, {estim_prev}) = {error:.3f}')
|
||||
```
|
||||
|
||||
will print:
|
||||
```
|
||||
mrae([0.500, 0.300, 0.200], [0.100, 0.300, 0.600]) = 0.914
|
||||
```
|
||||
|
||||
Finally, it is possible to instantiate QuaPy's quantification
|
||||
error functions from strings using, e.g.:
|
||||
|
||||
```python
|
||||
error_function = qp.ae_.from_name('mse')
|
||||
error = error_function(true_prev, estim_prev)
|
||||
```
|
||||
|
||||
## Evaluation Protocols
|
||||
|
||||
QuaPy implements the so-called "artificial sampling protocol",
|
||||
according to which a test set is used to generate samplings at
|
||||
desired prevalences of fixed size and covering the full spectrum
|
||||
of prevalences. This protocol is called "artificial" in contrast
|
||||
to the "natural prevalence sampling" protocol that,
|
||||
despite introducing some variability during sampling, approximately
|
||||
preserves the training class prevalence.
|
||||
|
||||
In the artificial sampling procol, the user specifies the number
|
||||
of (equally distant) points to be generated from the interval [0,1].
|
||||
|
||||
For example, if n_prevpoints=11 then, for each class, the prevalences
|
||||
[0., 0.1, 0.2, ..., 1.] will be used. This means that, for two classes,
|
||||
the number of different prevalences will be 11 (since, once the prevalence
|
||||
of one class is determined, the other one is constrained). For 3 classes,
|
||||
the number of valid combinations can be obtained as 11 + 10 + ... + 1 = 66.
|
||||
In general, the number of valid combinations that will be produced for a given
|
||||
value of n_prevpoints can be consulted by invoking
|
||||
quapy.functional.num_prevalence_combinations, e.g.:
|
||||
|
||||
```python
|
||||
import quapy.functional as F
|
||||
n_prevpoints = 21
|
||||
n_classes = 4
|
||||
n = F.num_prevalence_combinations(n_prevpoints, n_classes, n_repeats=1)
|
||||
```
|
||||
|
||||
in this example, n=1771. Note the last argument, n_repeats, that
|
||||
informs of the number of examples that will be generated for any
|
||||
valid combination (typical values are, e.g., 1 for a single sample,
|
||||
or 10 or higher for computing standard deviations of performing statistical
|
||||
significance tests).
|
||||
|
||||
One can instead work the other way around, i.e., one could set a
|
||||
maximum budged of evaluations and get the number of prevalence points that
|
||||
will generate a number of evaluations close, but not higher, than
|
||||
the fixed budget. This can be achieved with the function
|
||||
quapy.functional.get_nprevpoints_approximation, e.g.:
|
||||
|
||||
```python
|
||||
budget = 5000
|
||||
n_prevpoints = F.get_nprevpoints_approximation(budget, n_classes, n_repeats=1)
|
||||
n = F.num_prevalence_combinations(n_prevpoints, n_classes, n_repeats=1)
|
||||
print(f'by setting n_prevpoints={n_prevpoints} the number of evaluations for {n_classes} classes will be {n}')
|
||||
```
|
||||
that will print:
|
||||
```
|
||||
by setting n_prevpoints=30 the number of evaluations for 4 classes will be 4960
|
||||
```
|
||||
|
||||
The cost of evaluation will depend on the values of _n_prevpoints_, _n_classes_,
|
||||
and _n_repeats_. Since it might sometimes be cumbersome to control the overall
|
||||
cost of an experiment having to do with the number of combinations that
|
||||
will be generated for a particular setting of these arguments (particularly
|
||||
when _n_classes>2_), evaluation functions
|
||||
typically allow the user to rather specify an _evaluation budget_, i.e., a maximum
|
||||
number of samplings to generate. By specifying this argument, one could avoid
|
||||
specifying _n_prevpoints_, and the value for it that would lead to a closer
|
||||
number of evaluation budget, without surpassing it, will be automatically set.
|
||||
|
||||
The following script shows a full example in which a PACC model relying
|
||||
on a Logistic Regressor classifier is
|
||||
tested on the _kindle_ dataset by means of the artificial prevalence
|
||||
sampling protocol on samples of size 500, in terms of various
|
||||
evaluation metrics.
|
||||
|
||||
````python
|
||||
import quapy as qp
|
||||
import quapy.functional as F
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
|
||||
qp.environ['SAMPLE_SIZE'] = 500
|
||||
|
||||
dataset = qp.datasets.fetch_reviews('kindle')
|
||||
qp.data.preprocessing.text2tfidf(dataset, min_df=5, inplace=True)
|
||||
|
||||
training = dataset.training
|
||||
test = dataset.test
|
||||
|
||||
lr = LogisticRegression()
|
||||
pacc = qp.method.aggregative.PACC(lr)
|
||||
|
||||
pacc.fit(training)
|
||||
|
||||
df = qp.evaluation.artificial_sampling_report(
|
||||
pacc, # the quantification method
|
||||
test, # the test set on which the method will be evaluated
|
||||
sample_size=qp.environ['SAMPLE_SIZE'], #indicates the size of samples to be drawn
|
||||
n_prevpoints=11, # how many prevalence points will be extracted from the interval [0, 1] for each category
|
||||
n_repetitions=1, # number of times each prevalence will be used to generate a test sample
|
||||
n_jobs=-1, # indicates the number of parallel workers (-1 indicates, as in sklearn, all CPUs)
|
||||
random_seed=42, # setting a random seed allows to replicate the test samples across runs
|
||||
error_metrics=['mae', 'mrae', 'mkld'], # specify the evaluation metrics
|
||||
verbose=True # set to True to show some standard-line outputs
|
||||
)
|
||||
````
|
||||
|
||||
The resulting report is a pandas' dataframe that can be directly printed.
|
||||
Here, we set some display options from pandas just to make the output clearer;
|
||||
note also that the estimated prevalences are shown as strings using the
|
||||
function strprev function that simply converts a prevalence into a
|
||||
string representing it, with a fixed decimal precision (default 3):
|
||||
|
||||
```python
|
||||
import pandas as pd
|
||||
pd.set_option('display.expand_frame_repr', False)
|
||||
pd.set_option("precision", 3)
|
||||
df['estim-prev'] = df['estim-prev'].map(F.strprev)
|
||||
print(df)
|
||||
```
|
||||
|
||||
The output should look like:
|
||||
|
||||
```
|
||||
true-prev estim-prev mae mrae mkld
|
||||
0 [0.0, 1.0] [0.000, 1.000] 0.000 0.000 0.000e+00
|
||||
1 [0.1, 0.9] [0.091, 0.909] 0.009 0.048 4.426e-04
|
||||
2 [0.2, 0.8] [0.163, 0.837] 0.037 0.114 4.633e-03
|
||||
3 [0.3, 0.7] [0.283, 0.717] 0.017 0.041 7.383e-04
|
||||
4 [0.4, 0.6] [0.366, 0.634] 0.034 0.070 2.412e-03
|
||||
5 [0.5, 0.5] [0.459, 0.541] 0.041 0.082 3.387e-03
|
||||
6 [0.6, 0.4] [0.565, 0.435] 0.035 0.073 2.535e-03
|
||||
7 [0.7, 0.3] [0.654, 0.346] 0.046 0.108 4.701e-03
|
||||
8 [0.8, 0.2] [0.725, 0.275] 0.075 0.235 1.515e-02
|
||||
9 [0.9, 0.1] [0.858, 0.142] 0.042 0.229 7.740e-03
|
||||
10 [1.0, 0.0] [0.945, 0.055] 0.055 27.357 5.219e-02
|
||||
```
|
||||
|
||||
One can get the averaged scores using standard pandas'
|
||||
functions, i.e.:
|
||||
|
||||
```python
|
||||
print(df.mean())
|
||||
```
|
||||
|
||||
will produce the following output:
|
||||
|
||||
```
|
||||
true-prev 0.500
|
||||
mae 0.035
|
||||
mrae 2.578
|
||||
mkld 0.009
|
||||
dtype: float64
|
||||
```
|
||||
|
||||
Other evaluation functions include:
|
||||
|
||||
* _artificial_sampling_eval_: that computes the evaluation for a
|
||||
given evaluation metric, returning the average instead of a dataframe.
|
||||
* _artificial_sampling_prediction_: that returns two np.arrays containing the
|
||||
true prevalences and the estimated prevalences.
|
||||
|
||||
See the documentation for further details.
|
||||
|
|
@ -0,0 +1,56 @@
|
|||
Installation
|
||||
------------
|
||||
|
||||
QuaPy can be easily installed via `pip`
|
||||
|
||||
::
|
||||
|
||||
pip install quapy
|
||||
|
||||
See `pip page <https://pypi.org/project/QuaPy/>`_ for older versions.
|
||||
|
||||
Requirements
|
||||
************
|
||||
|
||||
* scikit-learn, numpy, scipy
|
||||
* pytorch (for QuaNet)
|
||||
* svmperf patched for quantification (see below)
|
||||
* joblib
|
||||
* tqdm
|
||||
* pandas, xlrd
|
||||
* matplotlib
|
||||
|
||||
|
||||
SVM-perf with quantification-oriented losses
|
||||
********************************************
|
||||
|
||||
In order to run experiments involving SVM(Q), SVM(KLD), SVM(NKLD),
|
||||
SVM(AE), or SVM(RAE), you have to first download the
|
||||
`svmperf <http://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html>`_
|
||||
package, apply the patch
|
||||
`svm-perf-quantification-ext.patch <https://github.com/HLT-ISTI/QuaPy/blob/master/svm-perf-quantification-ext.patch>`_,
|
||||
and compile the sources.
|
||||
The script
|
||||
`prepare_svmperf.sh <https://github.com/HLT-ISTI/QuaPy/blob/master/prepare_svmperf.sh>`_,
|
||||
does all the job. Simply run:
|
||||
|
||||
::
|
||||
|
||||
./prepare_svmperf.sh
|
||||
|
||||
|
||||
The resulting directory `./svm_perf_quantification` contains the
|
||||
patched version of `svmperf` with quantification-oriented losses.
|
||||
|
||||
The
|
||||
`svm-perf-quantification-ext.patch <https://github.com/HLT-ISTI/QuaPy/blob/master/svm-perf-quantification-ext.patch>`_
|
||||
is an extension of the patch made available by
|
||||
`Esuli et al. 2015 <https://dl.acm.org/doi/abs/10.1145/2700406?casa_token=8D2fHsGCVn0AAAAA:ZfThYOvrzWxMGfZYlQW_y8Cagg-o_l6X_PcF09mdETQ4Tu7jK98mxFbGSXp9ZSO14JkUIYuDGFG0>`_
|
||||
that allows SVMperf to optimize for
|
||||
the `Q` measure as proposed by
|
||||
`Barranquero et al. 2015 <https://www.sciencedirect.com/science/article/abs/pii/S003132031400291X>`_
|
||||
and for the `KLD` and `NKLD` as proposed by
|
||||
`Esuli et al. 2015 <https://dl.acm.org/doi/abs/10.1145/2700406?casa_token=8D2fHsGCVn0AAAAA:ZfThYOvrzWxMGfZYlQW_y8Cagg-o_l6X_PcF09mdETQ4Tu7jK98mxFbGSXp9ZSO14JkUIYuDGFG0>`_
|
||||
for quantification.
|
||||
This patch extends the former by also allowing SVMperf to optimize for
|
||||
`AE` and `RAE`.
|
||||
|
|
@ -0,0 +1,412 @@
|
|||
# Quantification Methods
|
||||
|
||||
Quantification methods can be categorized as belonging to
|
||||
_aggregative_ and _non-aggregative_ groups.
|
||||
Most methods included in QuaPy at the moment are of type _aggregative_
|
||||
(though we plan to add many more methods in the near future), i.e.,
|
||||
are methods characterized by the fact that
|
||||
quantification is performed as an aggregation function of the individual
|
||||
products of classification.
|
||||
|
||||
Any quantifier in QuaPy shoud extend the class _BaseQuantifier_,
|
||||
and implement some abstract methods:
|
||||
```python
|
||||
@abstractmethod
|
||||
def fit(self, data: LabelledCollection): ...
|
||||
|
||||
@abstractmethod
|
||||
def quantify(self, instances): ...
|
||||
|
||||
@abstractmethod
|
||||
def set_params(self, **parameters): ...
|
||||
|
||||
@abstractmethod
|
||||
def get_params(self, deep=True): ...
|
||||
```
|
||||
The meaning of those functions should be familiar to those
|
||||
used to work with scikit-learn since the class structure of QuaPy
|
||||
is directly inspired by scikit-learn's _Estimators_. Functions
|
||||
_fit_ and _quantify_ are used to train the model and to provide
|
||||
class estimations (the reason why
|
||||
scikit-learn' structure has not been adopted _as is_ in QuaPy responds to
|
||||
the fact that scikit-learn's _predict_ function is expected to return
|
||||
one output for each input element --e.g., a predicted label for each
|
||||
instance in a sample-- while in quantification the output for a sample
|
||||
is one single array of class prevalences), while functions _set_params_
|
||||
and _get_params_ allow a
|
||||
[model selector](https://github.com/HLT-ISTI/QuaPy/wiki/Model-Selection)
|
||||
to automate the process of hyperparameter search.
|
||||
|
||||
## Aggregative Methods
|
||||
|
||||
All quantification methods are implemented as part of the
|
||||
_qp.method_ package. In particular, _aggregative_ methods are defined in
|
||||
_qp.method.aggregative_, and extend _AggregativeQuantifier(BaseQuantifier)_.
|
||||
The methods that any _aggregative_ quantifier must implement are:
|
||||
|
||||
```python
|
||||
@abstractmethod
|
||||
def fit(self, data: LabelledCollection, fit_learner=True): ...
|
||||
|
||||
@abstractmethod
|
||||
def aggregate(self, classif_predictions:np.ndarray): ...
|
||||
```
|
||||
|
||||
since, as mentioned before, aggregative methods base their prediction on the
|
||||
individual predictions of a classifier. Indeed, a default implementation
|
||||
of _BaseQuantifier.quantify_ is already provided, which looks like:
|
||||
|
||||
```python
|
||||
def quantify(self, instances):
|
||||
classif_predictions = self.preclassify(instances)
|
||||
return self.aggregate(classif_predictions)
|
||||
```
|
||||
Aggregative quantifiers are expected to maintain a classifier (which is
|
||||
accessed through the _@property_ _learner_). This classifier is
|
||||
given as input to the quantifier, and can be already fit
|
||||
on external data (in which case, the _fit_learner_ argument should
|
||||
be set to False), or be fit by the quantifier's fit (default).
|
||||
|
||||
Another class of _aggregative_ methods are the _probabilistic_
|
||||
aggregative methods, that should inherit from the abstract class
|
||||
_AggregativeProbabilisticQuantifier(AggregativeQuantifier)_.
|
||||
The particularity of _probabilistic_ aggregative methods (w.r.t.
|
||||
non-probabilistic ones), is that the default quantifier is defined
|
||||
in terms of the posterior probabilities returned by a probabilistic
|
||||
classifier, and not by the crisp decisions of a hard classifier; i.e.:
|
||||
|
||||
```python
|
||||
def quantify(self, instances):
|
||||
classif_posteriors = self.posterior_probabilities(instances)
|
||||
return self.aggregate(classif_posteriors)
|
||||
```
|
||||
|
||||
One advantage of _aggregative_ methods (either probabilistic or not)
|
||||
is that the evaluation according to any sampling procedure (e.g.,
|
||||
the [artificial sampling protocol](https://github.com/HLT-ISTI/QuaPy/wiki/Evaluation))
|
||||
can be achieved very efficiently, since the entire set can be pre-classified
|
||||
once, and the quantification estimations for different samples can directly
|
||||
reuse these predictions, without requiring to classify each element every time.
|
||||
QuaPy leverages this property to speed-up any procedure having to do with
|
||||
quantification over samples, as is customarily done in model selection or
|
||||
in evaluation.
|
||||
|
||||
### The Classify & Count variants
|
||||
|
||||
QuaPy implements the four CC variants, i.e.:
|
||||
|
||||
* _CC_ (Classify & Count), the simplest aggregative quantifier; one that
|
||||
simply relies on the label predictions of a classifier to deliver class estimates.
|
||||
* _ACC_ (Adjusted Classify & Count), the adjusted variant of CC.
|
||||
* _PCC_ (Probabilistic Classify & Count), the probabilistic variant of CC that
|
||||
relies on the soft estimations (or posterior probabilities) returned by a (probabilistic) classifier.
|
||||
* _PACC_ (Probabilistic Adjusted Classify & Count), the adjusted variant of PCC.
|
||||
|
||||
The following code serves as a complete example using CC equipped
|
||||
with a SVM as the classifier:
|
||||
|
||||
```python
|
||||
import quapy as qp
|
||||
import quapy.functional as F
|
||||
from sklearn.svm import LinearSVC
|
||||
|
||||
dataset = qp.datasets.fetch_twitter('hcr', pickle=True)
|
||||
training = dataset.training
|
||||
test = dataset.test
|
||||
|
||||
# instantiate a classifier learner, in this case a SVM
|
||||
svm = LinearSVC()
|
||||
|
||||
# instantiate a Classify & Count with the SVM
|
||||
# (an alias is available in qp.method.aggregative.ClassifyAndCount)
|
||||
model = qp.method.aggregative.CC(svm)
|
||||
model.fit(training)
|
||||
estim_prevalence = model.quantify(test.instances)
|
||||
```
|
||||
|
||||
The same code could be used to instantiate an ACC, by simply replacing
|
||||
the instantiation of the model with:
|
||||
```python
|
||||
model = qp.method.aggregative.ACC(svm)
|
||||
```
|
||||
Note that the adjusted variants (ACC and PACC) need to estimate
|
||||
some parameters for performing the adjustment (e.g., the
|
||||
_true positive rate_ and the _false positive rate_ in case of
|
||||
binary classification) that are estimated on a validation split
|
||||
of the labelled set. In this case, the __init__ method of
|
||||
ACC defines an additional parameter, _val_split_ which, by
|
||||
default, is set to 0.4 and so, the 40% of the labelled data
|
||||
will be used for estimating the parameters for adjusting the
|
||||
predictions. This parameters can also be set with an integer,
|
||||
indicating that the parameters should be estimated by means of
|
||||
_k_-fold cross-validation, for which the integer indicates the
|
||||
number _k_ of folds. Finally, _val_split_ can be set to a
|
||||
specific held-out validation set (i.e., an instance of _LabelledCollection_).
|
||||
|
||||
The specification of _val_split_ can be
|
||||
postponed to the invokation of the fit method (if _val_split_ was also
|
||||
set in the constructor, the one specified at fit time would prevail),
|
||||
e.g.:
|
||||
|
||||
```python
|
||||
model = qp.method.aggregative.ACC(svm)
|
||||
# perform 5-fold cross validation for estimating ACC's parameters
|
||||
# (overrides the default val_split=0.4 in the constructor)
|
||||
model.fit(training, val_split=5)
|
||||
```
|
||||
|
||||
The following code illustrates the case in which PCC is used:
|
||||
```python
|
||||
model = qp.method.aggregative.PCC(svm)
|
||||
model.fit(training)
|
||||
estim_prevalence = model.quantify(test.instances)
|
||||
print('classifier:', model.learner)
|
||||
```
|
||||
In this case, QuaPy will print:
|
||||
```
|
||||
The learner LinearSVC does not seem to be probabilistic. The learner will be calibrated.
|
||||
classifier: CalibratedClassifierCV(base_estimator=LinearSVC(), cv=5)
|
||||
```
|
||||
The first output indicates that the learner (_LinearSVC_ in this case)
|
||||
is not a probabilistic classifier (i.e., it does not implement the
|
||||
_predict_proba_ method) and so, the classifier will be converted to
|
||||
a probabilistic one through [calibration](https://scikit-learn.org/stable/modules/calibration.html).
|
||||
As a result, the classifier that is printed in the second line points
|
||||
to a _CalibratedClassifier_ instance. Note that calibration can only
|
||||
be applied to hard classifiers when _fit_learner=True_; an exception
|
||||
will be raised otherwise.
|
||||
|
||||
Lastly, everything we said aboud ACC and PCC
|
||||
applies to PACC as well.
|
||||
|
||||
|
||||
### Expectation Maximization (EMQ)
|
||||
|
||||
The Expectation Maximization Quantifier (EMQ), also known as
|
||||
the SLD, is available at _qp.method.aggregative.EMQ_ or via the
|
||||
alias _qp.method.aggregative.ExpectationMaximizationQuantifier_.
|
||||
The method is described in:
|
||||
|
||||
_Saerens, M., Latinne, P., and Decaestecker, C. (2002). Adjusting the outputs of a classifier
|
||||
to new a priori probabilities: A simple procedure. Neural Computation, 14(1):21–41._
|
||||
|
||||
EMQ works with a probabilistic classifier (if the classifier
|
||||
given as input is a hard one, a calibration will be attempted).
|
||||
Although this method was originally proposed for improving the
|
||||
posterior probabilities of a probabilistic classifier, and not
|
||||
for improving the estimation of prior probabilities, EMQ ranks
|
||||
almost always among the most effective quantifiers in the
|
||||
experiments we have carried out.
|
||||
|
||||
An example of use can be found below:
|
||||
|
||||
```python
|
||||
import quapy as qp
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
|
||||
dataset = qp.datasets.fetch_twitter('hcr', pickle=True)
|
||||
|
||||
model = qp.method.aggregative.EMQ(LogisticRegression())
|
||||
model.fit(dataset.training)
|
||||
estim_prevalence = model.quantify(dataset.test.instances)
|
||||
```
|
||||
|
||||
|
||||
### Hellinger Distance y (HDy)
|
||||
|
||||
The method HDy is described in:
|
||||
|
||||
_Implementation of the method based on the Hellinger Distance y (HDy) proposed by
|
||||
González-Castro, V., Alaiz-Rodrı́guez, R., and Alegre, E. (2013). Class distribution
|
||||
estimation based on the Hellinger distance. Information Sciences, 218:146–164._
|
||||
|
||||
It is implemented in _qp.method.aggregative.HDy_ (also accessible
|
||||
through the allias _qp.method.aggregative.HellingerDistanceY_).
|
||||
This method works with a probabilistic classifier (hard classifiers
|
||||
can be used as well and will be calibrated) and requires a validation
|
||||
set to estimate parameter for the mixture model. Just like
|
||||
ACC and PACC, this quantifier receives a _val_split_ argument
|
||||
in the constructor (or in the fit method, in which case the previous
|
||||
value is overridden) that can either be a float indicating the proportion
|
||||
of training data to be taken as the validation set (in a random
|
||||
stratified split), or a validation set (i.e., an instance of
|
||||
_LabelledCollection_) itself.
|
||||
|
||||
HDy was proposed as a binary classifier and the implementation
|
||||
provided in QuaPy accepts only binary datasets.
|
||||
|
||||
The following code shows an example of use:
|
||||
```python
|
||||
import quapy as qp
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
|
||||
# load a binary dataset
|
||||
dataset = qp.datasets.fetch_reviews('hp', pickle=True)
|
||||
qp.data.preprocessing.text2tfidf(dataset, min_df=5, inplace=True)
|
||||
|
||||
model = qp.method.aggregative.HDy(LogisticRegression())
|
||||
model.fit(dataset.training)
|
||||
estim_prevalence = model.quantify(dataset.test.instances)
|
||||
```
|
||||
|
||||
### Explicit Loss Minimization
|
||||
|
||||
The Explicit Loss Minimization (ELM) represent a family of methods
|
||||
based on structured output learning, i.e., quantifiers relying on
|
||||
classifiers that have been optimized targeting a
|
||||
quantification-oriented evaluation measure.
|
||||
|
||||
In QuaPy, the following methods, all relying on Joachim's
|
||||
[SVMperf](https://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html)
|
||||
implementation, are available in _qp.method.aggregative_:
|
||||
|
||||
* SVMQ (SVM-Q) is a quantification method optimizing the metric _Q_ defined
|
||||
in _Barranquero, J., Díez, J., and del Coz, J. J. (2015). Quantification-oriented learning based
|
||||
on reliable classifiers. Pattern Recognition, 48(2):591–604._
|
||||
* SVMKLD (SVM for Kullback-Leibler Divergence) proposed in _Esuli, A. and Sebastiani, F. (2015).
|
||||
Optimizing text quantifiers for multivariate loss functions.
|
||||
ACM Transactions on Knowledge Discovery and Data, 9(4):Article 27._
|
||||
* SVMNKLD (SVM for Normalized Kullback-Leibler Divergence) proposed in _Esuli, A. and Sebastiani, F. (2015).
|
||||
Optimizing text quantifiers for multivariate loss functions.
|
||||
ACM Transactions on Knowledge Discovery and Data, 9(4):Article 27._
|
||||
* SVMAE (SVM for Mean Absolute Error)
|
||||
* SVMRAE (SVM for Mean Relative Absolute Error)
|
||||
|
||||
the last two methods (SVMAE and SVMRAE) have been implemented in
|
||||
QuaPy in order to make available ELM variants for what nowadays
|
||||
are considered the most well-behaved evaluation metrics in quantification.
|
||||
|
||||
In order to make these models work, you would need to run the script
|
||||
_prepare_svmperf.sh_ (distributed along with QuaPy) that
|
||||
downloads _SVMperf_' source code, applies a patch that
|
||||
implements the quantification oriented losses, and compiles the
|
||||
sources.
|
||||
|
||||
If you want to add any custom loss, you would need to modify
|
||||
the source code of _SVMperf_ in order to implement it, and
|
||||
assign a valid loss code to it. Then you must re-compile
|
||||
the whole thing and instantiate the quantifier in QuaPy
|
||||
as follows:
|
||||
|
||||
```python
|
||||
# you can either set the path to your custom svm_perf_quantification implementation
|
||||
# in the environment variable, or as an argument to the constructor of ELM
|
||||
qp.environ['SVMPERF_HOME'] = './path/to/svm_perf_quantification'
|
||||
|
||||
# assign an alias to your custom loss and the id you have assigned to it
|
||||
svmperf = qp.classification.svmperf.SVMperf
|
||||
svmperf.valid_losses['mycustomloss'] = 28
|
||||
|
||||
# instantiate the ELM method indicating the loss
|
||||
model = qp.method.aggregative.ELM(loss='mycustomloss')
|
||||
```
|
||||
|
||||
All ELM are binary quantifiers since they rely on _SVMperf_, that
|
||||
currently supports only binary classification.
|
||||
ELM variants (any binary quantifier in general) can be extended
|
||||
to operate in single-label scenarios trivially by adopting a
|
||||
"one-vs-all" strategy (as, e.g., in
|
||||
_Gao, W. and Sebastiani, F. (2016). From classification to quantification in tweet sentiment
|
||||
analysis. Social Network Analysis and Mining, 6(19):1–22_).
|
||||
In QuaPy this is possible by using the _OneVsAll_ class:
|
||||
|
||||
```python
|
||||
import quapy as qp
|
||||
from quapy.method.aggregative import SVMQ, OneVsAll
|
||||
|
||||
# load a single-label dataset (this one contains 3 classes)
|
||||
dataset = qp.datasets.fetch_twitter('hcr', pickle=True)
|
||||
|
||||
# let qp know where svmperf is
|
||||
qp.environ['SVMPERF_HOME'] = '../svm_perf_quantification'
|
||||
|
||||
model = OneVsAll(SVMQ(), n_jobs=-1) # run them on parallel
|
||||
model.fit(dataset.training)
|
||||
estim_prevalence = model.quantify(dataset.test.instances)
|
||||
```
|
||||
|
||||
## Meta Models
|
||||
|
||||
By _meta_ models we mean quantification methods that are defined on top of other
|
||||
quantification methods, and that thus do not squarely belong to the aggregative nor
|
||||
the non-aggregative group (indeed, _meta_ models could use quantifiers from any of those
|
||||
groups).
|
||||
_Meta_ models are implemented in the _qp.method.meta_ module.
|
||||
|
||||
### Ensembles
|
||||
|
||||
QuaPy implements (some of) the variants proposed in:
|
||||
|
||||
* _Pérez-Gállego, P., Quevedo, J. R., & del Coz, J. J. (2017).
