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<li class="nav-item nav-item-0"><a href="index.html">QuaPy 0.1.6 documentation</a> &#187;</li> <li class="nav-item nav-item-0"><a href="index.html">QuaPy 0.1.7 documentation</a> &#187;</li>
<li class="nav-item nav-item-this"><a href="">Datasets</a></li> <li class="nav-item nav-item-this"><a href="">Datasets</a></li>
</ul> </ul>
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@ -49,8 +52,8 @@
<div class="bodywrapper"> <div class="bodywrapper">
<div class="body" role="main"> <div class="body" role="main">
<div class="tex2jax_ignore mathjax_ignore section" id="datasets"> <section id="datasets">
<h1>Datasets<a class="headerlink" href="#datasets" title="Permalink to this headline"></a></h1> <h1>Datasets<a class="headerlink" href="#datasets" title="Permalink to this heading"></a></h1>
<p>QuaPy makes available several datasets that have been used in <p>QuaPy makes available several datasets that have been used in
quantification literature, as well as an interface to allow quantification literature, as well as an interface to allow
anyone import their custom datasets.</p> anyone import their custom datasets.</p>
@ -129,8 +132,8 @@ that is:</p>
<p>See the <a class="reference external" href="https://github.com/HLT-ISTI/QuaPy/wiki/Evaluation">Evaluation wiki</a> for <p>See the <a class="reference external" href="https://github.com/HLT-ISTI/QuaPy/wiki/Evaluation">Evaluation wiki</a> for
further details on how to use the artificial sampling protocol to properly further details on how to use the artificial sampling protocol to properly
evaluate a quantification method.</p> evaluate a quantification method.</p>
<div class