|
||||
Using ensembles for problems with characterizable changes in data distribution: A case study on quantification.
|
||||
Information Fusion, 34, 87-100._
|
||||
* _Pérez-Gállego, P., Castano, A., Quevedo, J. R., & del Coz, J. J. (2019).
|
||||
Dynamic ensemble selection for quantification tasks.
|
||||
Information Fusion, 45, 1-15._
|
||||
|
||||
The following code shows how to instantiate an Ensemble of 30 _Adjusted Classify & Count_ (ACC)
|
||||
quantifiers operating with a _Logistic Regressor_ (LR) as the base classifier, and using the
|
||||
_average_ as the aggregation policy (see the original article for further details).
|
||||
The last parameter indicates to use all processors for parallelization.
|
||||
|
||||
```python
|
||||
import quapy as qp
|
||||
from quapy.method.aggregative import ACC
|
||||
from quapy.method.meta import Ensemble
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
|
||||
dataset = qp.datasets.fetch_UCIDataset('haberman')
|
||||
|
||||
model = Ensemble(quantifier=ACC(LogisticRegression()), size=30, policy='ave', n_jobs=-1)
|
||||
model.fit(dataset.training)
|
||||
estim_prevalence = model.quantify(dataset.test.instances)
|
||||
```
|
||||
|
||||
Other aggregation policies implemented in QuaPy include:
|
||||
* 'ptr' for applying a dynamic selection based on the training prevalence of the ensemble's members
|
||||
* 'ds' for applying a dynamic selection based on the Hellinger Distance
|
||||
* _any valid quantification measure_ (e.g., 'mse') for performing a static selection based on
|
||||
the performance estimated for each member of the ensemble in terms of that evaluation metric.
|
||||
|
||||
When using any of the above options, it is important to set the _red_size_ parameter, which
|
||||
informs of the number of members to retain.
|
||||
|
||||
Please, check the [model selection](https://github.com/HLT-ISTI/QuaPy/wiki/Model-Selection)
|
||||
wiki if you want to optimize the hyperparameters of ensemble for classification or quantification.
|
||||
|
||||
### The QuaNet neural network
|
||||
|
||||
QuaPy offers an implementation of QuaNet, a deep learning model presented in:
|
||||
|
||||
_Esuli, A., Moreo, A., & Sebastiani, F. (2018, October).
|
||||
A recurrent neural network for sentiment quantification.
|
||||
In Proceedings of the 27th ACM International Conference on
|
||||
Information and Knowledge Management (pp. 1775-1778)._
|
||||
|
||||
This model requires _torch_ to be installed.
|
||||
QuaNet also requires a classifier that can provide embedded representations
|
||||
of the inputs.
|
||||
In the original paper, QuaNet was tested using an LSTM as the base classifier.
|
||||
In the following example, we show an instantiation of QuaNet that instead uses CNN as a probabilistic classifier, taking its last layer representation as the document embedding:
|
||||
|
||||
```python
|
||||
import quapy as qp
|
||||
from quapy.method.meta import QuaNet
|
||||
from quapy.classification.neural import NeuralClassifierTrainer, CNNnet
|
||||
|
||||
# use samples of 100 elements
|
||||
qp.environ['SAMPLE_SIZE'] = 100
|
||||
|
||||
# load the kindle dataset as text, and convert words to numerical indexes
|
||||
dataset = qp.datasets.fetch_reviews('kindle', pickle=True)
|
||||
qp.data.preprocessing.index(dataset, min_df=5, inplace=True)
|
||||
|
||||
# the text classifier is a CNN trained by NeuralClassifierTrainer
|
||||
cnn = CNNnet(dataset.vocabulary_size, dataset.n_classes)
|
||||
learner = NeuralClassifierTrainer(cnn, device='cuda')
|
||||
|
||||
# train QuaNet
|
||||
model = QuaNet(learner, qp.environ['SAMPLE_SIZE'], device='cuda')
|
||||
model.fit(dataset.training)
|
||||
estim_prevalence = model.quantify(dataset.test.instances)
|
||||
```
|
||||
|
|
@ -0,0 +1,159 @@
|
|||
# Model Selection
|
||||
|
||||
As a supervised machine learning task, quantification methods
|
||||
can strongly depend on a good choice of model hyper-parameters.
|
||||
The process whereby those hyper-parameters are chosen is
|
||||
typically known as _Model Selection_, and typically consists of
|
||||
testing different settings and picking the one that performed
|
||||
best in a held-out validation set in terms of any given
|
||||
evaluation measure.
|
||||
|
||||
## Targeting a Quantification-oriented loss
|
||||
|
||||
The task being optimized determines the evaluation protocol,
|
||||
i.e., the criteria according to which the performance of
|
||||
any given method for solving is to be assessed.
|
||||
As a task on its own right, quantification should impose
|
||||
its own model selection strategies, i.e., strategies
|
||||
aimed at finding appropriate configurations
|
||||
specifically designed for the task of quantification.
|
||||
|
||||
Quantification has long been regarded as an add-on of
|
||||
classification, and thus the model selection strategies
|
||||
customarily adopted in classification have simply been
|
||||
applied to quantification (see the next section).
|
||||
It has been argued in _Moreo, Alejandro, and Fabrizio Sebastiani.
|
||||
"Re-Assessing the" Classify and Count" Quantification Method."
|
||||
arXiv preprint arXiv:2011.02552 (2020)._
|
||||
that specific model selection strategies should
|
||||
be adopted for quantification. That is, model selection
|
||||
strategies for quantification should target
|
||||
quantification-oriented losses and be tested in a variety
|
||||
of scenarios exhibiting different degrees of prior
|
||||
probability shift.
|
||||
|
||||
The class
|
||||
_qp.model_selection.GridSearchQ_
|
||||
implements a grid-search exploration over the space of
|
||||
hyper-parameter combinations that evaluates each
|
||||
combination of hyper-parameters
|
||||
by means of a given quantification-oriented
|
||||
error metric (e.g., any of the error functions implemented
|
||||
in _qp.error_) and according to the
|
||||
[_artificial sampling protocol_](https://github.com/HLT-ISTI/QuaPy/wiki/Evaluation).
|
||||
|
||||
The following is an example of model selection for quantification:
|
||||
|
||||
```python
|
||||
import quapy as qp
|
||||
from quapy.method.aggregative import PCC
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
import numpy as np
|
||||
|
||||
# set a seed to replicate runs
|
||||
np.random.seed(0)
|
||||
qp.environ['SAMPLE_SIZE'] = 500
|
||||
|
||||
dataset = qp.datasets.fetch_reviews('hp', tfidf=True, min_df=5)
|
||||
|
||||
# The model will be returned by the fit method of GridSearchQ.
|
||||
# Model selection will be performed with a fixed budget of 1000 evaluations
|
||||
# for each hyper-parameter combination. The error to optimize is the MAE for
|
||||
# quantification, as evaluated on artificially drawn samples at prevalences
|
||||
# covering the entire spectrum on a held-out portion (40%) of the training set.
|
||||
model = qp.model_selection.GridSearchQ(
|
||||
model=PCC(LogisticRegression()),
|
||||
param_grid={'C': np.logspace(-4,5,10), 'class_weight': ['balanced', None]},
|
||||
sample_size=qp.environ['SAMPLE_SIZE'],
|
||||
eval_budget=1000,
|
||||
error='mae',
|
||||
refit=True, # retrain on the whole labelled set
|
||||
val_split=0.4,
|
||||
verbose=True # show information as the process goes on
|
||||
).fit(dataset.training)
|
||||
|
||||
print(f'model selection ended: best hyper-parameters={model.best_params_}')
|
||||
model = model.best_model_
|
||||
|
||||
# evaluation in terms of MAE
|
||||
results = qp.evaluation.artificial_sampling_eval(
|
||||
model,
|
||||
dataset.test,
|
||||
sample_size=qp.environ['SAMPLE_SIZE'],
|
||||
n_prevpoints=101,
|
||||
n_repetitions=10,
|
||||
error_metric='mae'
|
||||
)
|
||||
|
||||
print(f'MAE={results:.5f}')
|
||||
```
|
||||
|
||||
In this example, the system outputs:
|
||||
```
|
||||
[GridSearchQ]: starting optimization with n_jobs=1
|
||||
[GridSearchQ]: checking hyperparams={'C': 0.0001, 'class_weight': 'balanced'} got mae score 0.24987
|
||||
[GridSearchQ]: checking hyperparams={'C': 0.0001, 'class_weight': None} got mae score 0.48135
|
||||
[GridSearchQ]: checking hyperparams={'C': 0.001, 'class_weight': 'balanced'} got mae score 0.24866
|
||||
[...]
|
||||
[GridSearchQ]: checking hyperparams={'C': 100000.0, 'class_weight': None} got mae score 0.43676
|
||||
[GridSearchQ]: optimization finished: best params {'C': 0.1, 'class_weight': 'balanced'} (score=0.19982)
|
||||
[GridSearchQ]: refitting on the whole development set
|
||||
model selection ended: best hyper-parameters={'C': 0.1, 'class_weight': 'balanced'}
|
||||
1010 evaluations will be performed for each combination of hyper-parameters
|
||||
[artificial sampling protocol] generating predictions: 100%|██████████| 1010/1010 [00:00<00:00, 5005.54it/s]
|
||||
MAE=0.20342
|
||||
```
|
||||
|
||||
The parameter _val_split_ can alternatively be used to indicate
|
||||
a validation set (i.e., an instance of _LabelledCollection_) instead
|
||||
of a proportion. This could be useful if one wants to have control
|
||||
on the specific data split to be used across different model selection
|
||||
experiments.
|
||||
|
||||
## Targeting a Classification-oriented loss
|
||||
|
||||
Optimizing a model for quantification could rather be
|
||||
computationally costly.
|
||||
In aggregative methods, one could alternatively try to optimize
|
||||
the classifier's hyper-parameters for classification.
|
||||
Although this is theoretically suboptimal, many articles in
|
||||
quantification literature have opted for this strategy.
|
||||
|
||||
In QuaPy, this is achieved by simply instantiating the
|
||||
classifier learner as a GridSearchCV from scikit-learn.
|
||||
The following code illustrates how to do that:
|
||||
|
||||
```python
|
||||
learner = GridSearchCV(
|
||||
LogisticRegression(),
|
||||
param_grid={'C': np.logspace(-4, 5, 10), 'class_weight': ['balanced', None]},
|
||||
cv=5)
|
||||
model = PCC(learner).fit(dataset.training)
|
||||
print(f'model selection ended: best hyper-parameters={model.learner.best_params_}')
|
||||
```
|
||||
|
||||
In this example, the system outputs:
|
||||
```
|
||||
model selection ended: best hyper-parameters={'C': 10000.0, 'class_weight': None}
|
||||
1010 evaluations will be performed for each combination of hyper-parameters
|
||||
[artificial sampling protocol] generating predictions: 100%|██████████| 1010/1010 [00:00<00:00, 5379.55it/s]
|
||||
MAE=0.41734
|
||||
```
|
||||
|
||||
Note that the MAE is worse than the one we obtained when optimizing
|
||||
for quantification and, indeed, the hyper-parameters found optimal
|
||||
largely differ between the two selection modalities. The
|
||||
hyper-parameters C=10000 and class_weight=None have been found
|
||||
to work well for the specific training prevalence of the HP dataset,
|
||||
but these hyper-parameters turned out to be suboptimal when the
|
||||
class prevalences of the test set differs (as is indeed tested
|
||||
in scenarios of quantification).
|
||||
|
||||
This is, however, not always the case, and one could, in practice,
|
||||
find examples
|
||||
in which optimizing for classification ends up resulting in a better
|
||||
quantifier than when optimizing for quantification.
|
||||
Nonetheless, this is theoretically unlikely to happen.
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,253 @@
|
|||
# Plotting
|
||||
|
||||
The module _qp.plot_ implements some basic plotting functions
|
||||
that can help analyse the performance of a quantification method.
|
||||
|
||||
All plotting functions receive as inputs the outcomes of
|
||||
some experiments and include, for each experiment,
|
||||
the following three main arguments:
|
||||
|
||||
* _method_names_ a list containing the names of the quantification methods
|
||||
* _true_prevs_ a list containing matrices of true prevalences
|
||||
* _estim_prevs_ a list containing matrices of estimated prevalences
|
||||
(should be of the same shape as the corresponding matrix in _true_prevs_)
|
||||
|
||||
Note that a method (as indicated by a name in _method_names_) can
|
||||
appear more than once. This could occur when various datasets are
|
||||
involved in the experiments. In this case, all experiments for the
|
||||
method will be merged and the plot will represent the method's
|
||||
performance across various datasets.
|
||||
|
||||
This is a very simple example of a valid input for the plotting functions:
|
||||
```python
|
||||
method_names = ['classify & count', 'EMQ', 'classify & count']
|
||||
true_prevs = [
|
||||
np.array([[0.5, 0.5], [0.25, 0.75]]),
|
||||
np.array([[0.0, 1.0], [0.25, 0.75], [0.0, 0.1]]),
|
||||
np.array([[0.0, 1.0], [0.25, 0.75], [0.0, 0.1]]),
|
||||
]
|
||||
estim_prevs = [
|
||||
np.array([[0.45, 0.55], [0.6, 0.4]]),
|
||||
np.array([[0.0, 1.0], [0.5, 0.5], [0.2, 0.8]]),
|
||||
np.array([[0.1, 0.9], [0.3, 0.7], [0.0, 0.1]]),
|
||||
]
|
||||
```
|
||||
in which the _classify & count_ has been tested in two datasets and
|
||||
the _EMQ_ method has been tested only in one dataset. For the first
|
||||
experiment, only two (binary) quantifications have been tested,
|
||||
while for the second and third experiments three instances have
|
||||
been tested.
|
||||
|
||||
In general, we would like to test the performance of the
|
||||
quantification methods across different scenarios showcasing
|
||||
the accuracy of the quantifier in predicting class prevalences
|
||||
for a wide range of prior distributions. This can easily be
|
||||
achieved by means of the
|
||||
[artificial sampling protocol](https://github.com/HLT-ISTI/QuaPy/wiki/Evaluation)
|
||||
that is implemented in QuaPy.
|
||||
|
||||
The following code shows how to perform one simple experiment
|
||||
in which the 4 _CC-variants_, all equipped with a linear SVM, are
|
||||
applied to one binary dataset of reviews about _Kindle_ devices and
|
||||
tested across the entire spectrum of class priors (taking 21 splits
|
||||
of the interval [0,1], i.e., using prevalence steps of 0.05, and
|
||||
generating 100 random samples at each prevalence).
|
||||
|
||||
```python
|
||||
import quapy as qp
|
||||
from quapy.method.aggregative import CC, ACC, PCC, PACC
|
||||
from sklearn.svm import LinearSVC
|
||||
|
||||
qp.environ['SAMPLE_SIZE'] = 500
|
||||
|
||||
def gen_data():
|
||||
|
||||
def base_classifier():
|
||||
return LinearSVC()
|
||||
|
||||
def models():
|
||||
yield CC(base_classifier())
|
||||
yield ACC(base_classifier())
|
||||
yield PCC(base_classifier())
|
||||
yield PACC(base_classifier())
|
||||
|
||||
data = qp.datasets.fetch_reviews('kindle', tfidf=True, min_df=5)
|
||||
|
||||
method_names, true_prevs, estim_prevs, tr_prevs = [], [], [], []
|
||||
|
||||
for model in models():
|
||||
model.fit(data.training)
|
||||
true_prev, estim_prev = qp.evaluation.artificial_sampling_prediction(
|
||||
model, data.test, qp.environ['SAMPLE_SIZE'], n_repetitions=100, n_prevpoints=21
|
||||
)
|
||||
|
||||
method_names.append(model.__class__.__name__)
|
||||
true_prevs.append(true_prev)
|
||||
estim_prevs.append(estim_prev)
|
||||
tr_prevs.append(data.training.prevalence())
|
||||
|
||||
return method_names, true_prevs, estim_prevs, tr_prevs
|
||||
|
||||
method_names, true_prevs, estim_prevs, tr_prevs = gen_data()
|
||||
````
|
||||
the plots that can be generated are explained below.
|
||||
|
||||
## Diagonal Plot
|
||||
|
||||
The _diagonal_ plot shows a very insightful view of the
|
||||
quantifier's performance. It plots the predicted class
|
||||
prevalence (in the y-axis) against the true class prevalence
|
||||
(in the x-axis). Unfortunately, it is limited to binary quantification,
|
||||
although one can simply generate as many _diagonal_ plots as
|
||||
classes there are by indicating which class should be considered
|
||||
the target of the plot.
|
||||
|
||||
The following call will produce the plot:
|
||||
|
||||
```python
|
||||
qp.plot.binary_diagonal(method_names, true_prevs, estim_prevs, train_prev=tr_prevs[0], savepath='./plots/bin_diag.png')
|
||||
```
|
||||
|
||||
the last argument is optional, and indicates the path where to save
|
||||
the plot (the file extension will determine the format -- typical extensions
|
||||
are '.png' or '.pdf'). If this path is not provided, then the plot
|
||||
will be shown but not saved.
|
||||
The resulting plot should look like:
|
||||
|
||||
|
||||
|
||||
Note that in this case, we are also indicating the training
|
||||
prevalence, which is plotted in the diagonal a as cyan dot.
|
||||
The color bands indicate the standard deviations of the predictions,
|
||||
and can be hidden by setting the argument _show_std=False_ (see
|
||||
the complete list of arguments in the documentation).
|
||||
|
||||
Finally, note how most quantifiers, and specially the "unadjusted"
|
||||
variants CC and PCC, are strongly biased towards the
|
||||
prevalence seen during training.
|
||||
|
||||
## Quantification bias
|
||||
|
||||
This plot aims at evincing the bias that any quantifier
|
||||
displays with respect to the training prevalences by
|
||||
means of [box plots](https://en.wikipedia.org/wiki/Box_plot).
|
||||
This plot can be generated by:
|
||||
|
||||
```python
|
||||
qp.plot.binary_bias_global(method_names, true_prevs, estim_prevs, savepath='./plots/bin_bias.png')
|
||||
```
|
||||
|
||||
and should look like:
|
||||
|
||||
|
||||
|
||||
The box plots show some interesting facts:
|
||||
* all methods are biased towards the training prevalence but specially
|
||||
so CC and PCC (an unbiased quantifier would have a box centered at 0)
|
||||
* the bias is always positive, indicating that all methods tend to
|
||||
overestimate the positive class prevalence
|
||||
* CC and PCC have high variability while ACC and specially PACC exhibit
|
||||
lower variability.
|
||||
|
||||
Again, these plots could be generated for experiments ranging across
|
||||
different datasets, and the plot will merge all data accordingly.
|
||||
|
||||
Another illustrative example can be shown that consists of
|
||||
training different CC quantifiers trained at different
|
||||
(artificially sampled) training prevalences.
|
||||
For this example, we generate training samples of 5000
|
||||
documents containing 10%, 20%, ..., 90% of positives from the
|
||||
IMDb dataset, and generate the bias plot again.
|
||||
This example can be run by rewritting the _gen_data()_ function
|
||||
like this:
|
||||
|
||||
```python
|
||||
def gen_data():
|
||||
data = qp.datasets.fetch_reviews('imdb', tfidf=True, min_df=5)
|
||||
model = CC(LinearSVC())
|
||||
|
||||
method_data = []
|
||||
for training_prevalence in np.linspace(0.1, 0.9, 9):
|
||||
training_size = 5000
|
||||
# since the problem is binary, it suffices to specify the negative prevalence (the positive is constrained)
|
||||
training = data.training.sampling(training_size, 1 - training_prevalence)
|
||||
model.fit(training)
|
||||
true_prev, estim_prev = qp.evaluation.artificial_sampling_prediction(
|
||||
model, data.sample, qp.environ['SAMPLE_SIZE'], n_repetitions=100, n_prevpoints=21
|
||||
)
|
||||
# method names can contain Latex syntax
|
||||
method_name = 'CC$_{' + f'{int(100 * training_prevalence)}' + '\%}$'
|
||||
method_data.append((method_name, true_prev, estim_prev, training.prevalence()))
|
||||
|
||||
return zip(*method_data)
|
||||
```
|
||||
|
||||
and the plot should now look like:
|
||||
|
||||
|
||||
|
||||
which clearly shows a negative bias for CC variants trained on
|
||||
data containing more negatives (i.e., < 50%) and positive biases
|
||||
in cases containing more positives (i.e., >50%). The CC trained
|
||||
at 50% behaves as an unbiased estimator of the positive class
|
||||
prevalence.
|
||||
|
||||
The function _qp.plot.binary_bias_bins_ allows the user to
|
||||
generate box plots broken down by bins of true test prevalence.
|
||||
To this aim, an argument _nbins_ is passed which indicates
|
||||
how many isometric subintervals to take. For example
|
||||
the following plot is produced for _nbins=3_:
|
||||
|
||||
|
||||
|
||||
Interestingly enough, the seemingly unbiased estimator (CC at 50%) happens to display
|
||||
a positive bias (or a tendency to overestimate) in cases of low prevalence
|
||||
(i.e., when the true prevalence of the positive class is below 33%),
|
||||
and a negative bias (or a tendency to underestimate) in cases of high prevalence
|
||||
(i.e., when the true prevalence is beyond 67%).
|
||||
|
||||
Out of curiosity, the diagonal plot for this experiment looks like:
|
||||
|
||||
|
||||
|
||||
showing pretty clearly the dependency of CC on the prior probabilities
|
||||
of the labeled set it was trained on.
|
||||
|
||||
|
||||
## Error by Drift
|
||||
|
||||
Above discussed plots are useful for analyzing and comparing
|
||||
the performance of different quantification methods, but are
|
||||
limited to the binary case. The "error by drift" is a plot
|
||||
that shows the error in predictions as a function of the
|
||||
(prior probability) drift between each test sample and the
|
||||
training set. Interestingly, the error and drift can both be measured
|
||||
in terms of any evaluation measure for quantification (like the
|
||||
ones available in _qp.error_) and can thus be computed
|
||||
irrespectively of the number of classes.
|
||||
|
||||
The following shows how to generate the plot for the 4 CC variants,
|
||||
using 10 bins for the drift
|
||||
and _absolute error_ as the measure of the error (the
|
||||
drift in the x-axis is always computed in terms of _absolute error_ since
|
||||
other errors are harder to interpret):
|
||||
|
||||
```python
|
||||
qp.plot.error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
|
||||
error_name='ae', n_bins=10, savepath='./plots/err_drift.png')
|
||||
```
|
||||
|
||||
|
||||
|
||||
Note that all methods work reasonably well in cases of low prevalence
|
||||
drift (i.e., any CC-variant is a good quantifier whenever the IID
|
||||
assumption is approximately preserved). The higher the drift, the worse
|
||||
those quantifiers tend to perform, although it is clear that PACC
|
||||
yields the lowest error for the most difficult cases.
|
||||
|
||||
Remember that any plot can be generated _across many datasets_, and
|
||||
that this would probably result in a more solid comparison.
|
||||
In those cases, however, it is likely that the variances of each
|
||||
method get higher, to the detriment of the visualization.
|
||||
We recommend to set _show_std=False_ in those cases
|
||||
in order to hide the color bands.
|
||||
|
|
@ -0,0 +1,90 @@
|
|||
.. QuaPy documentation master file, created by
|
||||
sphinx-quickstart on Tue Nov 9 11:31:32 2021.
|
||||
You can adapt this file completely to your liking, but it should at least
|
||||
contain the root `toctree` directive.
|
||||
|
||||
Welcome to QuaPy's documentation!
|
||||
=================================
|
||||
|
||||
QuaPy is an open source framework for Quantification (a.k.a. Supervised Prevalence Estimation)
|
||||
written in Python.
|
||||
|
||||
Introduction
|
||||
------------
|
||||
|
||||
QuaPy roots on the concept of data sample, and provides implementations of most important concepts
|
||||
in quantification literature, such as the most important quantification baselines, many advanced
|
||||
quantification methods, quantification-oriented model selection, many evaluation measures and protocols
|
||||
used for evaluating quantification methods.
|
||||
QuaPy also integrates commonly used datasets and offers visualization tools for facilitating the analysis and
|
||||
interpretation of results.
|
||||
|
||||
A quick example:
|
||||
****************
|
||||
|
||||
The following script fetchs a Twitter dataset, trains and evaluates an
|
||||
`Adjusted Classify & Count` model in terms of the `Mean Absolute Error` (MAE)
|
||||
between the class prevalences estimated for the test set and the true prevalences
|
||||
of the test set.
|
||||
|
||||
::
|
||||
|
||||
import quapy as qp
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
|
||||
dataset = qp.datasets.fetch_twitter('semeval16')
|
||||
|
||||
# create an "Adjusted Classify & Count" quantifier
|
||||
model = qp.method.aggregative.ACC(LogisticRegression())
|
||||
model.fit(dataset.training)
|
||||
|
||||
estim_prevalences = model.quantify(dataset.test.instances)
|
||||
true_prevalences = dataset.test.prevalence()
|
||||
|
||||
error = qp.error.mae(true_prevalences, estim_prevalences)
|
||||
|
||||
print(f'Mean Absolute Error (MAE)={error:.3f}')
|
||||
|
||||
|
||||
Quantification is useful in scenarios of prior probability shift. In other
|
||||
words, we would not be interested in estimating the class prevalences of the test set if
|
||||
we could assume the IID assumption to hold, as this prevalence would simply coincide with the
|
||||
class prevalence of the training set. For this reason, any Quantification model
|
||||
should be tested across samples characterized by different class prevalences.
|
||||
QuaPy implements sampling procedures and evaluation protocols that automates this endeavour.
|
||||
See the :doc:`Evaluation` for detailed examples.
|
||||
|
||||
Features
|
||||
********
|
||||
|
||||
* Implementation of most popular quantification methods (Classify-&-Count variants, Expectation-Maximization, SVM-based variants for quantification, HDy, QuaNet, and Ensembles).
|
||||
* Versatile functionality for performing evaluation based on artificial sampling protocols.
|
||||
* Implementation of most commonly used evaluation metrics (e.g., MAE, MRAE, MSE, NKLD, etc.).
|
||||
* Popular datasets for Quantification (textual and numeric) available, including:
|
||||
* 32 UCI Machine Learning datasets.
|
||||
* 11 Twitter Sentiment datasets.
|
||||
* 3 Reviews Sentiment datasets.
|
||||
* Native supports for binary and single-label scenarios of quantification.
|
||||
* Model selection functionality targeting quantification-oriented losses.
|
||||
* Visualization tools for analysing results.
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
:caption: Contents:
|
||||
|
||||
Installation
|
||||
Datasets
|
||||
Evaluation
|
||||
Methods
|
||||
Model Selection
|
||||
Plotting
|
||||
API Developer documentation<modules>
|
||||
|
||||
|
||||
|
||||
Indices and tables
|
||||
==================
|
||||
|
||||
* :ref:`genindex`
|
||||
* :ref:`modindex`
|
||||
* :ref:`search`
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
quapy
|
||||
=====
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 4
|
||||
|
||||
quapy
|
||||
|
|
@ -0,0 +1,37 @@
|
|||
quapy.classification package
|
||||
============================
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
quapy.classification.methods module
|
||||
-----------------------------------
|
||||
|
||||
.. automodule:: quapy.classification.methods
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.classification.neural module
|
||||
----------------------------------
|
||||
|
||||
.. automodule:: quapy.classification.neural
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.classification.svmperf module
|
||||
-----------------------------------
|
||||
|
||||
.. automodule:: quapy.classification.svmperf
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: quapy.classification
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
@ -0,0 +1,45 @@
|
|||
quapy.data package
|
||||
==================
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
quapy.data.base module
|
||||
----------------------
|
||||
|
||||
.. automodule:: quapy.data.base
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.data.datasets module
|
||||
--------------------------
|
||||
|
||||
.. automodule:: quapy.data.datasets
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.data.preprocessing module
|
||||
-------------------------------
|
||||
|
||||
.. automodule:: quapy.data.preprocessing
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.data.reader module
|
||||
------------------------
|
||||
|
||||
.. automodule:: quapy.data.reader
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: quapy.data
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
quapy.method package
|
||||
====================
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
quapy.method.aggregative module
|
||||
-------------------------------
|
||||
|
||||
.. automodule:: quapy.method.aggregative
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.method.base module
|
||||
------------------------
|
||||
|
||||
.. automodule:: quapy.method.base
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.method.meta module
|
||||
------------------------
|
||||
|
||||
.. automodule:: quapy.method.meta
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.method.neural module
|
||||
--------------------------
|
||||
|
||||
.. automodule:: quapy.method.neural
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.method.non\_aggregative module
|
||||
------------------------------------
|
||||
|
||||
.. automodule:: quapy.method.non_aggregative
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: quapy.method
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
@ -0,0 +1,72 @@
|
|||
quapy package
|
||||
=============
|
||||
|
||||
Subpackages
|
||||
-----------
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 4
|
||||
|
||||
quapy.classification
|
||||
quapy.data
|
||||
quapy.method
|
||||
quapy.tests
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
quapy.error module
|
||||
------------------
|
||||
|
||||
.. automodule:: quapy.error
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.evaluation module
|
||||
-----------------------
|
||||
|
||||
.. automodule:: quapy.evaluation
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.functional module
|
||||
-----------------------
|
||||
|
||||
.. automodule:: quapy.functional
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.model\_selection module
|
||||
-----------------------------
|
||||
|
||||
.. automodule:: quapy.model_selection
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.plot module
|
||||
-----------------
|
||||
|
||||
.. automodule:: quapy.plot
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.util module
|
||||
-----------------
|
||||
|
||||
.. automodule:: quapy.util
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: quapy
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
@ -0,0 +1,37 @@
|
|||
quapy.tests package
|
||||
===================
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
quapy.tests.test\_base module
|
||||
-----------------------------
|
||||
|
||||
.. automodule:: quapy.tests.test_base
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.tests.test\_datasets module
|
||||
---------------------------------
|
||||
|
||||
.. automodule:: quapy.tests.test_datasets
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
quapy.tests.test\_methods module
|
||||
--------------------------------
|
||||
|
||||
.. automodule:: quapy.tests.test_methods
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: quapy.tests
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
|
@ -0,0 +1,7 @@
|
|||
Getting Started
|
||||
===============
|
||||
QuaPy is an open source framework for Quantification (a.k.a. Supervised Prevalence Estimation) written in Python.
|
||||
|
||||
Installation
|
||||
------------
|
||||
>>> pip install quapy
|
||||
|
|
@ -0,0 +1 @@
|
|||
.. include:: ../../README.md
|
||||
|
|
@ -0,0 +1,701 @@
|
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|
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|
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|
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|
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||||
div.sphinxsidebar h3.donation {
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a {
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color: #6D4100;
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||||
div.body h1,
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div.body h2,
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div.body h3,
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div.body h4,
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div.body h5,
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div.body h6 {
|
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font-family: Georgia, serif;
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font-weight: normal;
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margin: 30px 0px 10px 0px;
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||||
|
||||
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a.headerlink {
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color: #DDD;
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text-decoration: none;
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}
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|
||||
a.headerlink:hover {
|
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color: #444;
|
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background: #EAEAEA;
|
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}
|
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|
||||
div.body p, div.body dd, div.body li {
|
||||
line-height: 1.4em;
|
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}
|
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|
||||
div.admonition {
|
||||
margin: 20px 0px;
|
||||
padding: 10px 30px;
|
||||
background-color: #EEE;
|
||||
border: 1px solid #CCC;
|
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}
|
||||
|
||||
div.admonition tt.xref, div.admonition code.xref, div.admonition a tt {
|
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background-color: #FBFBFB;
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border-bottom: 1px solid #fafafa;
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|
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|
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|
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
border: 1px solid #CCC;
|
||||
}
|
||||
|
||||
div.topic {
|
||||
background-color: #EEE;
|
||||
}
|
||||
|
||||
p.admonition-title {
|
||||
display: inline;
|
||||
}
|
||||
|
||||
p.admonition-title:after {
|
||||
content: ":";
|
||||
}
|
||||
|
||||
pre, tt, code {
|
||||
font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
|
||||
font-size: 0.9em;
|
||||
}
|
||||
|
||||
.hll {
|
||||
background-color: #FFC;
|
||||
margin: 0 -12px;
|
||||
padding: 0 12px;
|
||||
display: block;
|
||||
}
|
||||
|
||||
img.screenshot {
|
||||
}
|
||||
|
||||
tt.descname, tt.descclassname, code.descname, code.descclassname {
|
||||
font-size: 0.95em;
|
||||
}
|
||||
|
||||
tt.descname, code.descname {
|
||||
padding-right: 0.08em;
|
||||
}
|
||||
|
||||
img.screenshot {
|
||||
-moz-box-shadow: 2px 2px 4px #EEE;
|
||||
-webkit-box-shadow: 2px 2px 4px #EEE;
|
||||
box-shadow: 2px 2px 4px #EEE;
|
||||
}
|
||||
|
||||
table.docutils {
|
||||
border: 1px solid #888;
|
||||
-moz-box-shadow: 2px 2px 4px #EEE;
|
||||
-webkit-box-shadow: 2px 2px 4px #EEE;
|
||||
box-shadow: 2px 2px 4px #EEE;
|
||||
}
|
||||
|
||||
table.docutils td, table.docutils th {
|
||||
border: 1px solid #888;
|
||||
padding: 0.25em 0.7em;
|
||||
}
|
||||
|
||||
table.field-list, table.footnote {
|
||||
border: none;
|
||||
-moz-box-shadow: none;
|
||||
-webkit-box-shadow: none;
|
||||
box-shadow: none;
|
||||
}
|
||||
|
||||
table.footnote {
|
||||
margin: 15px 0;
|
||||
width: 100%;
|
||||
border: 1px solid #EEE;
|
||||
background: #FDFDFD;
|
||||
font-size: 0.9em;
|
||||
}
|
||||
|
||||
table.footnote + table.footnote {
|
||||
margin-top: -15px;
|
||||
border-top: none;
|
||||
}
|
||||
|
||||
table.field-list th {
|
||||
padding: 0 0.8em 0 0;
|
||||
}
|
||||
|
||||
table.field-list td {
|
||||
padding: 0;
|
||||
}
|
||||
|
||||
table.field-list p {
|
||||
margin-bottom: 0.8em;
|
||||
}
|
||||
|
||||
/* Cloned from
|
||||
* https://github.com/sphinx-doc/sphinx/commit/ef60dbfce09286b20b7385333d63a60321784e68
|
||||
*/
|
||||
.field-name {
|
||||
-moz-hyphens: manual;
|
||||
-ms-hyphens: manual;
|
||||
-webkit-hyphens: manual;
|
||||
hyphens: manual;
|
||||
}
|
||||
|
||||
table.footnote td.label {
|
||||
width: .1px;
|
||||
padding: 0.3em 0 0.3em 0.5em;
|
||||
}
|
||||
|
||||
table.footnote td {
|
||||
padding: 0.3em 0.5em;
|
||||
}
|
||||
|
||||
dl {
|
||||
margin: 0;
|
||||
padding: 0;
|
||||
}
|
||||
|
||||
dl dd {
|
||||
margin-left: 30px;
|
||||
}
|
||||
|
||||
blockquote {
|
||||
margin: 0 0 0 30px;
|
||||
padding: 0;
|
||||
}
|
||||
|
||||
ul, ol {
|
||||
/* Matches the 30px from the narrow-screen "li > ul" selector below */
|
||||
margin: 10px 0 10px 30px;
|
||||
padding: 0;
|
||||
}
|
||||
|
||||
pre {
|
||||
background: #EEE;
|
||||
padding: 7px 30px;
|
||||
margin: 15px 0px;
|
||||
line-height: 1.3em;
|
||||
}
|
||||
|
||||
div.viewcode-block:target {
|
||||
background: #ffd;
|
||||
}
|
||||
|
||||
dl pre, blockquote pre, li pre {
|
||||
margin-left: 0;
|
||||
padding-left: 30px;
|
||||
}
|
||||
|
||||
tt, code {
|
||||
background-color: #ecf0f3;
|
||||
color: #222;
|
||||
/* padding: 1px 2px; */
|
||||
}
|
||||
|
||||
tt.xref, code.xref, a tt {
|
||||
background-color: #FBFBFB;
|
||||
border-bottom: 1px solid #fff;
|
||||
}
|
||||
|
||||
a.reference {
|
||||
text-decoration: none;
|
||||
border-bottom: 1px dotted #004B6B;
|
||||
}
|
||||
|
||||
/* Don't put an underline on images */
|
||||
a.image-reference, a.image-reference:hover {
|
||||
border-bottom: none;
|
||||
}
|
||||
|
||||
a.reference:hover {
|
||||
border-bottom: 1px solid #6D4100;
|
||||
}
|
||||
|
||||
a.footnote-reference {
|
||||
text-decoration: none;
|
||||
font-size: 0.7em;
|
||||
vertical-align: top;
|
||||
border-bottom: 1px dotted #004B6B;
|
||||
}
|
||||
|
||||
a.footnote-reference:hover {
|
||||
border-bottom: 1px solid #6D4100;
|
||||
}
|
||||
|
||||
a:hover tt, a:hover code {
|
||||
background: #EEE;
|
||||
}
|
||||
|
||||
|
||||
@media screen and (max-width: 870px) {
|
||||
|
||||
div.sphinxsidebar {
|
||||
display: none;
|
||||
}
|
||||
|
||||
div.document {
|
||||
width: 100%;
|
||||
|
||||
}
|
||||
|
||||
div.documentwrapper {
|
||||
margin-left: 0;
|
||||
margin-top: 0;
|
||||
margin-right: 0;
|
||||
margin-bottom: 0;
|
||||
}
|
||||
|
||||
div.bodywrapper {
|
||||
margin-top: 0;
|
||||
margin-right: 0;
|
||||
margin-bottom: 0;
|
||||
margin-left: 0;
|
||||
}
|
||||
|
||||
ul {
|
||||
margin-left: 0;
|
||||
}
|
||||
|
||||
li > ul {
|
||||
/* Matches the 30px from the "ul, ol" selector above */
|
||||
margin-left: 30px;
|
||||
}
|
||||
|
||||
.document {
|
||||
width: auto;
|
||||
}
|
||||
|
||||
.footer {
|
||||
width: auto;
|
||||
}
|
||||
|
||||
.bodywrapper {
|
||||
margin: 0;
|
||||
}
|
||||
|
||||
.footer {
|
||||
width: auto;
|
||||
}
|
||||
|
||||
.github {
|
||||
display: none;
|
||||
}
|
||||
|
||||
|
||||
|
||||
}
|
||||
|
||||
|
||||
|
||||
@media screen and (max-width: 875px) {
|
||||
|
||||
body {
|
||||
margin: 0;
|
||||
padding: 20px 30px;
|
||||
}
|
||||
|
||||
div.documentwrapper {
|
||||
float: none;
|
||||
background: #fff;
|
||||
}
|
||||
|
||||
div.sphinxsidebar {
|
||||
display: block;
|
||||
float: none;
|
||||
width: 102.5%;
|
||||
margin: 50px -30px -20px -30px;
|
||||
padding: 10px 20px;
|
||||
background: #333;
|
||||
color: #FFF;
|
||||
}
|
||||
|
||||
div.sphinxsidebar h3, div.sphinxsidebar h4, div.sphinxsidebar p,
|
||||
div.sphinxsidebar h3 a {
|
||||
color: #fff;
|
||||
}
|
||||
|
||||
div.sphinxsidebar a {
|
||||
color: #AAA;
|
||||
}
|
||||
|
||||
div.sphinxsidebar p.logo {
|
||||
display: none;
|
||||
}
|
||||
|
||||
div.document {
|
||||
width: 100%;
|
||||
margin: 0;
|
||||
}
|
||||
|
||||
div.footer {
|
||||
display: none;
|
||||
}
|
||||
|
||||
div.bodywrapper {
|
||||
margin: 0;
|
||||
}
|
||||
|
||||
div.body {
|
||||
min-height: 0;
|
||||
padding: 0;
|
||||
}
|
||||
|
||||
.rtd_doc_footer {
|
||||
display: none;
|
||||
}
|
||||
|
||||
.document {
|
||||
width: auto;
|
||||
}
|
||||
|
||||
.footer {
|
||||
width: auto;
|
||||
}
|
||||
|
||||
.footer {
|
||||
width: auto;
|
||||
}
|
||||
|
||||
.github {
|
||||
display: none;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
/* misc. */
|
||||
|
||||
.revsys-inline {
|
||||
display: none!important;
|
||||
}
|
||||
|
||||
/* Make nested-list/multi-paragraph items look better in Releases changelog
|
||||
* pages. Without this, docutils' magical list fuckery causes inconsistent
|
||||
* formatting between different release sub-lists.
|
||||
*/
|
||||
div#changelog > div.section > ul > li > p:only-child {
|
||||
margin-bottom: 0;
|
||||
}
|
||||
|
||||
/* Hide fugly table cell borders in ..bibliography:: directive output */
|
||||
table.docutils.citation, table.docutils.citation td, table.docutils.citation th {
|
||||
border: none;
|
||||
/* Below needed in some edge cases; if not applied, bottom shadows appear */
|
||||
-moz-box-shadow: none;
|
||||
-webkit-box-shadow: none;
|
||||
box-shadow: none;
|
||||
}
|
||||
|
||||
|
||||
/* relbar */
|
||||
|
||||
.related {
|
||||
line-height: 30px;
|
||||
width: 100%;
|
||||
font-size: 0.9rem;
|
||||
}
|
||||
|
||||
.related.top {
|
||||
border-bottom: 1px solid #EEE;
|
||||
margin-bottom: 20px;
|
||||
}
|
||||
|
||||
.related.bottom {
|
||||
border-top: 1px solid #EEE;
|
||||
}
|
||||
|
||||
.related ul {
|
||||
padding: 0;
|
||||
margin: 0;
|
||||
list-style: none;
|
||||
}
|
||||
|
||||
.related li {
|
||||
display: inline;
|
||||
}
|
||||
|
||||
nav#rellinks {
|
||||
float: right;
|
||||
}
|
||||
|
||||
nav#rellinks li+li:before {
|
||||
content: "|";
|
||||
}
|
||||
|
||||
nav#breadcrumbs li+li:before {
|
||||
content: "\00BB";
|
||||
}
|
||||
|
||||
/* Hide certain items when printing */
|
||||
@media print {
|
||||
div.related {
|
||||
display: none;
|
||||
}
|
||||
}
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 78 B |
|
|
@ -0,0 +1,904 @@
|
|||
/*
|
||||
* basic.css
|
||||
* ~~~~~~~~~
|
||||
*
|
||||
* Sphinx stylesheet -- basic theme.
|
||||
*
|
||||
* :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
|
||||
* :license: BSD, see LICENSE for details.
|
||||
*
|
||||
*/
|
||||
|
||||
/* -- main layout ----------------------------------------------------------- */
|
||||
|
||||
div.clearer {
|
||||
clear: both;
|
||||
}
|
||||
|
||||
div.section::after {
|
||||
display: block;
|
||||
content: '';
|
||||
clear: left;
|
||||
}
|
||||
|
||||
/* -- relbar ---------------------------------------------------------------- */
|
||||
|
||||
div.related {
|
||||
width: 100%;
|
||||
font-size: 90%;
|
||||
}
|
||||
|
||||
div.related h3 {
|
||||
display: none;
|
||||
}
|
||||
|
||||
div.related ul {
|
||||
margin: 0;
|
||||
padding: 0 0 0 10px;
|
||||
list-style: none;
|
||||
}
|
||||
|
||||
div.related li {
|
||||
display: inline;
|
||||
}
|
||||
|
||||
div.related li.right {
|
||||
float: right;
|
||||
margin-right: 5px;
|
||||
}
|
||||
|
||||
/* -- sidebar --------------------------------------------------------------- */
|
||||
|
||||
div.sphinxsidebarwrapper {
|
||||
padding: 10px 5px 0 10px;
|
||||
}
|
||||
|
||||
div.sphinxsidebar {
|
||||
float: left;
|
||||
width: 210px;
|
||||
margin-left: -100%;
|
||||
font-size: 90%;
|
||||
word-wrap: break-word;
|
||||
overflow-wrap : break-word;
|
||||
}
|
||||
|
||||
div.sphinxsidebar ul {
|
||||
list-style: none;
|
||||
}
|
||||
|
||||
div.sphinxsidebar ul ul,
|
||||
div.sphinxsidebar ul.want-points {
|
||||
margin-left: 20px;
|
||||
list-style: square;
|
||||
}
|
||||
|
||||
div.sphinxsidebar ul ul {
|
||||
margin-top: 0;
|
||||
margin-bottom: 0;
|
||||
}
|
||||
|
||||
div.sphinxsidebar form {
|
||||
margin-top: 10px;
|
||||
}
|
||||
|
||||
div.sphinxsidebar input {
|
||||
border: 1px solid #98dbcc;
|
||||
font-family: sans-serif;
|
||||
font-size: 1em;
|
||||
}
|
||||
|
||||
div.sphinxsidebar #searchbox form.search {
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
div.sphinxsidebar #searchbox input[type="text"] {
|
||||
float: left;
|
||||
width: 80%;
|
||||
padding: 0.25em;
|
||||
box-sizing: border-box;
|
||||
}
|
||||
|
||||
div.sphinxsidebar #searchbox input[type="submit"] {
|
||||
float: left;
|
||||
width: 20%;
|
||||
border-left: none;
|
||||
padding: 0.25em;
|
||||
box-sizing: border-box;
|
||||
}
|
||||
|
||||
|
||||
img {
|
||||
border: 0;
|
||||
max-width: 100%;
|
||||
}
|
||||
|
||||
/* -- search page ----------------------------------------------------------- */
|
||||
|
||||
ul.search {
|
||||
margin: 10px 0 0 20px;
|
||||
padding: 0;
|
||||
}
|
||||
|
||||
ul.search li {
|
||||
padding: 5px 0 5px 20px;
|
||||
background-image: url(file.png);
|
||||
background-repeat: no-repeat;
|
||||
background-position: 0 7px;
|
||||
}
|
||||
|
||||
ul.search li a {
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
ul.search li p.context {
|
||||
color: #888;
|
||||
margin: 2px 0 0 30px;
|
||||
text-align: left;
|
||||
}
|
||||
|
||||
ul.keywordmatches li.goodmatch a {
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
/* -- index page ------------------------------------------------------------ */
|
||||
|
||||
table.contentstable {
|
||||
width: 90%;
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
||||
}
|
||||
|
||||
table.contentstable p.biglink {
|
||||
line-height: 150%;
|
||||
}
|
||||
|
||||
a.biglink {
|
||||
font-size: 1.3em;
|
||||
}
|
||||
|
||||
span.linkdescr {
|
||||
font-style: italic;
|
||||
padding-top: 5px;
|
||||
font-size: 90%;
|
||||
}
|
||||
|
||||
/* -- general index --------------------------------------------------------- */
|
||||
|
||||
table.indextable {
|
||||
width: 100%;
|
||||
}
|
||||
|
||||
table.indextable td {
|
||||
text-align: left;
|
||||
vertical-align: top;
|
||||
}
|
||||
|
||||
table.indextable ul {
|
||||
margin-top: 0;
|
||||
margin-bottom: 0;
|
||||
list-style-type: none;
|
||||
}
|
||||
|
||||
table.indextable > tbody > tr > td > ul {
|
||||
padding-left: 0em;
|
||||
}
|
||||
|
||||
table.indextable tr.pcap {
|
||||
height: 10px;
|
||||
}
|
||||
|
||||
table.indextable tr.cap {
|
||||
margin-top: 10px;
|
||||
background-color: #f2f2f2;
|
||||
}
|
||||
|
||||
img.toggler {
|
||||
margin-right: 3px;
|
||||
margin-top: 3px;
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
div.modindex-jumpbox {
|
||||
border-top: 1px solid #ddd;
|
||||
border-bottom: 1px solid #ddd;
|
||||
margin: 1em 0 1em 0;
|
||||
padding: 0.4em;
|
||||
}
|
||||
|
||||
div.genindex-jumpbox {
|
||||
border-top: 1px solid #ddd;
|
||||
border-bottom: 1px solid #ddd;
|
||||
margin: 1em 0 1em 0;
|
||||
padding: 0.4em;
|
||||
}
|
||||
|
||||
/* -- domain module index --------------------------------------------------- */
|
||||
|
||||
table.modindextable td {
|
||||
padding: 2px;
|
||||
border-collapse: collapse;
|
||||
}
|
||||
|
||||
/* -- general body styles --------------------------------------------------- */
|
||||
|
||||
div.body {
|
||||
min-width: 450px;
|
||||
max-width: 800px;
|
||||
}
|
||||
|
||||
div.body p, div.body dd, div.body li, div.body blockquote {
|
||||
-moz-hyphens: auto;
|
||||
-ms-hyphens: auto;
|
||||
-webkit-hyphens: auto;
|
||||
hyphens: auto;
|
||||
}
|
||||
|
||||
a.headerlink {
|
||||
visibility: hidden;
|
||||
}
|
||||
|
||||
a.brackets:before,
|
||||
span.brackets > a:before{
|
||||
content: "[";
|
||||
}
|
||||
|
||||
a.brackets:after,
|
||||
span.brackets > a:after {
|
||||
content: "]";
|
||||
}
|
||||
|
||||
h1:hover > a.headerlink,
|
||||
h2:hover > a.headerlink,
|
||||
h3:hover > a.headerlink,
|
||||
h4:hover > a.headerlink,
|
||||
h5:hover > a.headerlink,
|
||||
h6:hover > a.headerlink,
|
||||
dt:hover > a.headerlink,
|
||||
caption:hover > a.headerlink,
|
||||
p.caption:hover > a.headerlink,
|
||||
div.code-block-caption:hover > a.headerlink {
|
||||
visibility: visible;
|
||||
}
|
||||
|
||||
div.body p.caption {
|
||||
text-align: inherit;
|
||||
}
|
||||
|
||||
div.body td {
|
||||
text-align: left;
|
||||
}
|
||||
|
||||
.first {
|
||||
margin-top: 0 !important;
|
||||
}
|
||||
|
||||
p.rubric {
|
||||
margin-top: 30px;
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
img.align-left, figure.align-left, .figure.align-left, object.align-left {
|
||||
clear: left;
|
||||
float: left;
|
||||
margin-right: 1em;
|
||||
}
|
||||
|
||||
img.align-right, figure.align-right, .figure.align-right, object.align-right {
|
||||
clear: right;
|
||||
float: right;
|
||||
margin-left: 1em;
|
||||
}
|
||||
|
||||
img.align-center, figure.align-center, .figure.align-center, object.align-center {
|
||||
display: block;
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
||||
}
|
||||
|
||||
img.align-default, figure.align-default, .figure.align-default {
|
||||
display: block;
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
||||
}
|
||||
|
||||
.align-left {
|
||||
text-align: left;
|
||||
}
|
||||
|
||||
.align-center {
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.align-default {
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.align-right {
|
||||
text-align: right;
|
||||
}
|
||||
|
||||
/* -- sidebars -------------------------------------------------------------- */
|
||||
|
||||
div.sidebar,
|
||||
aside.sidebar {
|
||||
margin: 0 0 0.5em 1em;
|
||||
border: 1px solid #ddb;
|
||||
padding: 7px;
|
||||
background-color: #ffe;
|
||||
width: 40%;
|
||||
float: right;
|
||||
clear: right;
|
||||
overflow-x: auto;
|
||||
}
|
||||
|
||||
p.sidebar-title {
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
div.admonition, div.topic, blockquote {
|
||||
clear: left;
|
||||
}
|
||||
|
||||
/* -- topics ---------------------------------------------------------------- */
|
||||
|
||||
div.topic {
|
||||
border: 1px solid #ccc;
|
||||
padding: 7px;
|
||||
margin: 10px 0 10px 0;
|
||||
}
|
||||
|
||||
p.topic-title {
|
||||
font-size: 1.1em;
|
||||
font-weight: bold;
|
||||
margin-top: 10px;
|
||||
}
|
||||
|
||||
/* -- admonitions ----------------------------------------------------------- */
|
||||
|
||||
div.admonition {
|
||||
margin-top: 10px;
|
||||
margin-bottom: 10px;
|
||||
padding: 7px;
|
||||
}
|
||||
|
||||
div.admonition dt {
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
p.admonition-title {
|
||||
margin: 0px 10px 5px 0px;
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
div.body p.centered {
|
||||
text-align: center;
|
||||
margin-top: 25px;
|
||||
}
|
||||
|
||||
/* -- content of sidebars/topics/admonitions -------------------------------- */
|
||||
|
||||
div.sidebar > :last-child,
|
||||
aside.sidebar > :last-child,
|
||||
div.topic > :last-child,
|
||||
div.admonition > :last-child {
|
||||
margin-bottom: 0;
|
||||
}
|
||||
|
||||
div.sidebar::after,
|
||||
aside.sidebar::after,
|
||||
div.topic::after,
|
||||
div.admonition::after,
|
||||
blockquote::after {
|
||||
display: block;
|
||||
content: '';
|
||||
clear: both;
|
||||
}
|
||||
|
||||
/* -- tables ---------------------------------------------------------------- */
|
||||
|
||||
table.docutils {
|
||||
margin-top: 10px;
|
||||
margin-bottom: 10px;
|
||||
border: 0;
|
||||
border-collapse: collapse;
|
||||
}
|
||||
|
||||
table.align-center {
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
||||
}
|
||||
|
||||
table.align-default {
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
||||
}
|
||||
|
||||
table caption span.caption-number {
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
table caption span.caption-text {
|
||||
}
|
||||
|
||||
table.docutils td, table.docutils th {
|
||||
padding: 1px 8px 1px 5px;
|
||||
border-top: 0;
|
||||
border-left: 0;
|
||||
border-right: 0;
|
||||
border-bottom: 1px solid #aaa;
|
||||
}
|
||||
|
||||
table.footnote td, table.footnote th {
|
||||
border: 0 !important;
|
||||
}
|
||||
|
||||
th {
|
||||
text-align: left;
|
||||
padding-right: 5px;
|
||||
}
|
||||
|
||||
table.citation {
|
||||
border-left: solid 1px gray;
|
||||
margin-left: 1px;
|
||||
}
|
||||
|
||||
table.citation td {
|
||||
border-bottom: none;
|
||||
}
|
||||
|
||||
th > :first-child,
|
||||
td > :first-child {
|
||||
margin-top: 0px;
|
||||
}
|
||||
|
||||
th > :last-child,
|
||||
td > :last-child {
|
||||
margin-bottom: 0px;
|
||||
}
|
||||
|
||||
/* -- figures --------------------------------------------------------------- */
|
||||
|
||||
div.figure, figure {
|
||||
margin: 0.5em;
|
||||
padding: 0.5em;
|
||||
}
|
||||
|
||||
div.figure p.caption, figcaption {
|
||||
padding: 0.3em;
|
||||
}
|
||||
|
||||
div.figure p.caption span.caption-number,
|
||||
figcaption span.caption-number {
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
div.figure p.caption span.caption-text,
|
||||
figcaption span.caption-text {
|
||||
}
|
||||
|
||||
/* -- field list styles ----------------------------------------------------- */
|
||||
|
||||
table.field-list td, table.field-list th {
|
||||
border: 0 !important;
|
||||
}
|
||||
|
||||
.field-list ul {
|
||||
margin: 0;
|
||||
padding-left: 1em;
|
||||
}
|
||||
|
||||
.field-list p {
|
||||
margin: 0;
|
||||
}
|
||||
|
||||
.field-name {
|
||||
-moz-hyphens: manual;
|
||||
-ms-hyphens: manual;
|
||||
-webkit-hyphens: manual;
|
||||
hyphens: manual;
|
||||
}
|
||||
|
||||
/* -- hlist styles ---------------------------------------------------------- */
|
||||
|
||||
table.hlist {
|
||||
margin: 1em 0;
|
||||
}
|
||||
|
||||
table.hlist td {
|
||||
vertical-align: top;
|
||||
}
|
||||
|
||||
/* -- object description styles --------------------------------------------- */
|
||||
|
||||
.sig {
|
||||
font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
|
||||
}
|
||||
|
||||
.sig-name, code.descname {
|
||||
background-color: transparent;
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
.sig-name {
|
||||
font-size: 1.1em;
|
||||
}
|
||||
|
||||
code.descname {
|
||||
font-size: 1.2em;
|
||||
}
|
||||
|
||||
.sig-prename, code.descclassname {
|
||||
background-color: transparent;
|
||||
}
|
||||
|
||||
.optional {
|
||||
font-size: 1.3em;
|
||||
}
|
||||
|
||||
.sig-paren {
|
||||
font-size: larger;
|
||||
}
|
||||
|
||||
.sig-param.n {
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
/* C++ specific styling */
|
||||
|
||||
.sig-inline.c-texpr,
|
||||
.sig-inline.cpp-texpr {
|
||||
font-family: unset;
|
||||
}
|
||||
|
||||
.sig.c .k, .sig.c .kt,
|
||||
.sig.cpp .k, .sig.cpp .kt {
|
||||
color: #0033B3;
|
||||
}
|
||||
|
||||
.sig.c .m,
|
||||
.sig.cpp .m {
|
||||
color: #1750EB;
|
||||
}
|
||||
|
||||
.sig.c .s, .sig.c .sc,
|
||||
.sig.cpp .s, .sig.cpp .sc {
|
||||
color: #067D17;
|
||||
}
|
||||
|
||||
|
||||
/* -- other body styles ----------------------------------------------------- */
|
||||
|
||||
ol.arabic {
|
||||
list-style: decimal;
|
||||
}
|
||||
|
||||
ol.loweralpha {
|
||||
list-style: lower-alpha;
|
||||
}
|
||||
|
||||
ol.upperalpha {
|
||||
list-style: upper-alpha;
|
||||
}
|
||||
|
||||
ol.lowerroman {
|
||||
list-style: lower-roman;
|
||||
}
|
||||
|
||||
ol.upperroman {
|
||||
list-style: upper-roman;
|
||||
}
|
||||
|
||||
:not(li) > ol > li:first-child > :first-child,
|
||||
:not(li) > ul > li:first-child > :first-child {
|
||||
margin-top: 0px;
|
||||
}
|
||||
|
||||
:not(li) > ol > li:last-child > :last-child,
|
||||
:not(li) > ul > li:last-child > :last-child {
|
||||
margin-bottom: 0px;
|
||||
}
|
||||
|
||||
ol.simple ol p,
|
||||
ol.simple ul p,
|
||||
ul.simple ol p,
|
||||
ul.simple ul p {
|
||||
margin-top: 0;
|
||||
}
|
||||
|
||||
ol.simple > li:not(:first-child) > p,
|
||||
ul.simple > li:not(:first-child) > p {
|
||||
margin-top: 0;
|
||||
}
|
||||
|
||||
ol.simple p,
|
||||
ul.simple p {
|
||||
margin-bottom: 0;
|
||||
}
|
||||
|
||||
dl.footnote > dt,
|
||||
dl.citation > dt {
|
||||
float: left;
|
||||
margin-right: 0.5em;
|
||||
}
|
||||
|
||||
dl.footnote > dd,
|
||||
dl.citation > dd {
|
||||
margin-bottom: 0em;
|
||||
}
|
||||
|
||||
dl.footnote > dd:after,
|
||||
dl.citation > dd:after {
|
||||
content: "";
|
||||
clear: both;
|
||||
}
|
||||
|
||||
dl.field-list {
|
||||
display: grid;
|
||||
grid-template-columns: fit-content(30%) auto;
|
||||
}
|
||||
|
||||
dl.field-list > dt {
|
||||
font-weight: bold;
|
||||
word-break: break-word;
|
||||
padding-left: 0.5em;
|
||||
padding-right: 5px;
|
||||
}
|
||||
|
||||
dl.field-list > dt:after {
|
||||
content: ":";
|
||||
}
|
||||
|
||||
dl.field-list > dd {
|
||||
padding-left: 0.5em;
|
||||
margin-top: 0em;
|
||||
margin-left: 0em;
|
||||
margin-bottom: 0em;
|
||||
}
|
||||
|
||||
dl {
|
||||
margin-bottom: 15px;
|
||||
}
|
||||
|
||||
dd > :first-child {
|
||||
margin-top: 0px;
|
||||
}
|
||||
|
||||
dd ul, dd table {
|
||||
margin-bottom: 10px;
|
||||
}
|
||||
|
||||
dd {
|
||||
margin-top: 3px;
|
||||
margin-bottom: 10px;
|
||||
margin-left: 30px;
|
||||
}
|
||||
|
||||
dl > dd:last-child,
|
||||
dl > dd:last-child > :last-child {
|
||||
margin-bottom: 0;
|
||||
}
|
||||
|
||||
dt:target, span.highlighted {
|
||||
background-color: #fbe54e;
|
||||
}
|
||||
|
||||
rect.highlighted {
|
||||
fill: #fbe54e;
|
||||
}
|
||||
|
||||
dl.glossary dt {
|
||||
font-weight: bold;
|
||||
font-size: 1.1em;
|
||||
}
|
||||
|
||||
.versionmodified {
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
.system-message {
|
||||
background-color: #fda;
|
||||
padding: 5px;
|
||||
border: 3px solid red;
|
||||
}
|
||||
|
||||
.footnote:target {
|
||||
background-color: #ffa;
|
||||
}
|
||||
|
||||
.line-block {
|
||||
display: block;
|
||||
margin-top: 1em;
|
||||
margin-bottom: 1em;
|
||||
}
|
||||
|
||||
.line-block .line-block {
|
||||
margin-top: 0;
|
||||
margin-bottom: 0;
|
||||
margin-left: 1.5em;
|
||||
}
|
||||
|
||||
.guilabel, .menuselection {
|
||||
font-family: sans-serif;
|
||||
}
|
||||
|
||||
.accelerator {
|
||||
text-decoration: underline;
|
||||
}
|
||||
|
||||
.classifier {
|
||||
font-style: oblique;
|
||||
}
|
||||
|
||||
.classifier:before {
|
||||
font-style: normal;
|
||||
margin: 0.5em;
|
||||
content: ":";
|
||||
}
|
||||
|
||||
abbr, acronym {
|
||||
border-bottom: dotted 1px;
|
||||
cursor: help;
|
||||
}
|
||||
|
||||
/* -- code displays --------------------------------------------------------- */
|
||||
|
||||
pre {
|
||||
overflow: auto;
|
||||
overflow-y: hidden; /* fixes display issues on Chrome browsers */
|
||||
}
|
||||
|
||||
pre, div[class*="highlight-"] {
|
||||
clear: both;
|
||||
}
|
||||
|
||||
span.pre {
|
||||
-moz-hyphens: none;
|
||||
-ms-hyphens: none;
|
||||
-webkit-hyphens: none;
|
||||
hyphens: none;
|
||||
}
|
||||
|
||||
div[class*="highlight-"] {
|
||||
margin: 1em 0;
|
||||
}
|
||||
|
||||
td.linenos pre {
|
||||
border: 0;
|
||||
background-color: transparent;
|
||||
color: #aaa;
|
||||
}
|
||||
|
||||
table.highlighttable {
|
||||
display: block;
|
||||
}
|
||||
|
||||
table.highlighttable tbody {
|
||||
display: block;
|
||||
}
|
||||
|
||||
table.highlighttable tr {
|
||||
display: flex;
|
||||
}
|
||||
|
||||
table.highlighttable td {
|
||||
margin: 0;
|
||||
padding: 0;
|
||||
}
|
||||
|
||||
table.highlighttable td.linenos {
|
||||
padding-right: 0.5em;
|
||||
}
|
||||
|
||||
table.highlighttable td.code {
|
||||
flex: 1;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.highlight .hll {
|
||||
display: block;
|
||||
}
|
||||
|
||||
div.highlight pre,
|
||||
table.highlighttable pre {
|
||||
margin: 0;
|
||||
}
|
||||
|
||||
div.code-block-caption + div {
|
||||
margin-top: 0;
|
||||
}
|
||||
|
||||
div.code-block-caption {
|
||||
margin-top: 1em;
|
||||
padding: 2px 5px;
|
||||
font-size: small;
|
||||
}
|
||||
|
||||
div.code-block-caption code {
|
||||
background-color: transparent;
|
||||
}
|
||||
|
||||
table.highlighttable td.linenos,
|
||||
span.linenos,
|
||||
div.highlight span.gp { /* gp: Generic.Prompt */
|
||||
user-select: none;
|
||||
-webkit-user-select: text; /* Safari fallback only */
|
||||
-webkit-user-select: none; /* Chrome/Safari */
|
||||
-moz-user-select: none; /* Firefox */
|
||||
-ms-user-select: none; /* IE10+ */
|
||||
}
|
||||
|
||||
div.code-block-caption span.caption-number {
|
||||
padding: 0.1em 0.3em;
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
div.code-block-caption span.caption-text {
|
||||
}
|
||||
|
||||
div.literal-block-wrapper {
|
||||
margin: 1em 0;
|
||||
}
|
||||
|
||||
code.xref, a code {
|
||||
background-color: transparent;
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
|
||||
background-color: transparent;
|
||||
}
|
||||
|
||||
.viewcode-link {
|
||||
float: right;
|
||||
}
|
||||
|
||||
.viewcode-back {
|
||||
float: right;
|
||||
font-family: sans-serif;
|
||||
}
|
||||
|
||||
div.viewcode-block:target {
|
||||
margin: -1px -10px;
|
||||
padding: 0 10px;
|
||||
}
|
||||
|
||||
/* -- math display ---------------------------------------------------------- */
|
||||
|
||||
img.math {
|
||||
vertical-align: middle;
|
||||
}
|
||||
|
||||
div.body div.math p {
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
span.eqno {
|
||||
float: right;
|
||||
}
|
||||
|
||||
span.eqno a.headerlink {
|
||||
position: absolute;
|
||||
z-index: 1;
|
||||
}
|
||||
|
||||
div.math:hover a.headerlink {
|
||||
visibility: visible;
|
||||
}
|
||||
|
||||
/* -- printout stylesheet --------------------------------------------------- */
|
||||
|
||||
@media print {
|
||||
div.document,
|
||||
div.documentwrapper,
|
||||
div.bodywrapper {
|
||||
margin: 0 !important;
|
||||
width: 100%;
|
||||
}
|
||||
|
||||
div.sphinxsidebar,
|
||||
div.related,
|
||||
div.footer,
|
||||
#top-link {
|
||||
display: none;
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,506 @@
|
|||
/*
|
||||
* bizstyle.css_t
|
||||
* ~~~~~~~~~~~~~~
|
||||
*
|
||||
* Sphinx stylesheet -- business style theme.
|
||||
*
|
||||
* :copyright: Copyright 2011-2014 by Sphinx team, see AUTHORS.
|
||||
* :license: BSD, see LICENSE for details.
|
||||
*
|
||||
*/
|
||||
|
||||
@import url("basic.css");
|
||||
|
||||
/* -- page layout ----------------------------------------------------------- */
|
||||
|
||||
body {
|
||||
font-family: 'Lucida Grande', 'Lucida Sans Unicode', 'Geneva',
|
||||
'Verdana', sans-serif;
|
||||
font-size: 14px;
|
||||
letter-spacing: -0.01em;
|
||||
line-height: 150%;
|
||||
text-align: center;
|
||||
background-color: white;
|
||||
background-image: url(background_b01.png);
|
||||
color: black;
|
||||
padding: 0;
|
||||
border-right: 1px solid #336699;
|
||||
border-left: 1px solid #336699;
|
||||
|
||||
margin: 0px 40px 0px 40px;
|
||||
}
|
||||
|
||||
div.document {
|
||||
background-color: white;
|
||||
text-align: left;
|
||||
background-repeat: repeat-x;
|
||||
|
||||
-moz-box-shadow: 2px 2px 5px #000;
|
||||
-webkit-box-shadow: 2px 2px 5px #000;
|
||||
}
|
||||
|
||||
div.documentwrapper {
|
||||
float: left;
|
||||
width: 100%;
|
||||
}
|
||||
|
||||
div.bodywrapper {
|
||||
margin: 0 0 0 240px;
|
||||
border-left: 1px solid #ccc;
|
||||
}
|
||||
|
||||
div.body {
|
||||
margin: 0;
|
||||
padding: 0.5em 20px 20px 20px;
|
||||
}
|
||||
div.bodywrapper {
|
||||
margin: 0 0 0 calc(210px + 30px);
|
||||
}
|
||||
|
||||
div.related {
|
||||
font-size: 1em;
|
||||
|
||||
-moz-box-shadow: 2px 2px 5px #000;
|
||||
-webkit-box-shadow: 2px 2px 5px #000;
|
||||
}
|
||||
|
||||
div.related ul {
|
||||
background-color: #336699;
|
||||
height: 100%;
|
||||
overflow: hidden;
|
||||
border-top: 1px solid #ddd;
|
||||
border-bottom: 1px solid #ddd;
|
||||
}
|
||||
|
||||
div.related ul li {
|
||||
color: white;
|
||||
margin: 0;
|
||||
padding: 0;
|
||||
height: 2em;
|
||||
float: left;
|
||||
}
|
||||
|
||||
div.related ul li.right {
|
||||
float: right;
|
||||
margin-right: 5px;
|
||||
}
|
||||
|
||||
div.related ul li a {
|
||||
margin: 0;
|
||||
padding: 0 5px 0 5px;
|
||||
line-height: 1.75em;
|
||||
color: #fff;
|
||||
}
|
||||
|
||||
div.related ul li a:hover {
|
||||
color: #fff;
|
||||
text-decoration: underline;
|
||||
}
|
||||
|
||||
div.sphinxsidebarwrapper {
|
||||
padding: 0;
|
||||
}
|
||||
|
||||
div.sphinxsidebar {
|
||||
padding: 0.5em 12px 12px 12px;
|
||||
width: 210px;
|
||||
font-size: 1em;
|
||||
text-align: left;
|
||||
}
|
||||
|
||||
div.sphinxsidebar h3, div.sphinxsidebar h4 {
|
||||
margin: 1em 0 0.5em 0;
|
||||
font-size: 1em;
|
||||
padding: 0.1em 0 0.1em 0.5em;
|
||||
color: white;
|
||||
border: 1px solid #336699;
|
||||
background-color: #336699;
|
||||
}
|
||||
|
||||
div.sphinxsidebar h3 a {
|
||||
color: white;
|
||||
}
|
||||
|
||||
div.sphinxsidebar ul {
|
||||
padding-left: 1.5em;
|
||||
margin-top: 7px;
|
||||
padding: 0;
|
||||
line-height: 130%;
|
||||
}
|
||||
|
||||
div.sphinxsidebar ul ul {
|
||||
margin-left: 20px;
|
||||
}
|
||||
|
||||
div.sphinxsidebar input {
|
||||
border: 1px solid #336699;
|
||||
}
|
||||
|
||||
div.footer {
|
||||
background-color: white;
|
||||
color: #336699;
|
||||
padding: 3px 8px 3px 0;
|
||||
clear: both;
|
||||
font-size: 0.8em;
|
||||
text-align: right;
|
||||
border-bottom: 1px solid #336699;
|
||||
|
||||
-moz-box-shadow: 2px 2px 5px #000;
|
||||
-webkit-box-shadow: 2px 2px 5px #000;
|
||||
}
|
||||
|
||||
div.footer a {
|
||||
color: #336699;
|
||||
text-decoration: underline;
|
||||
}
|
||||
|
||||
/* -- body styles ----------------------------------------------------------- */
|
||||
|
||||
p {
|
||||
margin: 0.8em 0 0.5em 0;
|
||||
}
|
||||
|
||||
a {
|
||||
color: #336699;
|
||||
text-decoration: none;
|
||||
}
|
||||
|
||||
a:hover {
|
||||
color: #336699;
|
||||
text-decoration: underline;
|
||||
}
|
||||
|
||||
div.body a {
|
||||
text-decoration: underline;
|
||||
}
|
||||
|
||||
h1, h2, h3 {
|
||||
color: #336699;
|
||||
}
|
||||
|
||||
h1 {
|
||||
margin: 0;
|
||||
padding: 0.7em 0 0.3em 0;
|
||||
font-size: 1.5em;
|
||||
}
|
||||
|
||||
h2 {
|
||||
margin: 1.3em 0 0.2em 0;
|
||||
font-size: 1.35em;
|
||||
padding-bottom: .5em;
|
||||
border-bottom: 1px solid #336699;
|
||||
}
|
||||
|
||||
h3 {
|
||||
margin: 1em 0 -0.3em 0;
|
||||
font-size: 1.2em;
|
||||
padding-bottom: .3em;
|
||||
border-bottom: 1px solid #CCCCCC;
|
||||
}
|
||||
|
||||
div.body h1 a, div.body h2 a, div.body h3 a,
|
||||
div.body h4 a, div.body h5 a, div.body h6 a {
|
||||
color: black!important;
|
||||
}
|
||||
|
||||
h1 a.anchor, h2 a.anchor, h3 a.anchor,
|
||||
h4 a.anchor, h5 a.anchor, h6 a.anchor {
|
||||
display: none;
|
||||
margin: 0 0 0 0.3em;
|
||||
padding: 0 0.2em 0 0.2em;
|
||||
color: #aaa!important;
|
||||
}
|
||||
|
||||
h1:hover a.anchor, h2:hover a.anchor, h3:hover a.anchor, h4:hover a.anchor,
|
||||
h5:hover a.anchor, h6:hover a.anchor {
|
||||
display: inline;
|
||||
}
|
||||
|
||||
h1 a.anchor:hover, h2 a.anchor:hover, h3 a.anchor:hover, h4 a.anchor:hover,
|
||||
h5 a.anchor:hover, h6 a.anchor:hover {
|
||||
color: #777;
|
||||
background-color: #eee;
|
||||
}
|
||||
|
||||
a.headerlink {
|
||||
color: #c60f0f!important;
|
||||
font-size: 1em;
|
||||
margin-left: 6px;
|
||||
padding: 0 4px 0 4px;
|
||||
text-decoration: none!important;
|
||||
}
|
||||
|
||||
a.headerlink:hover {
|
||||
background-color: #ccc;
|
||||
color: white!important;
|
||||
}
|
||||
|
||||
cite, code, tt {
|
||||
font-family: 'Consolas', 'Deja Vu Sans Mono',
|
||||
'Bitstream Vera Sans Mono', monospace;
|
||||
font-size: 0.95em;
|
||||
letter-spacing: 0.01em;
|
||||
}
|
||||
|
||||
code {
|
||||
background-color: #F2F2F2;
|
||||
border-bottom: 1px solid #ddd;
|
||||
color: #333;
|
||||
}
|
||||
|
||||
code.descname, code.descclassname, code.xref {
|
||||
border: 0;
|
||||
}
|
||||
|
||||
hr {
|
||||
border: 1px solid #abc;
|
||||
margin: 2em;
|
||||
}
|
||||
|
||||
a code {
|
||||
border: 0;
|
||||
color: #CA7900;
|
||||
}
|
||||
|
||||
a code:hover {
|
||||
color: #2491CF;
|
||||
}
|
||||
|
||||
pre {
|
||||
background-color: transparent !important;
|
||||
font-family: 'Consolas', 'Deja Vu Sans Mono',
|
||||
'Bitstream Vera Sans Mono', monospace;
|
||||
font-size: 0.95em;
|
||||
letter-spacing: 0.015em;
|
||||
line-height: 120%;
|
||||
padding: 0.5em;
|
||||
border-right: 5px solid #ccc;
|
||||
border-left: 5px solid #ccc;
|
||||
}
|
||||
|
||||
pre a {
|
||||
color: inherit;
|
||||
text-decoration: underline;
|
||||
}
|
||||
|
||||
td.linenos pre {
|
||||
padding: 0.5em 0;
|
||||
}
|
||||
|
||||
div.quotebar {
|
||||
background-color: #f8f8f8;
|
||||
max-width: 250px;
|
||||
float: right;
|
||||
padding: 2px 7px;
|
||||
border: 1px solid #ccc;
|
||||
}
|
||||
|
||||
div.topic {
|
||||
background-color: #f8f8f8;
|
||||
}
|
||||
|
||||
table {
|
||||
border-collapse: collapse;
|
||||
margin: 0 -0.5em 0 -0.5em;
|
||||
}
|
||||
|
||||
table td, table th {
|
||||
padding: 0.2em 0.5em 0.2em 0.5em;
|
||||
}
|
||||
|
||||
div.admonition {
|
||||
font-size: 0.9em;
|
||||
margin: 1em 0 1em 0;
|
||||
border: 3px solid #cccccc;
|
||||
background-color: #f7f7f7;
|
||||
padding: 0;
|
||||
}
|
||||
|
||||
div.admonition p {
|
||||
margin: 0.5em 1em 0.5em 1em;
|
||||
padding: 0;
|
||||
}
|
||||
|
||||
div.admonition li p {
|
||||
margin-left: 0;
|
||||
}
|
||||
|
||||
div.admonition pre, div.warning pre {
|
||||
margin: 0;
|
||||
}
|
||||
|
||||
div.highlight {
|
||||
margin: 0.4em 1em;
|
||||
}
|
||||
|
||||
div.admonition p.admonition-title {
|
||||
margin: 0;
|
||||
padding: 0.1em 0 0.1em 0.5em;
|
||||
color: white;
|
||||
border-bottom: 3px solid #cccccc;
|
||||
font-weight: bold;
|
||||
background-color: #165e83;
|
||||
}
|
||||
|
||||
div.danger { border: 3px solid #f0908d; background-color: #f0cfa0; }
|
||||
div.error { border: 3px solid #f0908d; background-color: #ede4cd; }
|
||||
div.warning { border: 3px solid #f8b862; background-color: #f0cfa0; }
|
||||
div.caution { border: 3px solid #f8b862; background-color: #ede4cd; }
|
||||
div.attention { border: 3px solid #f8b862; background-color: #f3f3f3; }
|
||||
div.important { border: 3px solid #f0cfa0; background-color: #ede4cd; }
|
||||
div.note { border: 3px solid #f0cfa0; background-color: #f3f3f3; }
|
||||
div.hint { border: 3px solid #bed2c3; background-color: #f3f3f3; }
|
||||
div.tip { border: 3px solid #bed2c3; background-color: #f3f3f3; }
|
||||
|
||||
div.danger p.admonition-title, div.error p.admonition-title {
|
||||
background-color: #b7282e;
|
||||
border-bottom: 3px solid #f0908d;
|
||||
}
|
||||
|
||||
div.caution p.admonition-title,
|
||||
div.warning p.admonition-title,
|
||||
div.attention p.admonition-title {
|
||||
background-color: #f19072;
|
||||
border-bottom: 3px solid #f8b862;
|
||||
}
|
||||
|
||||
div.note p.admonition-title, div.important p.admonition-title {
|
||||
background-color: #f8b862;
|
||||
border-bottom: 3px solid #f0cfa0;
|
||||
}
|
||||
|
||||
div.hint p.admonition-title, div.tip p.admonition-title {
|
||||
background-color: #7ebea5;
|
||||
border-bottom: 3px solid #bed2c3;
|
||||
}
|
||||
|
||||
div.admonition ul, div.admonition ol,
|
||||
div.warning ul, div.warning ol {
|
||||
margin: 0.1em 0.5em 0.5em 3em;
|
||||
padding: 0;
|
||||
}
|
||||
|
||||
div.versioninfo {
|
||||
margin: 1em 0 0 0;
|
||||
border: 1px solid #ccc;
|
||||
background-color: #DDEAF0;
|
||||
padding: 8px;
|
||||
line-height: 1.3em;
|
||||
font-size: 0.9em;
|
||||
}
|
||||
|
||||
.viewcode-back {
|
||||
font-family: 'Lucida Grande', 'Lucida Sans Unicode', 'Geneva',
|
||||
'Verdana', sans-serif;
|
||||
}
|
||||
|
||||
div.viewcode-block:target {
|
||||
background-color: #f4debf;
|
||||
border-top: 1px solid #ac9;
|
||||
border-bottom: 1px solid #ac9;
|
||||
}
|
||||
|
||||
p.versionchanged span.versionmodified {
|
||||
font-size: 0.9em;
|
||||
margin-right: 0.2em;
|
||||
padding: 0.1em;
|
||||
background-color: #DCE6A0;
|
||||
}
|
||||
|
||||
dl.field-list > dt {
|
||||
color: white;
|
||||
background-color: #82A0BE;
|
||||
}
|
||||
|
||||
dl.field-list > dd {
|
||||
background-color: #f7f7f7;
|
||||
}
|
||||
|
||||
/* -- table styles ---------------------------------------------------------- */
|
||||
|
||||
table.docutils {
|
||||
margin: 1em 0;
|
||||
padding: 0;
|
||||
border: 1px solid white;
|
||||
background-color: #f7f7f7;
|
||||
}
|
||||
|
||||
table.docutils td, table.docutils th {
|
||||
padding: 1px 8px 1px 5px;
|
||||
border-top: 0;
|
||||
border-left: 0;
|
||||
border-right: 1px solid white;
|
||||
border-bottom: 1px solid white;
|
||||
}
|
||||
|
||||
table.docutils td p {
|
||||
margin-top: 0;
|
||||
margin-bottom: 0.3em;
|
||||
}
|
||||
|
||||
table.field-list td, table.field-list th {
|
||||
border: 0 !important;
|
||||
word-break: break-word;
|
||||
}
|
||||
|
||||
table.footnote td, table.footnote th {
|
||||
border: 0 !important;
|
||||
}
|
||||
|
||||
th {
|
||||
color: white;
|
||||
text-align: left;
|
||||
padding-right: 5px;
|
||||
background-color: #82A0BE;
|
||||
}
|
||||
|
||||
div.literal-block-wrapper div.code-block-caption {
|
||||
background-color: #EEE;
|
||||
border-style: solid;
|
||||
border-color: #CCC;
|
||||
border-width: 1px 5px;
|
||||
}
|
||||
|
||||
/* WIDE DESKTOP STYLE */
|
||||
@media only screen and (min-width: 1176px) {
|
||||
body {
|
||||
margin: 0 40px 0 40px;
|
||||
}
|
||||
}
|
||||
|
||||
/* TABLET STYLE */
|
||||
@media only screen and (min-width: 768px) and (max-width: 991px) {
|
||||
body {
|
||||
margin: 0 40px 0 40px;
|
||||
}
|
||||
}
|
||||
|
||||
/* MOBILE LAYOUT (PORTRAIT/320px) */
|
||||
@media only screen and (max-width: 767px) {
|
||||
body {
|
||||
margin: 0;
|
||||
}
|
||||
div.bodywrapper {
|
||||
margin: 0;
|
||||
width: 100%;
|
||||
border: none;
|
||||
}
|
||||
div.sphinxsidebar {
|
||||
display: none;
|
||||
}
|
||||
}
|
||||
|
||||
/* MOBILE LAYOUT (LANDSCAPE/480px) */
|
||||
@media only screen and (min-width: 480px) and (max-width: 767px) {
|
||||
body {
|
||||
margin: 0 20px 0 20px;
|
||||
}
|
||||
}
|
||||
|
||||
/* RETINA OVERRIDES */
|
||||
@media
|
||||
only screen and (-webkit-min-device-pixel-ratio: 2),
|
||||
only screen and (min-device-pixel-ratio: 2) {
|
||||
}
|
||||
|
||||
/* -- end ------------------------------------------------------------------- */
|
||||
|
|
@ -0,0 +1,41 @@
|
|||
//
|
||||
// bizstyle.js
|
||||
// ~~~~~~~~~~~
|
||||
//
|
||||
// Sphinx javascript -- for bizstyle theme.
|
||||
//
|
||||
// This theme was created by referring to 'sphinxdoc'
|
||||
//
|
||||
// :copyright: Copyright 2012-2014 by Sphinx team, see AUTHORS.
|
||||
// :license: BSD, see LICENSE for details.
|
||||
//
|
||||
$(document).ready(function(){
|
||||
if (navigator.userAgent.indexOf('iPhone') > 0 ||
|
||||
navigator.userAgent.indexOf('Android') > 0) {
|
||||
$("li.nav-item-0 a").text("Top");
|
||||
}
|
||||
|
||||
$("div.related:first ul li:not(.right) a").slice(1).each(function(i, item){
|
||||
if (item.text.length > 20) {
|
||||
var tmpstr = item.text
|
||||
$(item).attr("title", tmpstr);
|
||||
$(item).text(tmpstr.substr(0, 17) + "...");
|
||||
}
|
||||
});
|
||||
$("div.related:last ul li:not(.right) a").slice(1).each(function(i, item){
|
||||
if (item.text.length > 20) {
|
||||
var tmpstr = item.text
|
||||
$(item).attr("title", tmpstr);
|
||||
$(item).text(tmpstr.substr(0, 17) + "...");
|
||||
}
|
||||
});
|
||||
});
|
||||
|
||||
$(window).resize(function(){
|
||||
if ($(window).width() <= 776) {
|
||||
$("li.nav-item-0 a").text("Top");
|
||||
}
|
||||
else {
|
||||
$("li.nav-item-0 a").text("QuaPy 0.1.6 documentation");
|
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|
||||
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|
||||
File diff suppressed because one or more lines are too long
File diff suppressed because it is too large
Load Diff
|
|
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|
|||
/* This file intentionally left blank. */
|
||||
|
|
@ -0,0 +1,323 @@
|
|||
/*
|
||||
* doctools.js
|
||||
* ~~~~~~~~~~~
|
||||
*
|
||||
* Sphinx JavaScript utilities for all documentation.
|
||||
*
|
||||
* :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
|
||||
* :license: BSD, see LICENSE for details.
|
||||
*
|
||||
*/
|
||||
|
||||
/**
|
||||
* select a different prefix for underscore
|
||||
*/
|
||||
$u = _.noConflict();
|
||||
|
||||
/**
|
||||
* make the code below compatible with browsers without
|
||||
* an installed firebug like debugger
|
||||
if (!window.console || !console.firebug) {
|
||||
var names = ["log", "debug", "info", "warn", "error", "assert", "dir",
|
||||
"dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace",
|
||||
"profile", "profileEnd"];
|
||||
window.console = {};
|
||||
for (var i = 0; i < names.length; ++i)
|
||||
window.console[names[i]] = function() {};
|
||||
}
|
||||
*/
|
||||
|
||||
/**
|
||||
* small helper function to urldecode strings
|
||||
*
|
||||
* See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
|
||||
*/
|
||||
jQuery.urldecode = function(x) {
|
||||
if (!x) {
|
||||
return x
|
||||
}
|
||||
return decodeURIComponent(x.replace(/\+/g, ' '));
|
||||
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|
||||
|
||||
/**
|
||||
* small helper function to urlencode strings
|
||||
*/
|
||||
jQuery.urlencode = encodeURIComponent;
|
||||
|
||||
/**
|
||||
* This function returns the parsed url parameters of the
|
||||
* current request. Multiple values per key are supported,
|
||||
* it will always return arrays of strings for the value parts.
|
||||
*/
|
||||
jQuery.getQueryParameters = function(s) {
|
||||
if (typeof s === 'undefined')
|
||||
s = document.location.search;
|
||||
var parts = s.substr(s.indexOf('?') + 1).split('&');
|
||||
var result = {};
|
||||
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|
||||
var tmp = parts[i].split('=', 2);
|
||||
var key = jQuery.urldecode(tmp[0]);
|
||||
var value = jQuery.urldecode(tmp[1]);
|
||||
if (key in result)
|
||||
result[key].push(value);
|
||||
else
|
||||
result[key] = [value];
|
||||
}
|
||||
return result;
|
||||
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|
||||
|
||||
/**
|
||||
* highlight a given string on a jquery object by wrapping it in
|
||||
* span elements with the given class name.
|
||||
*/
|
||||
jQuery.fn.highlightText = function(text, className) {
|
||||
function highlight(node, addItems) {
|
||||
if (node.nodeType === 3) {
|
||||
var val = node.nodeValue;
|
||||
var pos = val.toLowerCase().indexOf(text);
|
||||
if (pos >= 0 &&
|
||||
!jQuery(node.parentNode).hasClass(className) &&
|
||||
!jQuery(node.parentNode).hasClass("nohighlight")) {
|
||||
var span;
|
||||
var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
|
||||
if (isInSVG) {
|
||||
span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
|
||||
} else {
|
||||
span = document.createElement("span");
|
||||
span.className = className;
|
||||
}
|
||||
span.appendChild(document.createTextNode(val.substr(pos, text.length)));
|
||||
node.parentNode.insertBefore(span, node.parentNode.insertBefore(
|
||||
document.createTextNode(val.substr(pos + text.length)),
|
||||
node.nextSibling));
|
||||
node.nodeValue = val.substr(0, pos);
|
||||
if (isInSVG) {
|
||||
var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
|
||||
var bbox = node.parentElement.getBBox();
|
||||
rect.x.baseVal.value = bbox.x;
|
||||
rect.y.baseVal.value = bbox.y;
|
||||
rect.width.baseVal.value = bbox.width;
|
||||
rect.height.baseVal.value = bbox.height;
|
||||
rect.setAttribute('class', className);
|
||||
addItems.push({
|
||||
"parent": node.parentNode,
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||||
"target": rect});
|
||||
}
|
||||
}
|
||||
}
|
||||
else if (!jQuery(node).is("button, select, textarea")) {
|
||||
jQuery.each(node.childNodes, function() {
|
||||
highlight(this, addItems);
|
||||
});
|
||||
}
|
||||
}
|
||||
var addItems = [];
|
||||
var result = this.each(function() {
|
||||
highlight(this, addItems);
|
||||
});
|
||||
for (var i = 0; i < addItems.length; ++i) {
|
||||
jQuery(addItems[i].parent).before(addItems[i].target);
|
||||
}
|
||||
return result;
|
||||
};
|
||||
|
||||
/*
|
||||
* backward compatibility for jQuery.browser
|
||||
* This will be supported until firefox bug is fixed.
|
||||
*/
|
||||
if (!jQuery.browser) {
|
||||
jQuery.uaMatch = function(ua) {
|
||||
ua = ua.toLowerCase();
|
||||
|
||||
var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
|
||||
/(webkit)[ \/]([\w.]+)/.exec(ua) ||
|
||||
/(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
|
||||
/(msie) ([\w.]+)/.exec(ua) ||
|
||||
ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
|
||||
[];
|
||||
|
||||
return {
|
||||
browser: match[ 1 ] || "",
|
||||
version: match[ 2 ] || "0"
|
||||
};
|
||||
};
|
||||
jQuery.browser = {};
|
||||
jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
|
||||
}
|
||||
|
||||
/**
|
||||
* Small JavaScript module for the documentation.
|
||||
*/
|
||||
var Documentation = {
|
||||
|
||||
init : function() {
|
||||
this.fixFirefoxAnchorBug();
|
||||
this.highlightSearchWords();
|
||||
this.initIndexTable();
|
||||
if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) {
|
||||
this.initOnKeyListeners();
|
||||
}
|
||||
},
|
||||
|
||||
/**
|
||||
* i18n support
|
||||
*/
|
||||
TRANSLATIONS : {},
|
||||
PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; },
|
||||
LOCALE : 'unknown',
|
||||
|
||||
// gettext and ngettext don't access this so that the functions
|
||||
// can safely bound to a different name (_ = Documentation.gettext)
|
||||
gettext : function(string) {
|
||||
var translated = Documentation.TRANSLATIONS[string];
|
||||
if (typeof translated === 'undefined')
|
||||
return string;
|
||||
return (typeof translated === 'string') ? translated : translated[0];
|
||||
},
|
||||
|
||||
ngettext : function(singular, plural, n) {
|
||||
var translated = Documentation.TRANSLATIONS[singular];
|
||||
if (typeof translated === 'undefined')
|
||||
return (n == 1) ? singular : plural;
|
||||
return translated[Documentation.PLURALEXPR(n)];
|
||||
},
|
||||
|
||||
addTranslations : function(catalog) {
|
||||
for (var key in catalog.messages)
|
||||
this.TRANSLATIONS[key] = catalog.messages[key];
|
||||
this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')');
|
||||
this.LOCALE = catalog.locale;
|
||||
},
|
||||
|
||||
/**
|
||||
* add context elements like header anchor links
|
||||
*/
|
||||
addContextElements : function() {
|
||||
$('div[id] > :header:first').each(function() {
|
||||
$('<a class="headerlink">\u00B6</a>').
|
||||
attr('href', '#' + this.id).
|
||||
attr('title', _('Permalink to this headline')).
|
||||
appendTo(this);
|
||||
});
|
||||
$('dt[id]').each(function() {
|
||||
$('<a class="headerlink">\u00B6</a>').
|
||||
attr('href', '#' + this.id).
|
||||
attr('title', _('Permalink to this definition')).
|
||||
appendTo(this);
|
||||
});
|
||||
},
|
||||
|
||||
/**
|
||||
* workaround a firefox stupidity
|
||||
* see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075
|
||||
*/
|
||||
fixFirefoxAnchorBug : function() {
|
||||
if (document.location.hash && $.browser.mozilla)
|
||||
window.setTimeout(function() {
|
||||
document.location.href += '';
|
||||
}, 10);
|
||||
},
|
||||
|
||||
/**
|
||||
* highlight the search words provided in the url in the text
|
||||
*/
|
||||
highlightSearchWords : function() {
|
||||
var params = $.getQueryParameters();
|
||||
var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : [];
|
||||
if (terms.length) {
|
||||
var body = $('div.body');
|
||||
if (!body.length) {
|
||||
body = $('body');
|
||||
}
|
||||
window.setTimeout(function() {
|
||||
$.each(terms, function() {
|
||||
body.highlightText(this.toLowerCase(), 'highlighted');
|
||||
});
|
||||
}, 10);
|
||||
$('<p class="highlight-link"><a href="javascript:Documentation.' +
|
||||
'hideSearchWords()">' + _('Hide Search Matches') + '</a></p>')
|
||||
.appendTo($('#searchbox'));
|
||||
}
|
||||
},
|
||||
|
||||
/**
|
||||
* init the domain index toggle buttons
|
||||
*/
|
||||
initIndexTable : function() {
|
||||
var togglers = $('img.toggler').click(function() {
|
||||
var src = $(this).attr('src');
|
||||
var idnum = $(this).attr('id').substr(7);
|
||||
$('tr.cg-' + idnum).toggle();
|
||||
if (src.substr(-9) === 'minus.png')
|
||||
$(this).attr('src', src.substr(0, src.length-9) + 'plus.png');
|
||||
else
|
||||
$(this).attr('src', src.substr(0, src.length-8) + 'minus.png');
|
||||
}).css('display', '');
|
||||
if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) {
|
||||
togglers.click();
|
||||
}
|
||||
},
|
||||
|
||||
/**
|
||||
* helper function to hide the search marks again
|
||||
*/
|
||||
hideSearchWords : function() {
|
||||
$('#searchbox .highlight-link').fadeOut(300);
|
||||
$('span.highlighted').removeClass('highlighted');
|
||||
},
|
||||
|
||||
/**
|
||||
* make the url absolute
|
||||
*/
|
||||
makeURL : function(relativeURL) {
|
||||
return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL;
|
||||
},
|
||||
|
||||
/**
|
||||
* get the current relative url
|
||||
*/
|
||||
getCurrentURL : function() {
|
||||
var path = document.location.pathname;
|
||||
var parts = path.split(/\//);
|
||||
$.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() {
|
||||
if (this === '..')
|
||||
parts.pop();
|
||||
});
|
||||
var url = parts.join('/');
|
||||
return path.substring(url.lastIndexOf('/') + 1, path.length - 1);
|
||||
},
|
||||
|
||||
initOnKeyListeners: function() {
|
||||
$(document).keydown(function(event) {
|
||||
var activeElementType = document.activeElement.tagName;
|
||||
// don't navigate when in search box, textarea, dropdown or button
|
||||
if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'
|
||||
&& activeElementType !== 'BUTTON' && !event.altKey && !event.ctrlKey && !event.metaKey
|
||||
&& !event.shiftKey) {
|
||||
switch (event.keyCode) {
|
||||
case 37: // left
|
||||
var prevHref = $('link[rel="prev"]').prop('href');
|
||||
if (prevHref) {
|
||||
window.location.href = prevHref;
|
||||
return false;
|
||||
}
|
||||
break;
|
||||
case 39: // right
|
||||
var nextHref = $('link[rel="next"]').prop('href');
|
||||
if (nextHref) {
|
||||
window.location.href = nextHref;
|
||||
return false;
|
||||
}
|
||||
break;
|
||||
}
|
||||
}
|
||||
});
|
||||
}
|
||||
};
|
||||
|
||||
// quick alias for translations
|
||||
_ = Documentation.gettext;
|
||||
|
||||
$(document).ready(function() {
|
||||
Documentation.init();
|
||||
});
|
||||
|
|
@ -0,0 +1,12 @@
|
|||
var DOCUMENTATION_OPTIONS = {
|
||||
URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
|
||||
VERSION: '0.1.6',
|
||||
LANGUAGE: 'None',
|
||||
COLLAPSE_INDEX: false,
|
||||
BUILDER: 'html',
|
||||
FILE_SUFFIX: '.html',
|
||||
LINK_SUFFIX: '.html',
|
||||
HAS_SOURCE: true,
|
||||
SOURCELINK_SUFFIX: '.txt',
|
||||
NAVIGATION_WITH_KEYS: false
|
||||
};
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 286 B |
File diff suppressed because it is too large
Load Diff
File diff suppressed because one or more lines are too long
|
|
@ -0,0 +1,297 @@
|
|||
/*
|
||||
* language_data.js
|
||||
* ~~~~~~~~~~~~~~~~
|
||||
*
|
||||
* This script contains the language-specific data used by searchtools.js,
|
||||
* namely the list of stopwords, stemmer, scorer and splitter.
|
||||
*
|
||||
* :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
|
||||
* :license: BSD, see LICENSE for details.
|
||||
*
|
||||
*/
|
||||
|
||||
var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"];
|
||||
|
||||
|
||||
/* Non-minified version is copied as a separate JS file, is available */
|
||||
|
||||
/**
|
||||
* Porter Stemmer
|
||||
*/
|
||||
var Stemmer = function() {
|
||||
|
||||
var step2list = {
|
||||
ational: 'ate',
|
||||
tional: 'tion',
|
||||
enci: 'ence',
|
||||
anci: 'ance',
|
||||
izer: 'ize',
|
||||
bli: 'ble',
|
||||
alli: 'al',
|
||||
entli: 'ent',
|
||||
eli: 'e',
|
||||
ousli: 'ous',
|
||||
ization: 'ize',
|
||||
ation: 'ate',
|
||||
ator: 'ate',
|
||||
alism: 'al',
|
||||
iveness: 'ive',
|
||||
fulness: 'ful',
|
||||
ousness: 'ous',
|
||||
aliti: 'al',
|
||||
iviti: 'ive',
|
||||
biliti: 'ble',
|
||||
logi: 'log'
|
||||
};
|
||||
|
||||
var step3list = {
|
||||
icate: 'ic',
|
||||
ative: '',
|
||||
alize: 'al',
|
||||
iciti: 'ic',
|
||||
ical: 'ic',
|
||||
ful: '',
|
||||
ness: ''
|
||||
};
|
||||
|
||||
var c = "[^aeiou]"; // consonant
|
||||
var v = "[aeiouy]"; // vowel
|
||||
var C = c + "[^aeiouy]*"; // consonant sequence
|
||||
var V = v + "[aeiou]*"; // vowel sequence
|
||||
|
||||
var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0
|
||||
var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1
|
||||
var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1
|
||||
var s_v = "^(" + C + ")?" + v; // vowel in stem
|
||||
|
||||
this.stemWord = function (w) {
|
||||
var stem;
|
||||
var suffix;
|
||||
var firstch;
|
||||
var origword = w;
|
||||
|
||||
if (w.length < 3)
|
||||
return w;
|
||||
|
||||
var re;
|
||||
var re2;
|
||||
var re3;
|
||||
var re4;
|
||||
|
||||
firstch = w.substr(0,1);
|
||||
if (firstch == "y")
|
||||
w = firstch.toUpperCase() + w.substr(1);
|
||||
|
||||
// Step 1a
|
||||
re = /^(.+?)(ss|i)es$/;
|
||||
re2 = /^(.+?)([^s])s$/;
|
||||
|
||||
if (re.test(w))
|
||||
w = w.replace(re,"$1$2");
|
||||
else if (re2.test(w))
|
||||
w = w.replace(re2,"$1$2");
|
||||
|
||||
// Step 1b
|
||||
re = /^(.+?)eed$/;
|
||||
re2 = /^(.+?)(ed|ing)$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
re = new RegExp(mgr0);
|
||||
if (re.test(fp[1])) {
|
||||
re = /.$/;
|
||||
w = w.replace(re,"");
|
||||
}
|
||||
}
|
||||
else if (re2.test(w)) {
|
||||
var fp = re2.exec(w);
|
||||
stem = fp[1];
|
||||
re2 = new RegExp(s_v);
|
||||
if (re2.test(stem)) {
|
||||
w = stem;
|
||||
re2 = /(at|bl|iz)$/;
|
||||
re3 = new RegExp("([^aeiouylsz])\\1$");
|
||||
re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
|
||||
if (re2.test(w))
|
||||
w = w + "e";
|
||||
else if (re3.test(w)) {
|
||||
re = /.$/;
|
||||
w = w.replace(re,"");
|
||||
}
|
||||
else if (re4.test(w))
|
||||
w = w + "e";
|
||||
}
|
||||
}
|
||||
|
||||
// Step 1c
|
||||
re = /^(.+?)y$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
re = new RegExp(s_v);
|
||||
if (re.test(stem))
|
||||
w = stem + "i";
|
||||
}
|
||||
|
||||
// Step 2
|
||||
re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
suffix = fp[2];
|
||||
re = new RegExp(mgr0);
|
||||
if (re.test(stem))
|
||||
w = stem + step2list[suffix];
|
||||
}
|
||||
|
||||
// Step 3
|
||||
re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
suffix = fp[2];
|
||||
re = new RegExp(mgr0);
|
||||
if (re.test(stem))
|
||||
w = stem + step3list[suffix];
|
||||
}
|
||||
|
||||
// Step 4
|
||||
re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
|
||||
re2 = /^(.+?)(s|t)(ion)$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
re = new RegExp(mgr1);
|
||||
if (re.test(stem))
|
||||
w = stem;
|
||||
}
|
||||
else if (re2.test(w)) {
|
||||
var fp = re2.exec(w);
|
||||
stem = fp[1] + fp[2];
|
||||
re2 = new RegExp(mgr1);
|
||||
if (re2.test(stem))
|
||||
w = stem;
|
||||
}
|
||||
|
||||
// Step 5
|
||||
re = /^(.+?)e$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
re = new RegExp(mgr1);
|
||||
re2 = new RegExp(meq1);
|
||||
re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
|
||||
if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
|
||||
w = stem;
|
||||
}
|
||||
re = /ll$/;
|
||||
re2 = new RegExp(mgr1);
|
||||
if (re.test(w) && re2.test(w)) {
|
||||
re = /.$/;
|
||||
w = w.replace(re,"");
|
||||
}
|
||||
|
||||
// and turn initial Y back to y
|
||||
if (firstch == "y")
|
||||
w = firstch.toLowerCase() + w.substr(1);
|
||||
return w;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
var splitChars = (function() {
|
||||
var result = {};
|
||||
var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648,
|
||||
1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702,
|
||||
2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971,
|
||||
2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345,
|
||||
3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761,
|
||||
3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823,
|
||||
4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125,
|
||||
8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695,
|
||||
11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587,
|
||||
43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141];
|
||||
var i, j, start, end;
|
||||
for (i = 0; i < singles.length; i++) {
|
||||
result[singles[i]] = true;
|
||||
}
|
||||
var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709],
|
||||
[722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161],
|
||||
[1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568],
|
||||
[1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807],
|
||||
[1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047],
|
||||
[2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383],
|
||||
[2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450],
|
||||
[2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547],
|
||||
[2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673],
|
||||
[2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820],
|
||||
[2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946],
|
||||
[2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023],
|
||||
[3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173],
|
||||
[3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332],
|
||||
[3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481],
|
||||
[3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718],
|
||||
[3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791],
|
||||
[3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095],
|
||||
[4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205],
|
||||
[4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687],
|
||||
[4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968],
|
||||
[4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869],
|
||||
[5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102],
|
||||
[6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271],
|
||||
[6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592],
|
||||
[6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822],
|
||||
[6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167],
|
||||
[7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959],
|
||||
[7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143],
|
||||
[8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318],
|
||||
[8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483],
|
||||
[8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101],
|
||||
[10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567],
|
||||
[11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292],
|
||||
[12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444],
|
||||
[12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783],
|
||||
[12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311],
|
||||
[19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511],
|
||||
[42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774],
|
||||
[42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071],
|
||||
[43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263],
|
||||
[43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519],
|
||||
[43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647],
|
||||
[43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967],
|
||||
[44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295],
|
||||
[57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274],
|
||||
[64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007],
|
||||
[65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381],
|
||||
[65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]];
|
||||
for (i = 0; i < ranges.length; i++) {
|
||||
start = ranges[i][0];
|
||||
end = ranges[i][1];
|
||||
for (j = start; j <= end; j++) {
|
||||
result[j] = true;
|
||||
}
|
||||
}
|
||||
return result;
|
||||
})();
|
||||
|
||||
function splitQuery(query) {
|
||||
var result = [];
|
||||
var start = -1;
|
||||
for (var i = 0; i < query.length; i++) {
|
||||
if (splitChars[query.charCodeAt(i)]) {
|
||||
if (start !== -1) {
|
||||
result.push(query.slice(start, i));
|
||||
start = -1;
|
||||
}
|
||||
} else if (start === -1) {
|
||||
start = i;
|
||||
}
|
||||
}
|
||||
if (start !== -1) {
|
||||
result.push(query.slice(start));
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 90 B |
Binary file not shown.
|
After Width: | Height: | Size: 90 B |
|
|
@ -0,0 +1,74 @@
|
|||
pre { line-height: 125%; }
|
||||
td.linenos .normal { color: #666666; background-color: transparent; padding-left: 5px; padding-right: 5px; }
|
||||
span.linenos { color: #666666; background-color: transparent; padding-left: 5px; padding-right: 5px; }
|
||||
td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
|
||||
span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
|
||||
.highlight .hll { background-color: #ffffcc }
|
||||
.highlight { background: #f0f0f0; }
|
||||
.highlight .c { color: #60a0b0; font-style: italic } /* Comment */
|
||||
.highlight .err { border: 1px solid #FF0000 } /* Error */
|
||||
.highlight .k { color: #007020; font-weight: bold } /* Keyword */
|
||||
.highlight .o { color: #666666 } /* Operator */
|
||||
.highlight .ch { color: #60a0b0; font-style: italic } /* Comment.Hashbang */
|
||||
.highlight .cm { color: #60a0b0; font-style: italic } /* Comment.Multiline */
|
||||
.highlight .cp { color: #007020 } /* Comment.Preproc */
|
||||
.highlight .cpf { color: #60a0b0; font-style: italic } /* Comment.PreprocFile */
|
||||
.highlight .c1 { color: #60a0b0; font-style: italic } /* Comment.Single */
|
||||
.highlight .cs { color: #60a0b0; background-color: #fff0f0 } /* Comment.Special */
|
||||
.highlight .gd { color: #A00000 } /* Generic.Deleted */
|
||||
.highlight .ge { font-style: italic } /* Generic.Emph */
|
||||
.highlight .gr { color: #FF0000 } /* Generic.Error */
|
||||
.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
|
||||
.highlight .gi { color: #00A000 } /* Generic.Inserted */
|
||||
.highlight .go { color: #888888 } /* Generic.Output */
|
||||
.highlight .gp { color: #c65d09; font-weight: bold } /* Generic.Prompt */
|
||||
.highlight .gs { font-weight: bold } /* Generic.Strong */
|
||||
.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
|
||||
.highlight .gt { color: #0044DD } /* Generic.Traceback */
|
||||
.highlight .kc { color: #007020; font-weight: bold } /* Keyword.Constant */
|
||||
.highlight .kd { color: #007020; font-weight: bold } /* Keyword.Declaration */
|
||||
.highlight .kn { color: #007020; font-weight: bold } /* Keyword.Namespace */
|
||||
.highlight .kp { color: #007020 } /* Keyword.Pseudo */
|
||||
.highlight .kr { color: #007020; font-weight: bold } /* Keyword.Reserved */
|
||||
.highlight .kt { color: #902000 } /* Keyword.Type */
|
||||
.highlight .m { color: #40a070 } /* Literal.Number */
|
||||
.highlight .s { color: #4070a0 } /* Literal.String */
|
||||
.highlight .na { color: #4070a0 } /* Name.Attribute */
|
||||
.highlight .nb { color: #007020 } /* Name.Builtin */
|
||||
.highlight .nc { color: #0e84b5; font-weight: bold } /* Name.Class */
|
||||
.highlight .no { color: #60add5 } /* Name.Constant */
|
||||
.highlight .nd { color: #555555; font-weight: bold } /* Name.Decorator */
|
||||
.highlight .ni { color: #d55537; font-weight: bold } /* Name.Entity */
|
||||
.highlight .ne { color: #007020 } /* Name.Exception */
|
||||
.highlight .nf { color: #06287e } /* Name.Function */
|
||||
.highlight .nl { color: #002070; font-weight: bold } /* Name.Label */
|
||||
.highlight .nn { color: #0e84b5; font-weight: bold } /* Name.Namespace */
|
||||
.highlight .nt { color: #062873; font-weight: bold } /* Name.Tag */
|
||||
.highlight .nv { color: #bb60d5 } /* Name.Variable */
|
||||
.highlight .ow { color: #007020; font-weight: bold } /* Operator.Word */
|
||||
.highlight .w { color: #bbbbbb } /* Text.Whitespace */
|
||||
.highlight .mb { color: #40a070 } /* Literal.Number.Bin */
|
||||
.highlight .mf { color: #40a070 } /* Literal.Number.Float */
|
||||
.highlight .mh { color: #40a070 } /* Literal.Number.Hex */
|
||||
.highlight .mi { color: #40a070 } /* Literal.Number.Integer */
|
||||
.highlight .mo { color: #40a070 } /* Literal.Number.Oct */
|
||||
.highlight .sa { color: #4070a0 } /* Literal.String.Affix */
|
||||
.highlight .sb { color: #4070a0 } /* Literal.String.Backtick */
|
||||
.highlight .sc { color: #4070a0 } /* Literal.String.Char */
|
||||
.highlight .dl { color: #4070a0 } /* Literal.String.Delimiter */
|
||||
.highlight .sd { color: #4070a0; font-style: italic } /* Literal.String.Doc */
|
||||
.highlight .s2 { color: #4070a0 } /* Literal.String.Double */
|
||||
.highlight .se { color: #4070a0; font-weight: bold } /* Literal.String.Escape */
|
||||
.highlight .sh { color: #4070a0 } /* Literal.String.Heredoc */
|
||||
.highlight .si { color: #70a0d0; font-style: italic } /* Literal.String.Interpol */
|
||||
.highlight .sx { color: #c65d09 } /* Literal.String.Other */
|
||||
.highlight .sr { color: #235388 } /* Literal.String.Regex */
|
||||
.highlight .s1 { color: #4070a0 } /* Literal.String.Single */
|
||||
.highlight .ss { color: #517918 } /* Literal.String.Symbol */
|
||||
.highlight .bp { color: #007020 } /* Name.Builtin.Pseudo */
|
||||
.highlight .fm { color: #06287e } /* Name.Function.Magic */
|
||||
.highlight .vc { color: #bb60d5 } /* Name.Variable.Class */
|
||||
.highlight .vg { color: #bb60d5 } /* Name.Variable.Global */
|
||||
.highlight .vi { color: #bb60d5 } /* Name.Variable.Instance */
|
||||
.highlight .vm { color: #bb60d5 } /* Name.Variable.Magic */
|
||||
.highlight .il { color: #40a070 } /* Literal.Number.Integer.Long */
|
||||
|
|
@ -0,0 +1,528 @@
|
|||
/*
|
||||
* searchtools.js
|
||||
* ~~~~~~~~~~~~~~~~
|
||||
*
|
||||
* Sphinx JavaScript utilities for the full-text search.
|
||||
*
|
||||
* :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
|
||||
* :license: BSD, see LICENSE for details.
|
||||
*
|
||||
*/
|
||||
|
||||
if (!Scorer) {
|
||||
/**
|
||||
* Simple result scoring code.
|
||||
*/
|
||||
var Scorer = {
|
||||
// Implement the following function to further tweak the score for each result
|
||||
// The function takes a result array [filename, title, anchor, descr, score]
|
||||
// and returns the new score.
|
||||
/*
|
||||
score: function(result) {
|
||||
return result[4];
|
||||
},
|
||||
*/
|
||||
|
||||
// query matches the full name of an object
|
||||
objNameMatch: 11,
|
||||
// or matches in the last dotted part of the object name
|
||||
objPartialMatch: 6,
|
||||
// Additive scores depending on the priority of the object
|
||||
objPrio: {0: 15, // used to be importantResults
|
||||
1: 5, // used to be objectResults
|
||||
2: -5}, // used to be unimportantResults
|
||||
// Used when the priority is not in the mapping.
|
||||
objPrioDefault: 0,
|
||||
|
||||
// query found in title
|
||||
title: 15,
|
||||
partialTitle: 7,
|
||||
// query found in terms
|
||||
term: 5,
|
||||
partialTerm: 2
|
||||
};
|
||||
}
|
||||
|
||||
if (!splitQuery) {
|
||||
function splitQuery(query) {
|
||||
return query.split(/\s+/);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Search Module
|
||||
*/
|
||||
var Search = {
|
||||
|
||||
_index : null,
|
||||
_queued_query : null,
|
||||
_pulse_status : -1,
|
||||
|
||||
htmlToText : function(htmlString) {
|
||||
var virtualDocument = document.implementation.createHTMLDocument('virtual');
|
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var htmlElement = $(htmlString, virtualDocument);
|
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htmlElement.find('.headerlink').remove();
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docContent = htmlElement.find('[role=main]')[0];
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if(docContent === undefined) {
|
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console.warn("Content block not found. Sphinx search tries to obtain it " +
|
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"via '[role=main]'. Could you check your theme or template.");
|
||||
return "";
|
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|
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return docContent.textContent || docContent.innerText;
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|
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|
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$('input[name="q"]')[0].value = query;
|
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this.performSearch(query);
|
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}
|
||||
},
|
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|
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loadIndex : function(url) {
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$.ajax({type: "GET", url: url, data: null,
|
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dataType: "script", cache: true,
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complete: function(jqxhr, textstatus) {
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if (textstatus != "success") {
|
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document.getElementById("searchindexloader").src = url;
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}});
|
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setIndex : function(index) {
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var q;
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this._index = index;
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|
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this._queued_query = null;
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Search.query(q);
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},
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hasIndex : function() {
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|
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|
||||
deferQuery : function(query) {
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this._queued_query = query;
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},
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stopPulse : function() {
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this._pulse_status = 0;
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},
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startPulse : function() {
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if (this._pulse_status >= 0)
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return;
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function pulse() {
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var i;
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Search._pulse_status = (Search._pulse_status + 1) % 4;
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var dotString = '';
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dotString += '.';
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Search.dots.text(dotString);
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if (Search._pulse_status > -1)
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window.setTimeout(pulse, 500);
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pulse();
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/**
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* perform a search for something (or wait until index is loaded)
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*/
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performSearch : function(query) {
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this.out = $('#search-results');
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this.title = $('<h2>' + _('Searching') + '</h2>').appendTo(this.out);
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this.dots = $('<span></span>').appendTo(this.title);
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this.status = $('<p class="search-summary"> </p>').appendTo(this.out);
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this.output = $('<ul class="search"/>').appendTo(this.out);
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$('#search-progress').text(_('Preparing search...'));
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this.startPulse();
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||||
// index already loaded, the browser was quick!
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if (this.hasIndex())
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this.query(query);
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else
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this.deferQuery(query);
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/**
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* execute search (requires search index to be loaded)
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*/
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query : function(query) {
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var i;
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// stem the searchterms and add them to the correct list
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var hlterms = [];
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var tmp = splitQuery(query);
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if (tmp[i] !== "") {
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objectterms.push(tmp[i].toLowerCase());
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continue;
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// prevent stemmer from cutting word smaller than two chars
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var toAppend;
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// select the correct list
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if (word[0] == '-') {
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toAppend = excluded;
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word = word.substr(1);
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}
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else {
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toAppend = searchterms;
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hlterms.push(tmp[i].toLowerCase());
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// only add if not already in the list
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if (!$u.contains(toAppend, word))
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toAppend.push(word);
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}
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var highlightstring = '?highlight=' + $.urlencode(hlterms.join(" "));
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// console.debug('SEARCH: searching for:');
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// console.info('required: ', searchterms);
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// console.info('excluded: ', excluded);
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// prepare search
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var terms = this._index.terms;
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var titleterms = this._index.titleterms;
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// array of [filename, title, anchor, descr, score]
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var results = [];
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$('#search-progress').empty();
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// lookup as object
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for (i = 0; i < objectterms.length; i++) {
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var others = [].concat(objectterms.slice(0, i),
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objectterms.slice(i+1, objectterms.length));
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results = results.concat(this.performObjectSearch(objectterms[i], others));
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}
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||||
|
||||
// lookup as search terms in fulltext
|
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results = results.concat(this.performTermsSearch(searchterms, excluded, terms, titleterms));
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||||
|
||||
// let the scorer override scores with a custom scoring function
|
||||
if (Scorer.score) {
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for (i = 0; i < results.length; i++)
|
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results[i][4] = Scorer.score(results[i]);
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}
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|
||||
// now sort the results by score (in opposite order of appearance, since the
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// display function below uses pop() to retrieve items) and then
|
||||
// alphabetically
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results.sort(function(a, b) {
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var left = a[4];
|
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var right = b[4];
|
||||
if (left > right) {
|
||||
return 1;
|
||||
} else if (left < right) {
|
||||
return -1;
|
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} else {
|
||||
// same score: sort alphabetically
|
||||
left = a[1].toLowerCase();
|
||||
right = b[1].toLowerCase();
|
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return (left > right) ? -1 : ((left < right) ? 1 : 0);
|
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}
|
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});
|
||||
|
||||
// for debugging
|
||||
//Search.lastresults = results.slice(); // a copy
|
||||
//console.info('search results:', Search.lastresults);
|
||||
|
||||
// print the results
|
||||
var resultCount = results.length;
|
||||
function displayNextItem() {
|
||||
// results left, load the summary and display it
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||||
if (results.length) {
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var item = results.pop();
|
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var listItem = $('<li></li>');
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var requestUrl = "";
|
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var linkUrl = "";
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if (DOCUMENTATION_OPTIONS.BUILDER === 'dirhtml') {
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// dirhtml builder
|
||||
var dirname = item[0] + '/';
|
||||
if (dirname.match(/\/index\/$/)) {
|
||||
dirname = dirname.substring(0, dirname.length-6);
|
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} else if (dirname == 'index/') {
|
||||
dirname = '';
|
||||
}
|
||||
requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + dirname;
|
||||
linkUrl = requestUrl;
|
||||
|
||||
} else {
|
||||
// normal html builders
|
||||
requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + item[0] + DOCUMENTATION_OPTIONS.FILE_SUFFIX;
|
||||
linkUrl = item[0] + DOCUMENTATION_OPTIONS.LINK_SUFFIX;
|
||||
}
|
||||
listItem.append($('<a/>').attr('href',
|
||||
linkUrl +
|
||||
highlightstring + item[2]).html(item[1]));
|
||||
if (item[3]) {
|
||||
listItem.append($('<span> (' + item[3] + ')</span>'));
|
||||
Search.output.append(listItem);
|
||||
setTimeout(function() {
|
||||
displayNextItem();
|
||||
}, 5);
|
||||
} else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
|
||||
$.ajax({url: requestUrl,
|
||||
dataType: "text",
|
||||
complete: function(jqxhr, textstatus) {
|
||||
var data = jqxhr.responseText;
|
||||
if (data !== '' && data !== undefined) {
|
||||
var summary = Search.makeSearchSummary(data, searchterms, hlterms);
|
||||
if (summary) {
|
||||
listItem.append(summary);
|
||||
}
|
||||
}
|
||||
Search.output.append(listItem);
|
||||
setTimeout(function() {
|
||||
displayNextItem();
|
||||
}, 5);
|
||||
}});
|
||||
} else {
|
||||
// no source available, just display title
|
||||
Search.output.append(listItem);
|
||||
setTimeout(function() {
|
||||
displayNextItem();
|
||||
}, 5);
|
||||
}
|
||||
}
|
||||
// search finished, update title and status message
|
||||
else {
|
||||
Search.stopPulse();
|
||||
Search.title.text(_('Search Results'));
|
||||
if (!resultCount)
|
||||
Search.status.text(_('Your search did not match any documents. Please make sure that all words are spelled correctly and that you\'ve selected enough categories.'));
|
||||
else
|
||||
Search.status.text(_('Search finished, found %s page(s) matching the search query.').replace('%s', resultCount));
|
||||
Search.status.fadeIn(500);
|
||||
}
|
||||
}
|
||||
displayNextItem();
|
||||
},
|
||||
|
||||
/**
|
||||
* search for object names
|
||||
*/
|
||||
performObjectSearch : function(object, otherterms) {
|
||||
var filenames = this._index.filenames;
|
||||
var docnames = this._index.docnames;
|
||||
var objects = this._index.objects;
|
||||
var objnames = this._index.objnames;
|
||||
var titles = this._index.titles;
|
||||
|
||||
var i;
|
||||
var results = [];
|
||||
|
||||
for (var prefix in objects) {
|
||||
for (var name in objects[prefix]) {
|
||||
var fullname = (prefix ? prefix + '.' : '') + name;
|
||||
var fullnameLower = fullname.toLowerCase()
|
||||
if (fullnameLower.indexOf(object) > -1) {
|
||||
var score = 0;
|
||||
var parts = fullnameLower.split('.');
|
||||
// check for different match types: exact matches of full name or
|
||||
// "last name" (i.e. last dotted part)
|
||||
if (fullnameLower == object || parts[parts.length - 1] == object) {
|
||||
score += Scorer.objNameMatch;
|
||||
// matches in last name
|
||||
} else if (parts[parts.length - 1].indexOf(object) > -1) {
|
||||
score += Scorer.objPartialMatch;
|
||||
}
|
||||
var match = objects[prefix][name];
|
||||
var objname = objnames[match[1]][2];
|
||||
var title = titles[match[0]];
|
||||
// If more than one term searched for, we require other words to be
|
||||
// found in the name/title/description
|
||||
if (otherterms.length > 0) {
|
||||
var haystack = (prefix + ' ' + name + ' ' +
|
||||
objname + ' ' + title).toLowerCase();
|
||||
var allfound = true;
|
||||
for (i = 0; i < otherterms.length; i++) {
|
||||
if (haystack.indexOf(otherterms[i]) == -1) {
|
||||
allfound = false;
|
||||
break;
|
||||
}
|
||||
}
|
||||
if (!allfound) {
|
||||
continue;
|
||||
}
|
||||
}
|
||||
var descr = objname + _(', in ') + title;
|
||||
|
||||
var anchor = match[3];
|
||||
if (anchor === '')
|
||||
anchor = fullname;
|
||||
else if (anchor == '-')
|
||||
anchor = objnames[match[1]][1] + '-' + fullname;
|
||||
// add custom score for some objects according to scorer
|
||||
if (Scorer.objPrio.hasOwnProperty(match[2])) {
|
||||
score += Scorer.objPrio[match[2]];
|
||||
} else {
|
||||
score += Scorer.objPrioDefault;
|
||||
}
|
||||
results.push([docnames[match[0]], fullname, '#'+anchor, descr, score, filenames[match[0]]]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return results;
|
||||
},
|
||||
|
||||
/**
|
||||
* See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions
|
||||
*/
|
||||
escapeRegExp : function(string) {
|
||||
return string.replace(/[.*+\-?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
|
||||
},
|
||||
|
||||
/**
|
||||
* search for full-text terms in the index
|
||||
*/
|
||||
performTermsSearch : function(searchterms, excluded, terms, titleterms) {
|
||||
var docnames = this._index.docnames;
|
||||
var filenames = this._index.filenames;
|
||||
var titles = this._index.titles;
|
||||
|
||||
var i, j, file;
|
||||
var fileMap = {};
|
||||
var scoreMap = {};
|
||||
var results = [];
|
||||
|
||||
// perform the search on the required terms
|
||||
for (i = 0; i < searchterms.length; i++) {
|
||||
var word = searchterms[i];
|
||||
var files = [];
|
||||
var _o = [
|
||||
{files: terms[word], score: Scorer.term},
|
||||
{files: titleterms[word], score: Scorer.title}
|
||||
];
|
||||
// add support for partial matches
|
||||
if (word.length > 2) {
|
||||
var word_regex = this.escapeRegExp(word);
|
||||
for (var w in terms) {
|
||||
if (w.match(word_regex) && !terms[word]) {
|
||||
_o.push({files: terms[w], score: Scorer.partialTerm})
|
||||
}
|
||||
}
|
||||
for (var w in titleterms) {
|
||||
if (w.match(word_regex) && !titleterms[word]) {
|
||||
_o.push({files: titleterms[w], score: Scorer.partialTitle})
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// no match but word was a required one
|
||||
if ($u.every(_o, function(o){return o.files === undefined;})) {
|
||||
break;
|
||||
}
|
||||
// found search word in contents
|
||||
$u.each(_o, function(o) {
|
||||
var _files = o.files;
|
||||
if (_files === undefined)
|
||||
return
|
||||
|
||||
if (_files.length === undefined)
|
||||
_files = [_files];
|
||||
files = files.concat(_files);
|
||||
|
||||
// set score for the word in each file to Scorer.term
|
||||
for (j = 0; j < _files.length; j++) {
|
||||
file = _files[j];
|
||||
if (!(file in scoreMap))
|
||||
scoreMap[file] = {};
|
||||
scoreMap[file][word] = o.score;
|
||||
}
|
||||
});
|
||||
|
||||
// create the mapping
|
||||
for (j = 0; j < files.length; j++) {
|
||||
file = files[j];
|
||||
if (file in fileMap && fileMap[file].indexOf(word) === -1)
|
||||
fileMap[file].push(word);
|
||||
else
|
||||
fileMap[file] = [word];
|
||||
}
|
||||
}
|
||||
|
||||
// now check if the files don't contain excluded terms
|
||||
for (file in fileMap) {
|
||||
var valid = true;
|
||||
|
||||
// check if all requirements are matched
|
||||
var filteredTermCount = // as search terms with length < 3 are discarded: ignore
|
||||
searchterms.filter(function(term){return term.length > 2}).length
|
||||
if (
|
||||
fileMap[file].length != searchterms.length &&
|
||||
fileMap[file].length != filteredTermCount
|
||||
) continue;
|
||||
|
||||
// ensure that none of the excluded terms is in the search result
|
||||
for (i = 0; i < excluded.length; i++) {
|
||||
if (terms[excluded[i]] == file ||
|
||||
titleterms[excluded[i]] == file ||
|
||||
$u.contains(terms[excluded[i]] || [], file) ||
|
||||
$u.contains(titleterms[excluded[i]] || [], file)) {
|
||||
valid = false;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
// if we have still a valid result we can add it to the result list
|
||||
if (valid) {
|
||||
// select one (max) score for the file.
|
||||
// for better ranking, we should calculate ranking by using words statistics like basic tf-idf...
|
||||
var score = $u.max($u.map(fileMap[file], function(w){return scoreMap[file][w]}));
|
||||
results.push([docnames[file], titles[file], '', null, score, filenames[file]]);
|
||||
}
|
||||
}
|
||||
return results;
|
||||
},
|
||||
|
||||
/**
|
||||
* helper function to return a node containing the
|
||||
* search summary for a given text. keywords is a list
|
||||
* of stemmed words, hlwords is the list of normal, unstemmed
|
||||
* words. the first one is used to find the occurrence, the
|
||||
* latter for highlighting it.
|
||||
*/
|
||||
makeSearchSummary : function(htmlText, keywords, hlwords) {
|
||||
var text = Search.htmlToText(htmlText);
|
||||
if (text == "") {
|
||||
return null;
|
||||
}
|
||||
var textLower = text.toLowerCase();
|
||||
var start = 0;
|
||||
$.each(keywords, function() {
|
||||
var i = textLower.indexOf(this.toLowerCase());
|
||||
if (i > -1)
|
||||
start = i;
|
||||
});
|
||||
start = Math.max(start - 120, 0);
|
||||
var excerpt = ((start > 0) ? '...' : '') +
|
||||
$.trim(text.substr(start, 240)) +
|
||||
((start + 240 - text.length) ? '...' : '');
|
||||
var rv = $('<p class="context"></p>').text(excerpt);
|
||||
$.each(hlwords, function() {
|
||||
rv = rv.highlightText(this, 'highlighted');
|
||||
});
|
||||
return rv;
|
||||
}
|
||||
};
|
||||
|
||||
$(document).ready(function() {
|
||||
Search.init();
|
||||
});
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because one or more lines are too long
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,224 @@
|
|||
|
||||
|
||||
<!doctype html>
|
||||
|
||||
<html>
|
||||
<head>
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>Welcome to QuaPy’s documentation! — QuaPy 0.1.6 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="_static/pygments.css" />
|
||||
<link rel="stylesheet" type="text/css" href="_static/bizstyle.css" />
|
||||
|
||||
<script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
|
||||
<script src="_static/jquery.js"></script>
|
||||
<script src="_static/underscore.js"></script>
|
||||
<script src="_static/doctools.js"></script>
|
||||
<script src="_static/bizstyle.js"></script>
|
||||
<link rel="index" title="Index" href="genindex.html" />
|
||||
<link rel="search" title="Search" href="search.html" />
|
||||
<link rel="next" title="Installation" href="Installation.html" />
|
||||
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<div class="section" id="welcome-to-quapy-s-documentation">
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||||
<h1>Welcome to QuaPy’s documentation!<a class="headerlink" href="#welcome-to-quapy-s-documentation" title="Permalink to this headline">¶</a></h1>
|
||||
<p>QuaPy is an open source framework for Quantification (a.k.a. Supervised Prevalence Estimation)
|
||||
written in Python.</p>
|
||||
<div class="section" id="introduction">
|
||||
<h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">¶</a></h2>
|
||||
<p>QuaPy roots on the concept of data sample, and provides implementations of most important concepts
|
||||
in quantification literature, such as the most important quantification baselines, many advanced
|
||||
quantification methods, quantification-oriented model selection, many evaluation measures and protocols
|
||||
used for evaluating quantification methods.
|
||||
QuaPy also integrates commonly used datasets and offers visualization tools for facilitating the analysis and
|
||||
interpretation of results.</p>
|
||||
<div class="section" id="a-quick-example">
|
||||
<h3>A quick example:<a class="headerlink" href="#a-quick-example" title="Permalink to this headline">¶</a></h3>
|
||||
<p>The following script fetchs a Twitter dataset, trains and evaluates an
|
||||
<cite>Adjusted Classify & Count</cite> model in terms of the <cite>Mean Absolute Error</cite> (MAE)
|
||||
between the class prevalences estimated for the test set and the true prevalences
|
||||
of the test set.</p>
|
||||
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
|
||||
|
||||
<span class="n">dataset</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_twitter</span><span class="p">(</span><span class="s1">'semeval16'</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># create an "Adjusted Classify & Count" quantifier</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">method</span><span class="o">.</span><span class="n">aggregative</span><span class="o">.</span><span class="n">ACC</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">())</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="p">)</span>
|
||||
|
||||
<span class="n">estim_prevalences</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||||
<span class="n">true_prevalences</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span>
|
||||
|
||||
<span class="n">error</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">mae</span><span class="p">(</span><span class="n">true_prevalences</span><span class="p">,</span> <span class="n">estim_prevalences</span><span class="p">)</span>
|
||||
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Mean Absolute Error (MAE)=</span><span class="si">{</span><span class="n">error</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
</div>
|
||||
<p>Quantification is useful in scenarios of prior probability shift. In other
|
||||
words, we would not be interested in estimating the class prevalences of the test set if
|
||||
we could assume the IID assumption to hold, as this prevalence would simply coincide with the
|
||||
class prevalence of the training set. For this reason, any Quantification model
|
||||
should be tested across samples characterized by different class prevalences.
|
||||
QuaPy implements sampling procedures and evaluation protocols that automates this endeavour.
|
||||
See the <a class="reference internal" href="Evaluation.html"><span class="doc">Evaluation</span></a> for detailed examples.</p>
|
||||
</div>
|
||||
<div class="section" id="features">
|
||||
<h3>Features<a class="headerlink" href="#features" title="Permalink to this headline">¶</a></h3>
|
||||
<ul class="simple">
|
||||
<li><p>Implementation of most popular quantification methods (Classify-&-Count variants, Expectation-Maximization, SVM-based variants for quantification, HDy, QuaNet, and Ensembles).</p></li>
|
||||
<li><p>Versatile functionality for performing evaluation based on artificial sampling protocols.</p></li>
|
||||
<li><p>Implementation of most commonly used evaluation metrics (e.g., MAE, MRAE, MSE, NKLD, etc.).</p></li>
|
||||
<li><dl class="simple">
|
||||
<dt>Popular datasets for Quantification (textual and numeric) available, including:</dt><dd><ul>
|
||||
<li><p>32 UCI Machine Learning datasets.</p></li>
|
||||
<li><p>11 Twitter Sentiment datasets.</p></li>
|
||||
<li><p>3 Reviews Sentiment datasets.</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</li>
|
||||
<li><p>Native supports for binary and single-label scenarios of quantification.</p></li>
|
||||
<li><p>Model selection functionality targeting quantification-oriented losses.</p></li>
|
||||
<li><p>Visualization tools for analysing results.</p></li>
|
||||
</ul>
|
||||
<div class="toctree-wrapper compound">
|
||||
<p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="Installation.html">Installation</a><ul>
|
||||
<li class="toctree-l2"><a class="reference internal" href="Installation.html#requirements">Requirements</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="Installation.html#svm-perf-with-quantification-oriented-losses">SVM-perf with quantification-oriented losses</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="Datasets.html">Datasets</a><ul>
|
||||
<li class="toctree-l2"><a class="reference internal" href="Datasets.html#reviews-datasets">Reviews Datasets</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="Datasets.html#twitter-sentiment-datasets">Twitter Sentiment Datasets</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="Datasets.html#uci-machine-learning">UCI Machine Learning</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="Datasets.html#adding-custom-datasets">Adding Custom Datasets</a></li>
|
||||
</ul>
|
||||
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|
||||
<li class="toctree-l1"><a class="reference internal" href="Evaluation.html">Evaluation</a><ul>
|
||||
<li class="toctree-l2"><a class="reference internal" href="Evaluation.html#error-measures">Error Measures</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="Evaluation.html#evaluation-protocols">Evaluation Protocols</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="Methods.html">Quantification Methods</a><ul>
|
||||
<li class="toctree-l2"><a class="reference internal" href="Methods.html#aggregative-methods">Aggregative Methods</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="Methods.html#meta-models">Meta Models</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="Plotting.html">Plotting</a><ul>
|
||||
<li class="toctree-l2"><a class="reference internal" href="Plotting.html#diagonal-plot">Diagonal Plot</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="Plotting.html#quantification-bias">Quantification bias</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="Plotting.html#error-by-drift">Error by Drift</a></li>
|
||||
</ul>
|
||||
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|
||||
<li class="toctree-l1"><a class="reference internal" href="modules.html">API Developer documentation</a><ul>
|
||||
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|
||||
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|
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</li>
|
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</ul>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="section" id="indices-and-tables">
|
||||
<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this headline">¶</a></h1>
|
||||
<ul class="simple">
|
||||
<li><p><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></p></li>
|
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<h3><a href="#">Table of Contents</a></h3>
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|
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<li><a class="reference internal" href="#">Welcome to QuaPy’s documentation!</a><ul>
|
||||
<li><a class="reference internal" href="#introduction">Introduction</a><ul>
|
||||
<li><a class="reference internal" href="#a-quick-example">A quick example:</a></li>
|
||||
<li><a class="reference internal" href="#features">Features</a></li>
|
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|
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|
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<h1>quapy.classification package<a class="headerlink" href="#quapy-classification-package" title="Permalink to this headline">¶</a></h1>
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<div class="section" id="submodules">
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<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
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<div class="section" id="module-quapy.classification.methods">
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<span id="quapy-classification-methods-module"></span><h2>quapy.classification.methods module<a class="headerlink" href="#module-quapy.classification.methods" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.methods.PCALR">
|
||||
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.classification.methods.</span></span><span class="sig-name descname"><span class="pre">PCALR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_components</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.methods.PCALR" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.base.BaseEstimator</span></code></p>
|
||||
<p>An example of a classification method that also generates embedded inputs, as those required for QuaNet.
|
||||
This example simply combines a Principal Component Analysis (PCA) with Logistic Regression (LR).</p>
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.methods.PCALR.fit">
|
||||
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.methods.PCALR.fit" title="Permalink to this definition">¶</a></dt>
|
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<dl class="py method">
|
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<dt class="sig sig-object py" id="quapy.classification.methods.PCALR.get_params">
|
||||
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.methods.PCALR.get_params" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Get parameters for this estimator.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
<dd class="field-odd"><p><strong>deep</strong> (<em>bool</em><em>, </em><em>default=True</em>) – If True, will return the parameters for this estimator and
|
||||
contained subobjects that are estimators.</p>
|
||||
</dd>
|
||||
<dt class="field-even">Returns</dt>
|
||||
<dd class="field-even"><p><strong>params</strong> – Parameter names mapped to their values.</p>
|
||||
</dd>
|
||||
<dt class="field-odd">Return type</dt>
|
||||
<dd class="field-odd"><p>dict</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.methods.PCALR.predict">
|
||||
<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.methods.PCALR.predict" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.methods.PCALR.predict_proba">
|
||||
<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.methods.PCALR.predict_proba" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.methods.PCALR.set_params">
|
||||
<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.methods.PCALR.set_params" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Set the parameters of this estimator.</p>
|
||||
<p>The method works on simple estimators as well as on nested objects
|
||||
(such as <code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>). The latter have
|
||||
parameters of the form <code class="docutils literal notranslate"><span class="pre"><component>__<parameter></span></code> so that it’s
|
||||
possible to update each component of a nested object.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
<dd class="field-odd"><p><strong>**params</strong> (<em>dict</em>) – Estimator parameters.</p>
|
||||
</dd>
|
||||
<dt class="field-even">Returns</dt>
|
||||
<dd class="field-even"><p><strong>self</strong> – Estimator instance.</p>
|
||||
</dd>
|
||||
<dt class="field-odd">Return type</dt>
|
||||
<dd class="field-odd"><p>estimator instance</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.methods.PCALR.transform">
|
||||
<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.methods.PCALR.transform" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-quapy.classification.neural">
|
||||
<span id="quapy-classification-neural-module"></span><h2>quapy.classification.neural module<a class="headerlink" href="#module-quapy.classification.neural" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.CNNnet">
|
||||
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.classification.neural.</span></span><span class="sig-name descname"><span class="pre">CNNnet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">vocabulary_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">256</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repr_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_heights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[3,</span> <span class="pre">5,</span> <span class="pre">7]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.CNNnet" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#quapy.classification.neural.TextClassifierNet" title="quapy.classification.neural.TextClassifierNet"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.classification.neural.TextClassifierNet</span></code></a></p>
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.CNNnet.conv_block">
|
||||
<span class="sig-name descname"><span class="pre">conv_block</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_layer</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.CNNnet.conv_block" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.CNNnet.document_embedding">
|
||||
<span class="sig-name descname"><span class="pre">document_embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.CNNnet.document_embedding" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.CNNnet.get_params">
|
||||
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.CNNnet.get_params" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.CNNnet.vocabulary_size">
|
||||
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">vocabulary_size</span></span><a class="headerlink" href="#quapy.classification.neural.CNNnet.vocabulary_size" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.LSTMnet">
|
||||
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.classification.neural.</span></span><span class="sig-name descname"><span class="pre">LSTMnet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">vocabulary_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">256</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repr_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lstm_class_nlayers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.LSTMnet" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#quapy.classification.neural.TextClassifierNet" title="quapy.classification.neural.TextClassifierNet"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.classification.neural.TextClassifierNet</span></code></a></p>
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.LSTMnet.document_embedding">
|
||||
<span class="sig-name descname"><span class="pre">document_embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.LSTMnet.document_embedding" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.LSTMnet.get_params">
|
||||
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.LSTMnet.get_params" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.LSTMnet.init_hidden">
|
||||
<span class="sig-name descname"><span class="pre">init_hidden</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_size</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.LSTMnet.init_hidden" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.LSTMnet.vocabulary_size">
|
||||
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">vocabulary_size</span></span><a class="headerlink" href="#quapy.classification.neural.LSTMnet.vocabulary_size" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer">
|
||||
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.classification.neural.</span></span><span class="sig-name descname"><span class="pre">NeuralClassifierTrainer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="#quapy.classification.neural.TextClassifierNet" title="quapy.classification.neural.TextClassifierNet"><span class="pre">quapy.classification.neural.TextClassifierNet</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">patience</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">64</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size_test</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">512</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cpu'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpointpath</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'../checkpoint/classifier_net.dat'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.device">
|
||||
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">device</span></span><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.device" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.fit">
|
||||
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.fit" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.get_params">
|
||||
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.get_params" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.predict">
|
||||
<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.predict" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.predict_proba">
|
||||
<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.predict_proba" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.reset_net_params">
|
||||
<span class="sig-name descname"><span class="pre">reset_net_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">vocab_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.reset_net_params" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.set_params">
|
||||
<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.set_params" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.transform">
|
||||
<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.transform" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet">
|
||||
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.classification.neural.</span></span><span class="sig-name descname"><span class="pre">TextClassifierNet</span></span><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.modules.module.Module</span></code></p>
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.dimensions">
|
||||
<span class="sig-name descname"><span class="pre">dimensions</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.dimensions" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.document_embedding">
|
||||
<em class="property"><span class="pre">abstract</span> </em><span class="sig-name descname"><span class="pre">document_embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.document_embedding" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.forward">
|
||||
<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.forward" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Defines the computation performed at every call.</p>
|
||||
<p>Should be overridden by all subclasses.</p>
|
||||
<div class="admonition note">
|
||||
<p class="admonition-title">Note</p>
|
||||
<p>Although the recipe for forward pass needs to be defined within
|
||||
this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
|
||||
instead of this since the former takes care of running the
|
||||
registered hooks while the latter silently ignores them.</p>
|
||||
</div>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.get_params">
|
||||
<em class="property"><span class="pre">abstract</span> </em><span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.get_params" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.predict_proba">
|
||||
<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.predict_proba" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.vocabulary_size">
|
||||
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">vocabulary_size</span></span><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.vocabulary_size" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.xavier_uniform">
|
||||
<span class="sig-name descname"><span class="pre">xavier_uniform</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.xavier_uniform" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TorchDataset">
|
||||
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.classification.neural.</span></span><span class="sig-name descname"><span class="pre">TorchDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.TorchDataset" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.utils.data.dataset.Dataset</span></code></p>
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.neural.TorchDataset.asDataloader">
|
||||
<span class="sig-name descname"><span class="pre">asDataloader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad_length</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.TorchDataset.asDataloader" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-quapy.classification.svmperf">
|
||||
<span id="quapy-classification-svmperf-module"></span><h2>quapy.classification.svmperf module<a class="headerlink" href="#module-quapy.classification.svmperf" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.classification.svmperf.SVMperf">
|
||||
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.classification.svmperf.</span></span><span class="sig-name descname"><span class="pre">SVMperf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">svmperf_base</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">C</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'01'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.svmperf.SVMperf" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.base.BaseEstimator</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.base.ClassifierMixin</span></code></p>
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.svmperf.SVMperf.decision_function">
|
||||
<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.svmperf.SVMperf.decision_function" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.svmperf.SVMperf.fit">
|
||||
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.svmperf.SVMperf.fit" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.svmperf.SVMperf.predict">
|
||||
<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.svmperf.SVMperf.predict" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.classification.svmperf.SVMperf.set_params">
|
||||
<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">parameters</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.svmperf.SVMperf.set_params" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Set the parameters of this estimator.</p>
|
||||
<p>The method works on simple estimators as well as on nested objects
|
||||
(such as <code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code>). The latter have
|
||||
parameters of the form <code class="docutils literal notranslate"><span class="pre"><component>__<parameter></span></code> so that it’s
|
||||
possible to update each component of a nested object.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
<dd class="field-odd"><p><strong>**params</strong> (<em>dict</em>) – Estimator parameters.</p>
|
||||
</dd>
|
||||
<dt class="field-even">Returns</dt>
|
||||
<dd class="field-even"><p><strong>self</strong> – Estimator instance.</p>
|
||||
</dd>
|
||||
<dt class="field-odd">Return type</dt>
|
||||
<dd class="field-odd"><p>estimator instance</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py attribute">
|
||||
<dt class="sig sig-object py" id="quapy.classification.svmperf.SVMperf.valid_losses">
|
||||
<span class="sig-name descname"><span class="pre">valid_losses</span></span><em class="property"> <span class="pre">=</span> <span class="pre">{'01':</span> <span class="pre">0,</span> <span class="pre">'f1':</span> <span class="pre">1,</span> <span class="pre">'kld':</span> <span class="pre">12,</span> <span class="pre">'mae':</span> <span class="pre">26,</span> <span class="pre">'mrae':</span> <span class="pre">27,</span> <span class="pre">'nkld':</span> <span class="pre">13,</span> <span class="pre">'q':</span> <span class="pre">22,</span> <span class="pre">'qacc':</span> <span class="pre">23,</span> <span class="pre">'qf1':</span> <span class="pre">24,</span> <span class="pre">'qgm':</span> <span class="pre">25}</span></em><a class="headerlink" href="#quapy.classification.svmperf.SVMperf.valid_losses" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-quapy.classification">
|
||||
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-quapy.classification" title="Permalink to this headline">¶</a></h2>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
|
||||
<div class="clearer"></div>
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
<div class="sphinxsidebarwrapper">
|
||||
<h3><a href="index.html">Table of Contents</a></h3>
|
||||
<ul>
|
||||
<li><a class="reference internal" href="#">quapy.classification package</a><ul>
|
||||
<li><a class="reference internal" href="#submodules">Submodules</a></li>
|
||||
<li><a class="reference internal" href="#module-quapy.classification.methods">quapy.classification.methods module</a></li>
|
||||
<li><a class="reference internal" href="#module-quapy.classification.neural">quapy.classification.neural module</a></li>
|
||||
<li><a class="reference internal" href="#module-quapy.classification.svmperf">quapy.classification.svmperf module</a></li>
|
||||
<li><a class="reference internal" href="#module-quapy.classification">Module contents</a></li>
|
||||
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|
||||
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|
||||
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|
||||
|
||||
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|
||||
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|
||||
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|
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|
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|
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<div class="section" id="quapy-data-package">
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<h1>quapy.data package<a class="headerlink" href="#quapy-data-package" title="Permalink to this headline">¶</a></h1>
|
||||
<div class="section" id="submodules">
|
||||
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
|
||||
</div>
|
||||
<div class="section" id="module-quapy.data.base">
|
||||
<span id="quapy-data-base-module"></span><h2>quapy.data.base module<a class="headerlink" href="#module-quapy.data.base" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.Dataset">
|
||||
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.data.base.</span></span><span class="sig-name descname"><span class="pre">Dataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">test</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">vocabulary</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">dict</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.base.Dataset" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.Dataset.SplitStratified">
|
||||
<em class="property"><span class="pre">classmethod</span> </em><span class="sig-name descname"><span class="pre">SplitStratified</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">collection</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.6</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.base.Dataset.SplitStratified" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.Dataset.binary">
|
||||
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">binary</span></span><a class="headerlink" href="#quapy.data.base.Dataset.binary" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.Dataset.classes_">
|
||||
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">classes_</span></span><a class="headerlink" href="#quapy.data.base.Dataset.classes_" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.Dataset.kFCV">
|
||||
<em class="property"><span class="pre">classmethod</span> </em><span class="sig-name descname"><span class="pre">kFCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">nfolds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nrepeats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.base.Dataset.kFCV" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.Dataset.load">
|
||||
<em class="property"><span class="pre">classmethod</span> </em><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">train_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loader_func</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">callable</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.base.Dataset.load" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.Dataset.n_classes">
|
||||
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">n_classes</span></span><a class="headerlink" href="#quapy.data.base.Dataset.n_classes" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.Dataset.stats">
|
||||
<span class="sig-name descname"><span class="pre">stats</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.base.Dataset.stats" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.Dataset.vocabulary_size">
|
||||
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">vocabulary_size</span></span><a class="headerlink" href="#quapy.data.base.Dataset.vocabulary_size" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection">
|
||||
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.data.base.</span></span><span class="sig-name descname"><span class="pre">LabelledCollection</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes_</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.base.LabelledCollection" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
|
||||
<p>A LabelledCollection is a set of objects each with a label associated to it.</p>
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.Xy">
|
||||
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">Xy</span></span><a class="headerlink" href="#quapy.data.base.LabelledCollection.Xy" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.artificial_sampling_generator">
|
||||
<span class="sig-name descname"><span class="pre">artificial_sampling_generator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_prevalences</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">101</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repeats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.base.LabelledCollection.artificial_sampling_generator" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.artificial_sampling_index_generator">
|
||||
<span class="sig-name descname"><span class="pre">artificial_sampling_index_generator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_prevalences</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">101</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repeats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.base.LabelledCollection.artificial_sampling_index_generator" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.binary">
|
||||
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">binary</span></span><a class="headerlink" href="#quapy.data.base.LabelledCollection.binary" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.counts">
|
||||
<span class="sig-name descname"><span class="pre">counts</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.base.LabelledCollection.counts" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.kFCV">
|
||||
<span class="sig-name descname"><span class="pre">kFCV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nfolds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nrepeats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.base.LabelledCollection.kFCV" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.load">
|
||||
<em class="property"><span class="pre">classmethod</span> </em><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loader_func</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">callable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.base.LabelledCollection.load" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.n_classes">
|
||||
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">n_classes</span></span><a class="headerlink" href="#quapy.data.base.LabelledCollection.n_classes" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.natural_sampling_generator">
|
||||
<span class="sig-name descname"><span class="pre">natural_sampling_generator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repeats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.base.LabelledCollection.natural_sampling_generator" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.natural_sampling_index_generator">
|
||||
<span class="sig-name descname"><span class="pre">natural_sampling_index_generator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repeats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.base.LabelledCollection.natural_sampling_index_generator" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.prevalence">
|
||||
<span class="sig-name descname"><span class="pre">prevalence</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.base.LabelledCollection.prevalence" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.sampling">
|
||||
<span class="sig-name descname"><span class="pre">sampling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.base.LabelledCollection.sampling" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.sampling_from_index">
|
||||
<span class="sig-name descname"><span class="pre">sampling_from_index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">index</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.base.LabelledCollection.sampling_from_index" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.sampling_index">
|
||||
<span class="sig-name descname"><span class="pre">sampling_index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.base.LabelledCollection.sampling_index" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.split_stratified">
|
||||
<span class="sig-name descname"><span class="pre">split_stratified</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">train_prop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.base.LabelledCollection.split_stratified" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.stats">
|
||||
<span class="sig-name descname"><span class="pre">stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">show</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.base.LabelledCollection.stats" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.uniform_sampling">
|
||||
<span class="sig-name descname"><span class="pre">uniform_sampling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.base.LabelledCollection.uniform_sampling" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.uniform_sampling_index">
|
||||
<span class="sig-name descname"><span class="pre">uniform_sampling_index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.base.LabelledCollection.uniform_sampling_index" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.base.isbinary">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.base.</span></span><span class="sig-name descname"><span class="pre">isbinary</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.base.isbinary" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-quapy.data.datasets">
|
||||
<span id="quapy-data-datasets-module"></span><h2>quapy.data.datasets module<a class="headerlink" href="#module-quapy.data.datasets" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.datasets.df_replace">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">df_replace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">df</span></em>, <em class="sig-param"><span class="pre">col</span></em>, <em class="sig-param"><span class="pre">repl={'no':</span> <span class="pre">0</span></em>, <em class="sig-param"><span class="pre">'yes':</span> <span class="pre">1}</span></em>, <em class="sig-param"><span class="pre">astype=<class</span> <span class="pre">'float'></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.datasets.df_replace" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.datasets.fetch_UCIDataset">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_UCIDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">quapy.data.base.Dataset</span></a></span></span><a class="headerlink" href="#quapy.data.datasets.fetch_UCIDataset" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.datasets.fetch_UCILabelledCollection">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_UCILabelledCollection</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">quapy.data.base.Dataset</span></a></span></span><a class="headerlink" href="#quapy.data.datasets.fetch_UCILabelledCollection" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.datasets.fetch_reviews">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_reviews</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tfidf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_df</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pickle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">quapy.data.base.Dataset</span></a></span></span><a class="headerlink" href="#quapy.data.datasets.fetch_reviews" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Load a Reviews dataset as a Dataset instance, as used in:
|
||||
Esuli, A., Moreo, A., and Sebastiani, F. “A recurrent neural network for sentiment quantification.”
|
||||
Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2018.
|
||||
:param dataset_name: the name of the dataset: valid ones are ‘hp’, ‘kindle’, ‘imdb’
|
||||
:param tfidf: set to True to transform the raw documents into tfidf weighted matrices
|
||||
:param min_df: minimun number of documents that should contain a term in order for the term to be
|
||||
kept (ignored if tfidf==False)
|
||||
:param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default
|
||||
~/quay_data/ directory)
|
||||
:param pickle: set to True to pickle the Dataset object the first time it is generated, in order to allow for
|
||||
faster subsequent invokations
|
||||
:return: a Dataset instance</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.datasets.fetch_twitter">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_twitter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">for_model_selection</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_df</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pickle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">quapy.data.base.Dataset</span></a></span></span><a class="headerlink" href="#quapy.data.datasets.fetch_twitter" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Load a Twitter dataset as a Dataset instance, as used in:
|
||||
Gao, W., Sebastiani, F.: From classification to quantification in tweet sentiment analysis.
|
||||
Social Network Analysis and Mining6(19), 1–22 (2016)
|
||||
The datasets ‘semeval13’, ‘semeval14’, ‘semeval15’ share the same training set.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
<dd class="field-odd"><p><strong>dataset_name</strong> – the name of the dataset: valid ones are ‘gasp’, ‘hcr’, ‘omd’, ‘sanders’, ‘semeval13’,</p>
|
||||
</dd>
|
||||
</dl>
|
||||
<p>‘semeval14’, ‘semeval15’, ‘semeval16’, ‘sst’, ‘wa’, ‘wb’
|
||||
:param for_model_selection: if True, then returns the train split as the training set and the devel split
|
||||
as the test set; if False, then returns the train+devel split as the training set and the test set as the
|
||||
test set
|
||||
:param min_df: minimun number of documents that should contain a term in order for the term to be kept
|
||||
:param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default
|
||||
~/quay_data/ directory)
|
||||
:param pickle: set to True to pickle the Dataset object the first time it is generated, in order to allow for
|
||||
faster subsequent invokations
|
||||
:return: a Dataset instance</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.datasets.warn">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">warn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.datasets.warn" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-quapy.data.preprocessing">
|
||||
<span id="quapy-data-preprocessing-module"></span><h2>quapy.data.preprocessing module<a class="headerlink" href="#module-quapy.data.preprocessing" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.data.preprocessing.IndexTransformer">
|
||||
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.data.preprocessing.</span></span><span class="sig-name descname"><span class="pre">IndexTransformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.preprocessing.IndexTransformer" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.preprocessing.IndexTransformer.add_word">
|
||||
<span class="sig-name descname"><span class="pre">add_word</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">word</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nogaps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.preprocessing.IndexTransformer.add_word" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.preprocessing.IndexTransformer.fit">
|
||||
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.preprocessing.IndexTransformer.fit" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
<dd class="field-odd"><p><strong>X</strong> – a list of strings</p>
|
||||
</dd>
|
||||
<dt class="field-even">Returns</dt>
|
||||
<dd class="field-even"><p>self</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.preprocessing.IndexTransformer.fit_transform">
|
||||
<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.preprocessing.IndexTransformer.fit_transform" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.preprocessing.IndexTransformer.index">
|
||||
<span class="sig-name descname"><span class="pre">index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">documents</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.preprocessing.IndexTransformer.index" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.preprocessing.IndexTransformer.transform">
|
||||
<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.preprocessing.IndexTransformer.transform" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.data.preprocessing.IndexTransformer.vocabulary_size">
|
||||
<span class="sig-name descname"><span class="pre">vocabulary_size</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.preprocessing.IndexTransformer.vocabulary_size" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.preprocessing.index">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.preprocessing.</span></span><span class="sig-name descname"><span class="pre">index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">quapy.data.base.Dataset</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_df</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.preprocessing.index" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Indexes a dataset of strings. To index a document means to replace each different token by a unique numerical index.
|
||||
Rare words (i.e., words occurring less than _min_df_ times) are replaced by a special token UNK
|
||||
:param dataset: a Dataset where the instances are lists of str
|
||||
:param min_df: minimum number of instances below which the term is replaced by a UNK index
|
||||
:param inplace: whether or not to apply the transformation inplace, or to a new copy
|
||||
:param kwargs: the rest of parameters of the transformation (as for sklearn.feature_extraction.text.CountVectorizer)
|
||||
:return: a new Dataset (if inplace=False) or a reference to the current Dataset (inplace=True)
|
||||
consisting of lists of integer values representing indices.</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.preprocessing.reduce_columns">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.preprocessing.</span></span><span class="sig-name descname"><span class="pre">reduce_columns</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">quapy.data.base.Dataset</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_df</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.preprocessing.reduce_columns" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Reduces the dimensionality of the csr_matrix by removing the columns of words which are not present in at least
|
||||
_min_df_ instances
|
||||
:param dataset: a Dataset in sparse format (any subtype of scipy.sparse.spmatrix)
|
||||
:param min_df: minimum number of instances below which the columns are removed
|
||||
:param inplace: whether or not to apply the transformation inplace, or to a new copy
|
||||
:return: a new Dataset (if inplace=False) or a reference to the current Dataset (inplace=True)
|
||||
where the dimensions corresponding to infrequent instances have been removed</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.preprocessing.standardize">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.preprocessing.</span></span><span class="sig-name descname"><span class="pre">standardize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">quapy.data.base.Dataset</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.preprocessing.standardize" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.preprocessing.text2tfidf">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.preprocessing.</span></span><span class="sig-name descname"><span class="pre">text2tfidf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">quapy.data.base.Dataset</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_df</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sublinear_tf</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.preprocessing.text2tfidf" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Transforms a Dataset of textual instances into a Dataset of tfidf weighted sparse vectors
|
||||
:param dataset: a Dataset where the instances are lists of str
|
||||
:param min_df: minimum number of occurrences for a word to be considered as part of the vocabulary
|
||||
:param sublinear_tf: whether or not to apply the log scalling to the tf counters
|
||||
:param inplace: whether or not to apply the transformation inplace, or to a new copy
|
||||
:param kwargs: the rest of parameters of the transformation (as for sklearn.feature_extraction.text.TfidfVectorizer)
|
||||
:return: a new Dataset in csr_matrix format (if inplace=False) or a reference to the current Dataset (inplace=True)
|
||||
where the instances are stored in a csr_matrix of real-valued tfidf scores</p>
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-quapy.data.reader">
|
||||
<span id="quapy-data-reader-module"></span><h2>quapy.data.reader module<a class="headerlink" href="#module-quapy.data.reader" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.reader.binarize">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.reader.</span></span><span class="sig-name descname"><span class="pre">binarize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_class</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.reader.binarize" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.reader.from_csv">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.reader.</span></span><span class="sig-name descname"><span class="pre">from_csv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encoding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'utf-8'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.reader.from_csv" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Reads a csv file in which columns are separated by ‘,’.
|
||||
File format <label>,<feat1>,<feat2>,…,<featn></p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
<dd class="field-odd"><p><strong>path</strong> – path to the csv file</p>
|
||||
</dd>
|
||||
<dt class="field-even">Returns</dt>
|
||||
<dd class="field-even"><p>a ndarray for the labels and a ndarray (float) for the covariates</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.reader.from_sparse">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.reader.</span></span><span class="sig-name descname"><span class="pre">from_sparse</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.reader.from_sparse" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Reads a labelled collection of real-valued instances expressed in sparse format
|
||||
File format <-1 or 0 or 1>[s col(int):val(float)]</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
<dd class="field-odd"><p><strong>path</strong> – path to the labelled collection</p>
|
||||
</dd>
|
||||
<dt class="field-even">Returns</dt>
|
||||
<dd class="field-even"><p>a csr_matrix containing the instances (rows), and a ndarray containing the labels</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.reader.from_text">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.reader.</span></span><span class="sig-name descname"><span class="pre">from_text</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encoding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'utf-8'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class2int</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.reader.from_text" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Reads a labelled colletion of documents.
|
||||
File fomart <0 or 1> <document></p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
<dd class="field-odd"><p><strong>path</strong> – path to the labelled collection</p>
|
||||
</dd>
|
||||
<dt class="field-even">Returns</dt>
|
||||
<dd class="field-even"><p>a list of sentences, and a list of labels</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.data.reader.reindex_labels">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.data.reader.</span></span><span class="sig-name descname"><span class="pre">reindex_labels</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.data.reader.reindex_labels" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Re-indexes a list of labels as a list of indexes, and returns the classnames corresponding to the indexes.
|
||||
E.g., y=[‘B’, ‘B’, ‘A’, ‘C’] -> [1,1,0,2], [‘A’,’B’,’C’]
|
||||
:param y: the list or array of original labels
|
||||
:return: a ndarray (int) of class indexes, and a ndarray of classnames corresponding to the indexes.</p>
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-quapy.data">
|
||||
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-quapy.data" title="Permalink to this headline">¶</a></h2>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
|
||||
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<h3><a href="index.html">Table of Contents</a></h3>
|
||||
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|
||||
<li><a class="reference internal" href="#">quapy.data package</a><ul>
|
||||
<li><a class="reference internal" href="#submodules">Submodules</a></li>
|
||||
<li><a class="reference internal" href="#module-quapy.data.base">quapy.data.base module</a></li>
|
||||
<li><a class="reference internal" href="#module-quapy.data.datasets">quapy.data.datasets module</a></li>
|
||||
<li><a class="reference internal" href="#module-quapy.data.preprocessing">quapy.data.preprocessing module</a></li>
|
||||
<li><a class="reference internal" href="#module-quapy.data.reader">quapy.data.reader module</a></li>
|
||||
<li><a class="reference internal" href="#module-quapy.data">Module contents</a></li>
|
||||
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|
||||
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|
||||
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|
||||
|
||||
<h4>Previous topic</h4>
|
||||
<p class="topless"><a href="quapy.classification.html"
|
||||
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|
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|
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|
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<h1>quapy package<a class="headerlink" href="#quapy-package" title="Permalink to this headline">¶</a></h1>
|
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<div class="section" id="subpackages">
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<h2>Subpackages<a class="headerlink" href="#subpackages" title="Permalink to this headline">¶</a></h2>
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<li class="toctree-l1"><a class="reference internal" href="quapy.classification.html">quapy.classification package</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="quapy.classification.html#submodules">Submodules</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="quapy.classification.html#module-quapy.classification.methods">quapy.classification.methods module</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="quapy.classification.html#module-quapy.classification.neural">quapy.classification.neural module</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="quapy.classification.html#module-quapy.classification.svmperf">quapy.classification.svmperf module</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="quapy.classification.html#module-quapy.classification">Module contents</a></li>
|
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|
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<li class="toctree-l1"><a class="reference internal" href="quapy.data.html">quapy.data package</a><ul>
|
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<li class="toctree-l2"><a class="reference internal" href="quapy.data.html#submodules">Submodules</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="quapy.data.html#module-quapy.data.base">quapy.data.base module</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="quapy.data.html#module-quapy.data.datasets">quapy.data.datasets module</a></li>
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<li class="toctree-l2"><a class="reference internal" href="quapy.data.html#module-quapy.data.preprocessing">quapy.data.preprocessing module</a></li>
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<li class="toctree-l2"><a class="reference internal" href="quapy.data.html#module-quapy.data">Module contents</a></li>
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|
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<li class="toctree-l1"><a class="reference internal" href="quapy.method.html">quapy.method package</a><ul>
|
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<li class="toctree-l2"><a class="reference internal" href="quapy.method.html#submodules">Submodules</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="quapy.method.html#module-quapy.method.aggregative">quapy.method.aggregative module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="quapy.method.html#module-quapy.method.base">quapy.method.base module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="quapy.method.html#module-quapy.method.meta">quapy.method.meta module</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="quapy.method.html#module-quapy.method.neural">quapy.method.neural module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="quapy.method.html#module-quapy.method.non_aggregative">quapy.method.non_aggregative module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="quapy.method.html#module-quapy.method">Module contents</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
<div class="section" id="submodules">
|
||||
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
|
||||
</div>
|
||||
<div class="section" id="module-quapy.error">
|
||||
<span id="quapy-error-module"></span><h2>quapy.error module<a class="headerlink" href="#module-quapy.error" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.error.absolute_error">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">absolute_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_hat</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.absolute_error" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.error.acc_error">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">acc_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.acc_error" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.error.acce">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">acce</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.acce" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.error.ae">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">ae</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_hat</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.ae" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.error.f1_error">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">f1_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.f1_error" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.error.f1e">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">f1e</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y_true</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.f1e" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.error.from_name">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">from_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">err_name</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.from_name" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.error.kld">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">kld</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_hat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.kld" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.error.mae">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">mae</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prevs_hat</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.mae" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.error.mean_absolute_error">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">mean_absolute_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prevs_hat</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.mean_absolute_error" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.error.mean_relative_absolute_error">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">mean_relative_absolute_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_hat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.mean_relative_absolute_error" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.error.mkld">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">mkld</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prevs_hat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.mkld" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.error.mnkld">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">mnkld</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prevs_hat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.mnkld" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.error.mrae">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">mrae</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_hat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.mrae" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.error.mse">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">mse</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prevs_hat</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.mse" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.error.nkld">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">nkld</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_hat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.nkld" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.error.rae">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">rae</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_hat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.rae" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.error.relative_absolute_error">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">relative_absolute_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_hat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.relative_absolute_error" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.error.se">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">se</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p_hat</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.se" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.error.smooth">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.error.</span></span><span class="sig-name descname"><span class="pre">smooth</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.error.smooth" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-quapy.evaluation">
|
||||
<span id="quapy-evaluation-module"></span><h2>quapy.evaluation module<a class="headerlink" href="#module-quapy.evaluation" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.evaluation.artificial_prevalence_prediction">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.evaluation.</span></span><span class="sig-name descname"><span class="pre">artificial_prevalence_prediction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="quapy.method.html#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><span class="pre">quapy.method.base.BaseQuantifier</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">test</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_prevpoints</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">210</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_repetitions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_budget</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.evaluation.artificial_prevalence_prediction" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Performs the predictions for all samples generated according to the artificial sampling protocol.
|
||||
:param model: the model in charge of generating the class prevalence estimations
|
||||
:param test: the test set on which to perform arificial sampling
|
||||
:param sample_size: the size of the samples
|
||||
:param n_prevpoints: the number of different prevalences to sample (or set to None if eval_budget is specified)
|
||||
:param n_repetitions: the number of repetitions for each prevalence
|
||||
:param eval_budget: if specified, sets a ceil on the number of evaluations to perform. For example, if there are 3
|
||||
classes, n_repetitions=1 and eval_budget=20, then n_prevpoints will be set to 5, since this will generate 15
|
||||
different prevalences ([0, 0, 1], [0, 0.25, 0.75], [0, 0.5, 0.5] … [1, 0, 0]) and since setting it n_prevpoints
|
||||
to 6 would produce more than 20 evaluations.
|
||||
:param n_jobs: number of jobs to be run in parallel
|
||||
:param random_seed: allows to replicate the samplings. The seed is local to the method and does not affect
|
||||
any other random process.
|
||||
:param verbose: if True, shows a progress bar
|
||||
:return: two ndarrays of shape (m,n) with m the number of samples (n_prevpoints*n_repetitions) and n the</p>
|
||||
<blockquote>
|
||||
<div><p>number of classes. The first one contains the true prevalences for the samples generated while the second one
|
||||
contains the the prevalence estimations</p>
|
||||
</div></blockquote>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.evaluation.artificial_prevalence_protocol">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.evaluation.</span></span><span class="sig-name descname"><span class="pre">artificial_prevalence_protocol</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="quapy.method.html#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><span class="pre">quapy.method.base.BaseQuantifier</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">test</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_prevpoints</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">210</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_repetitions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_budget</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_metric</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span> </span><span class="pre">Callable</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">'mae'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.evaluation.artificial_prevalence_protocol" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.evaluation.artificial_prevalence_report">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.evaluation.</span></span><span class="sig-name descname"><span class="pre">artificial_prevalence_report</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="quapy.method.html#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><span class="pre">quapy.method.base.BaseQuantifier</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">test</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_prevpoints</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">210</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_repetitions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_budget</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_metrics</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Iterable</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span> </span><span class="pre">Callable</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">'mae'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.evaluation.artificial_prevalence_report" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.evaluation.evaluate">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.evaluation.</span></span><span class="sig-name descname"><span class="pre">evaluate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="quapy.method.html#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><span class="pre">quapy.method.base.BaseQuantifier</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_samples</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Iterable</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">err</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span> </span><span class="pre">Callable</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.evaluation.evaluate" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.evaluation.gen_prevalence_prediction">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.evaluation.</span></span><span class="sig-name descname"><span class="pre">gen_prevalence_prediction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="quapy.method.html#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><span class="pre">quapy.method.base.BaseQuantifier</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">gen_fn</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Callable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eval_budget</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.evaluation.gen_prevalence_prediction" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.evaluation.natural_prevalence_prediction">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.evaluation.</span></span><span class="sig-name descname"><span class="pre">natural_prevalence_prediction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="quapy.method.html#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><span class="pre">quapy.method.base.BaseQuantifier</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">test</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_repetitions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.evaluation.natural_prevalence_prediction" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Performs the predictions for all samples generated according to the artificial sampling protocol.
|
||||
:param model: the model in charge of generating the class prevalence estimations
|
||||
:param test: the test set on which to perform arificial sampling
|
||||
:param sample_size: the size of the samples
|
||||
:param n_repetitions: the number of repetitions for each prevalence
|
||||
:param n_jobs: number of jobs to be run in parallel
|
||||
:param random_seed: allows to replicate the samplings. The seed is local to the method and does not affect
|
||||
any other random process.
|
||||
:param verbose: if True, shows a progress bar
|
||||
:return: two ndarrays of shape (m,n) with m the number of samples (n_repetitions) and n the</p>
|
||||
<blockquote>
|
||||
<div><p>number of classes. The first one contains the true prevalences for the samples generated while the second one
|
||||
contains the the prevalence estimations</p>
|
||||
</div></blockquote>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.evaluation.natural_prevalence_protocol">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.evaluation.</span></span><span class="sig-name descname"><span class="pre">natural_prevalence_protocol</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="quapy.method.html#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><span class="pre">quapy.method.base.BaseQuantifier</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">test</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_repetitions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_metric</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span> </span><span class="pre">Callable</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">'mae'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.evaluation.natural_prevalence_protocol" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.evaluation.natural_prevalence_report">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.evaluation.</span></span><span class="sig-name descname"><span class="pre">natural_prevalence_report</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="quapy.method.html#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><span class="pre">quapy.method.base.BaseQuantifier</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">test</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_repetitions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">42</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_metrics</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Iterable</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">,</span> </span><span class="pre">Callable</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">'mae'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.evaluation.natural_prevalence_report" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-quapy.functional">
|
||||
<span id="quapy-functional-module"></span><h2>quapy.functional module<a class="headerlink" href="#module-quapy.functional" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.functional.HellingerDistance">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.functional.</span></span><span class="sig-name descname"><span class="pre">HellingerDistance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">P</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Q</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.functional.HellingerDistance" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.functional.adjusted_quantification">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.functional.</span></span><span class="sig-name descname"><span class="pre">adjusted_quantification</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prevalence_estim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fpr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clip</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.functional.adjusted_quantification" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.functional.artificial_prevalence_sampling">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.functional.</span></span><span class="sig-name descname"><span class="pre">artificial_prevalence_sampling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dimensions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_prevalences</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">21</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repeat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_constrained_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.functional.artificial_prevalence_sampling" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.functional.get_nprevpoints_approximation">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.functional.</span></span><span class="sig-name descname"><span class="pre">get_nprevpoints_approximation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">combinations_budget</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_repeats</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.functional.get_nprevpoints_approximation" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Searches for the largest number of (equidistant) prevalence points to define for each of the n_classes classes so that
|
||||
the number of valid prevalences generated as combinations of prevalence points (points in a n_classes-dimensional
|
||||
simplex) do not exceed combinations_budget.
|
||||
:param n_classes: number of classes
|
||||
:param n_repeats: number of repetitions for each prevalence combination
|
||||
:param combinations_budget: maximum number of combinatios allowed
|
||||
:return: the largest number of prevalence points that generate less than combinations_budget valid prevalences</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.functional.normalize_prevalence">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.functional.</span></span><span class="sig-name descname"><span class="pre">normalize_prevalence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prevalences</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.functional.normalize_prevalence" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.functional.num_prevalence_combinations">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.functional.</span></span><span class="sig-name descname"><span class="pre">num_prevalence_combinations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_prevpoints</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_repeats</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">int</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.functional.num_prevalence_combinations" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Computes the number of prevalence combinations in the n_classes-dimensional simplex if nprevpoints equally distant
|
||||
prevalences are generated and n_repeats repetitions are requested
|
||||
:param n_classes: number of classes
|
||||
:param n_prevpoints: number of prevalence points.
|
||||
:param n_repeats: number of repetitions for each prevalence combination
|
||||
:return: The number of possible combinations. For example, if n_classes=2, n_prevpoints=5, n_repeats=1, then the
|
||||
number of possible combinations are 5, i.e.: [0,1], [0.25,0.75], [0.50,0.50], [0.75,0.25], and [1.0,0.0]</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.functional.prevalence_from_labels">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.functional.</span></span><span class="sig-name descname"><span class="pre">prevalence_from_labels</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">labels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">classes_</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.functional.prevalence_from_labels" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.functional.prevalence_from_probabilities">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.functional.</span></span><span class="sig-name descname"><span class="pre">prevalence_from_probabilities</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">posteriors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">binarize</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.functional.prevalence_from_probabilities" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.functional.prevalence_linspace">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.functional.</span></span><span class="sig-name descname"><span class="pre">prevalence_linspace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_prevalences</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">21</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repeat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">smooth_limits_epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.functional.prevalence_linspace" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Produces a uniformly separated values of prevalence. By default, produces an array 21 prevalences, with step 0.05
|
||||
and with the limits smoothed, i.e.:
|
||||
[0.01, 0.05, 0.10, 0.15, …, 0.90, 0.95, 0.99]
|
||||
:param n_prevalences: the number of prevalence values to sample from the [0,1] interval (default 21)
|
||||
:param repeat: number of times each prevalence is to be repeated (defaults to 1)
|
||||
:param smooth_limits_epsilon: the quantity to add and subtract to the limits 0 and 1
|
||||
:return: an array of uniformly separated prevalence values</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.functional.strprev">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.functional.</span></span><span class="sig-name descname"><span class="pre">strprev</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prevalences</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.functional.strprev" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.functional.uniform_prevalence_sampling">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.functional.</span></span><span class="sig-name descname"><span class="pre">uniform_prevalence_sampling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.functional.uniform_prevalence_sampling" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.functional.uniform_simplex_sampling">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.functional.</span></span><span class="sig-name descname"><span class="pre">uniform_simplex_sampling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.functional.uniform_simplex_sampling" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-quapy.model_selection">
|
||||
<span id="quapy-model-selection-module"></span><h2>quapy.model_selection module<a class="headerlink" href="#module-quapy.model_selection" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.model_selection.GridSearchQ">
|
||||
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.model_selection.</span></span><span class="sig-name descname"><span class="pre">GridSearchQ</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">model:</span> <span class="pre">quapy.method.base.BaseQuantifier,</span> <span class="pre">param_grid:</span> <span class="pre">dict,</span> <span class="pre">sample_size:</span> <span class="pre">Optional[int],</span> <span class="pre">protocol='app',</span> <span class="pre">n_prevpoints:</span> <span class="pre">Optional[int]</span> <span class="pre">=</span> <span class="pre">None,</span> <span class="pre">n_repetitions:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1,</span> <span class="pre">eval_budget:</span> <span class="pre">Optional[int]</span> <span class="pre">=</span> <span class="pre">None,</span> <span class="pre">error:</span> <span class="pre">Union[Callable,</span> <span class="pre">str]</span> <span class="pre">=</span> <span class="pre"><function</span> <span class="pre">mae>,</span> <span class="pre">refit=True,</span> <span class="pre">val_split=0.4,</span> <span class="pre">n_jobs=1,</span> <span class="pre">random_seed=42,</span> <span class="pre">timeout=-1,</span> <span class="pre">verbose=False</span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.model_selection.GridSearchQ" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="quapy.method.html#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.base.BaseQuantifier</span></code></a></p>
|
||||
<p>Grid Search optimization targeting a quantification-oriented metric.</p>
|
||||
<p>Optimizes the hyperparameters of a quantification method, based on an evaluation method and on an evaluation
|
||||
protocol for quantification.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>model</strong> (<a class="reference internal" href="quapy.method.html#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><em>BaseQuantifier</em></a>) – the quantifier to optimize</p></li>
|
||||
<li><p><strong>param_grid</strong> – a dictionary with keys the parameter names and values the list of values to explore</p></li>
|
||||
<li><p><strong>sample_size</strong> – the size of the samples to extract from the validation set (ignored if protocl=’gen’)</p></li>
|
||||
<li><p><strong>protocol</strong> – either ‘app’ for the artificial prevalence protocol, ‘npp’ for the natural prevalence
|
||||
protocol, or ‘gen’ for using a custom sampling generator function</p></li>
|
||||
<li><p><strong>n_prevpoints</strong> – if specified, indicates the number of equally distant points to extract from the interval
|
||||
[0,1] in order to define the prevalences of the samples; e.g., if n_prevpoints=5, then the prevalences for
|
||||
each class will be explored in [0.00, 0.25, 0.50, 0.75, 1.00]. If not specified, then eval_budget is requested.
|
||||
Ignored if protocol!=’app’.</p></li>
|
||||
<li><p><strong>n_repetitions</strong> – the number of repetitions for each combination of prevalences. This parameter is ignored
|
||||
for the protocol=’app’ if eval_budget is set and is lower than the number of combinations that would be
|
||||
generated using the value assigned to n_prevpoints (for the current number of classes and n_repetitions).
|
||||
Ignored for protocol=’npp’ and protocol=’gen’ (use eval_budget for setting a maximum number of samples in
|
||||
those cases).</p></li>
|
||||
<li><p><strong>eval_budget</strong> – if specified, sets a ceil on the number of evaluations to perform for each hyper-parameter
|
||||
combination. For example, if protocol=’app’, there are 3 classes, n_repetitions=1 and eval_budget=20, then
|
||||
n_prevpoints will be set to 5, since this will generate 15 different prevalences, i.e., [0, 0, 1],
|
||||
[0, 0.25, 0.75], [0, 0.5, 0.5] … [1, 0, 0], and since setting it to 6 would generate more than
|
||||
20. When protocol=’gen’, indicates the maximum number of samples to generate, but less samples will be
|
||||
generated if the generator yields less samples.</p></li>
|
||||
<li><p><strong>error</strong> – an error function (callable) or a string indicating the name of an error function (valid ones
|
||||
are those in qp.error.QUANTIFICATION_ERROR</p></li>
|
||||
<li><p><strong>refit</strong> – whether or not to refit the model on the whole labelled collection (training+validation) with
|
||||
the best chosen hyperparameter combination. Ignored if protocol=’gen’</p></li>
|
||||
<li><p><strong>val_split</strong> – either a LabelledCollection on which to test the performance of the different settings, or
|
||||
a float in [0,1] indicating the proportion of labelled data to extract from the training set, or a callable
|
||||
returning a generator function each time it is invoked (only for protocol=’gen’).</p></li>
|
||||
<li><p><strong>n_jobs</strong> – number of parallel jobs</p></li>
|
||||
<li><p><strong>random_seed</strong> – set the seed of the random generator to replicate experiments. Ignored if protocol=’gen’.</p></li>
|
||||
<li><p><strong>timeout</strong> – establishes a timer (in seconds) for each of the hyperparameters configurations being tested.
|
||||
Whenever a run takes longer than this timer, that configuration will be ignored. If all configurations end up
|
||||
being ignored, a TimeoutError exception is raised. If -1 (default) then no time bound is set.</p></li>
|
||||
<li><p><strong>verbose</strong> – set to True to get information through the stdout</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.model_selection.GridSearchQ.best_model">
|
||||
<span class="sig-name descname"><span class="pre">best_model</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#quapy.model_selection.GridSearchQ.best_model" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py property">
|
||||
<dt class="sig sig-object py" id="quapy.model_selection.GridSearchQ.classes_">
|
||||
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">classes_</span></span><a class="headerlink" href="#quapy.model_selection.GridSearchQ.classes_" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.model_selection.GridSearchQ.fit">
|
||||
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a><span class="p"><span class="pre">,</span> </span><span class="pre">float</span><span class="p"><span class="pre">,</span> </span><span class="pre">Callable</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.model_selection.GridSearchQ.fit" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><dl class="simple">
|
||||
<dt>Learning routine. Fits methods with all combinations of hyperparameters and selects the one minimizing</dt><dd><p>the error metric.</p>
|
||||
</dd>
|
||||
</dl>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>training</strong> – the training set on which to optimize the hyperparameters</p></li>
|
||||
<li><p><strong>val_split</strong> – either a LabelledCollection on which to test the performance of the different settings, or
|
||||
a float in [0,1] indicating the proportion of labelled data to extract from the training set</p></li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.model_selection.GridSearchQ.get_params">
|
||||
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.model_selection.GridSearchQ.get_params" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Returns the dictionary of hyper-parameters to explore (<cite>param_grid</cite>)</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
<dd class="field-odd"><p><strong>deep</strong> – Unused</p>
|
||||
</dd>
|
||||
<dt class="field-even">Returns</dt>
|
||||
<dd class="field-even"><p>the dictionary <cite>param_grid</cite></p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.model_selection.GridSearchQ.quantify">
|
||||
<span class="sig-name descname"><span class="pre">quantify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.model_selection.GridSearchQ.quantify" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Estimate class prevalence values</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
<dd class="field-odd"><p><strong>instances</strong> – sample contanining the instances</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py method">
|
||||
<dt class="sig sig-object py" id="quapy.model_selection.GridSearchQ.set_params">
|
||||
<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">parameters</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.model_selection.GridSearchQ.set_params" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Sets the hyper-parameters to explore.</p>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
<dd class="field-odd"><p><strong>parameters</strong> – a dictionary with keys the parameter names and values the list of values to explore</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-quapy.plot">
|
||||
<span id="quapy-plot-module"></span><h2>quapy.plot module<a class="headerlink" href="#module-quapy.plot" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.plot.binary_bias_bins">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.plot.</span></span><span class="sig-name descname"><span class="pre">binary_bias_bins</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">method_names</span></em>, <em class="sig-param"><span class="pre">true_prevs</span></em>, <em class="sig-param"><span class="pre">estim_prevs</span></em>, <em class="sig-param"><span class="pre">pos_class=1</span></em>, <em class="sig-param"><span class="pre">title=None</span></em>, <em class="sig-param"><span class="pre">nbins=5</span></em>, <em class="sig-param"><span class="pre">colormap=<matplotlib.colors.ListedColormap</span> <span class="pre">object></span></em>, <em class="sig-param"><span class="pre">vertical_xticks=False</span></em>, <em class="sig-param"><span class="pre">legend=True</span></em>, <em class="sig-param"><span class="pre">savepath=None</span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.plot.binary_bias_bins" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.plot.binary_bias_global">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.plot.</span></span><span class="sig-name descname"><span class="pre">binary_bias_global</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">true_prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estim_prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_class</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">savepath</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.plot.binary_bias_global" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.plot.binary_diagonal">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.plot.</span></span><span class="sig-name descname"><span class="pre">binary_diagonal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">true_prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estim_prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_class</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_prev</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">savepath</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.plot.binary_diagonal" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.plot.error_by_drift">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.plot.</span></span><span class="sig-name descname"><span class="pre">error_by_drift</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">true_prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estim_prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tr_prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ae'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logscale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Quantification</span> <span class="pre">error</span> <span class="pre">as</span> <span class="pre">a</span> <span class="pre">function</span> <span class="pre">of</span> <span class="pre">distribution</span> <span class="pre">shift'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">savepath</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.plot.error_by_drift" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.plot.save_or_show">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.plot.</span></span><span class="sig-name descname"><span class="pre">save_or_show</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">savepath</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.plot.save_or_show" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-quapy.util">
|
||||
<span id="quapy-util-module"></span><h2>quapy.util module<a class="headerlink" href="#module-quapy.util" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="py class">
|
||||
<dt class="sig sig-object py" id="quapy.util.EarlyStop">
|
||||
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.util.</span></span><span class="sig-name descname"><span class="pre">EarlyStop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">patience</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lower_is_better</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.util.EarlyStop" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.util.create_if_not_exist">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.util.</span></span><span class="sig-name descname"><span class="pre">create_if_not_exist</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.util.create_if_not_exist" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.util.create_parent_dir">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.util.</span></span><span class="sig-name descname"><span class="pre">create_parent_dir</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.util.create_parent_dir" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.util.download_file">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.util.</span></span><span class="sig-name descname"><span class="pre">download_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">url</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">archive_filename</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.util.download_file" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.util.download_file_if_not_exists">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.util.</span></span><span class="sig-name descname"><span class="pre">download_file_if_not_exists</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">url</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">archive_path</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.util.download_file_if_not_exists" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.util.get_quapy_home">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.util.</span></span><span class="sig-name descname"><span class="pre">get_quapy_home</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#quapy.util.get_quapy_home" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.util.map_parallel">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.util.</span></span><span class="sig-name descname"><span class="pre">map_parallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.util.map_parallel" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Applies func to n_jobs slices of args. E.g., if args is an array of 99 items and n_jobs=2, then
|
||||
func is applied in two parallel processes to args[0:50] and to args[50:99]</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.util.parallel">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.util.</span></span><span class="sig-name descname"><span class="pre">parallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.util.parallel" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>A wrapper of multiprocessing:
|
||||
Parallel(n_jobs=n_jobs)(</p>
|
||||
<blockquote>
|
||||
<div><p>delayed(func)(args_i) for args_i in args</p>
|
||||
</div></blockquote>
|
||||
<p>)
|
||||
that takes the quapy.environ variable as input silently</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.util.pickled_resource">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.util.</span></span><span class="sig-name descname"><span class="pre">pickled_resource</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pickle_path</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generation_func</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">callable</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.util.pickled_resource" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Allows for fast reuse of resources that are generated only once by calling generation_func(<a href="#id1"><span class="problematic" id="id2">*</span></a>args). The next times
|
||||
this function is invoked, it loads the pickled resource. Example:
|
||||
def some_array(n):</p>
|
||||
<blockquote>
|
||||
<div><p>return np.random.rand(n)</p>
|
||||
</div></blockquote>
|
||||
<p>pickled_resource(‘./my_array.pkl’, some_array, 10) # the resource does not exist: it is created by some_array(10)
|
||||
pickled_resource(‘./my_array.pkl’, some_array, 10) # the resource exists: it is loaded from ‘./my_array.pkl’
|
||||
:param pickle_path: the path where to save (first time) and load (next times) the resource
|
||||
:param generation_func: the function that generates the resource, in case it does not exist in pickle_path
|
||||
:param args: any arg that generation_func uses for generating the resources
|
||||
:return: the resource</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.util.save_text_file">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.util.</span></span><span class="sig-name descname"><span class="pre">save_text_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">text</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.util.save_text_file" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.util.temp_seed">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.util.</span></span><span class="sig-name descname"><span class="pre">temp_seed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.util.temp_seed" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Can be used in a “with” context to set a temporal seed without modifying the outer numpy’s current state. E.g.:
|
||||
with temp_seed(random_seed):</p>
|
||||
<blockquote>
|
||||
<div><p># do any computation depending on np.random functionality</p>
|
||||
</div></blockquote>
|
||||
<dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
<dd class="field-odd"><p><strong>seed</strong> – the seed to set within the “with” context</p>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
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|
||||
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|
||||
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-quapy" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="py function">
|
||||
<dt class="sig sig-object py" id="quapy.isbinary">
|
||||
<span class="sig-prename descclassname"><span class="pre">quapy.</span></span><span class="sig-name descname"><span class="pre">isbinary</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.isbinary" title="Permalink to this definition">¶</a></dt>
|
||||
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|
||||
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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<ul>
|
||||
<li class="right" style="margin-right: 10px">
|
||||
<a href="genindex.html" title="General Index"
|
||||
>index</a></li>
|
||||
<li class="right" >
|
||||
<a href="py-modindex.html" title="Python Module Index"
|
||||
>modules</a> |</li>
|
||||
<li class="right" >
|
||||
<a href="modules.html" title="quapy"
|
||||
>next</a> |</li>
|
||||
<li class="right" >
|
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<a href="index.html" title="Welcome to QuaPy’s documentation!"
|
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>previous</a> |</li>
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<li class="nav-item nav-item-0"><a href="index.html">QuaPy 0.1.6 documentation</a> »</li>
|
||||
<li class="nav-item nav-item-this"><a href="">Getting Started</a></li>
|
||||
</ul>
|
||||
</div>
|
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<div class="footer" role="contentinfo">
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© Copyright 2021, Alejandro Moreo.
|
||||
Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.2.0.
|
||||
</div>
|
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</body>
|
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</html>
|
||||
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|
@ -0,0 +1,92 @@
|
|||
|
||||
|
||||
<!doctype html>
|
||||
|
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<html>
|
||||
<head>
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
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<title><no title> — QuaPy 0.1.6 documentation</title>
|
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<link rel="stylesheet" type="text/css" href="_static/pygments.css" />
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<link rel="stylesheet" type="text/css" href="_static/bizstyle.css" />
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<script src="_static/jquery.js"></script>
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<script src="_static/underscore.js"></script>
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<script src="_static/doctools.js"></script>
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<link rel="index" title="Index" href="genindex.html" />
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<link rel="search" title="Search" href="search.html" />
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<meta name="viewport" content="width=device-width,initial-scale=1.0" />
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<script src="_static/css3-mediaqueries.js"></script>
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<![endif]-->
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</head><body>
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<div class="related" role="navigation" aria-label="related navigation">
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<h3>Navigation</h3>
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<ul>
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<li class="right" style="margin-right: 10px">
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<a href="genindex.html" title="General Index"
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accesskey="I">index</a></li>
|
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<li class="right" >
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<a href="py-modindex.html" title="Python Module Index"
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>modules</a> |</li>
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<li class="nav-item nav-item-0"><a href="index.html">QuaPy 0.1.6 documentation</a> »</li>
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<li class="nav-item nav-item-this"><a href=""><no title></a></li>
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</div>
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<div class="document">
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<div class="documentwrapper">
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<div class="bodywrapper">
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<div class="body" role="main">
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<p>.. include:: ../../README.md</p>
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<div class="clearer"></div>
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<ul class="this-page-menu">
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<li><a href="_sources/readme2.md.txt"
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<h3 id="searchlabel">Quick search</h3>
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<div class="searchformwrapper">
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<form class="search" action="search.html" method="get">
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<input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
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<input type="submit" value="Go" />
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</form>
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</div>
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</div>
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<script>$('#searchbox').show(0);</script>
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</div>
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</div>
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<div class="clearer"></div>
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</div>
|
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<div class="related" role="navigation" aria-label="related navigation">
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<h3>Navigation</h3>
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<ul>
|
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<li class="right" style="margin-right: 10px">
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<a href="genindex.html" title="General Index"
|
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>index</a></li>
|
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<li class="right" >
|
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<a href="py-modindex.html" title="Python Module Index"
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>modules</a> |</li>
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<li class="nav-item nav-item-0"><a href="index.html">QuaPy 0.1.6 documentation</a> »</li>
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<li class="nav-item nav-item-this"><a href=""><no title></a></li>
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</ul>
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</div>
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<div class="footer" role="contentinfo">
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© Copyright 2021, Alejandro Moreo.
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Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.2.0.
|
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</div>
|
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</body>
|
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</html>
|
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|
|
@ -0,0 +1,109 @@
|
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|
||||
|
||||
<!doctype html>
|
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|
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<html>
|
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<head>
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<meta charset="utf-8" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<title>Search — QuaPy 0.1.6 documentation</title>
|
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<link rel="stylesheet" type="text/css" href="_static/pygments.css" />
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<link rel="stylesheet" type="text/css" href="_static/bizstyle.css" />
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<script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
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<script src="_static/jquery.js"></script>
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<script src="_static/underscore.js"></script>
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<script src="_static/doctools.js"></script>
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<script src="_static/searchtools.js"></script>
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<script src="_static/language_data.js"></script>
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<link rel="index" title="Index" href="genindex.html" />
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<link rel="search" title="Search" href="#" />
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<script src="searchindex.js" defer></script>
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<meta name="viewport" content="width=device-width,initial-scale=1.0" />
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<script src="_static/css3-mediaqueries.js"></script>
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<![endif]-->
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</head><body>
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<div class="related" role="navigation" aria-label="related navigation">
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<h3>Navigation</h3>
|
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<ul>
|
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<li class="right" style="margin-right: 10px">
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<a href="genindex.html" title="General Index"
|
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accesskey="I">index</a></li>
|
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<li class="right" >
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<a href="py-modindex.html" title="Python Module Index"
|
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>modules</a> |</li>
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<li class="nav-item nav-item-0"><a href="index.html">QuaPy 0.1.6 documentation</a> »</li>
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<li class="nav-item nav-item-this"><a href="">Search</a></li>
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</ul>
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</div>
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<div class="document">
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<div class="documentwrapper">
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<div class="bodywrapper">
|
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<div class="body" role="main">
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<h1 id="search-documentation">Search</h1>
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<noscript>
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<div class="admonition warning">
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<p>
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Please activate JavaScript to enable the search
|
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functionality.
|
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</p>
|
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</div>
|
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</noscript>
|
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|
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|
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<p>
|
||||
Searching for multiple words only shows matches that contain
|
||||
all words.
|
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</p>
|
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|
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|
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<form action="" method="get">
|
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<input type="text" name="q" aria-labelledby="search-documentation" value="" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
|
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<input type="submit" value="search" />
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<span id="search-progress" style="padding-left: 10px"></span>
|
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</form>
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|
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|
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<div id="search-results">
|
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|
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</div>
|
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|
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|
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<div class="clearer"></div>
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</div>
|
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</div>
|
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</div>
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<div class="sphinxsidebar" role="navigation" aria-label="main navigation">
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<div class="sphinxsidebarwrapper">
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<h3>Navigation</h3>
|
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<ul>
|
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<li class="right" style="margin-right: 10px">
|
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<a href="genindex.html" title="General Index"
|
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>index</a></li>
|
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<li class="right" >
|
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<a href="py-modindex.html" title="Python Module Index"
|
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>modules</a> |</li>
|
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<li class="nav-item nav-item-0"><a href="index.html">QuaPy 0.1.6 documentation</a> »</li>
|
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<li class="nav-item nav-item-this"><a href="">Search</a></li>
|
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</ul>
|
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</div>
|
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<div class="footer" role="contentinfo">
|
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© Copyright 2021, Alejandro Moreo.
|
||||
Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.2.0.
|
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</div>
|
||||
</body>
|
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</html>
|
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Reference in New Issue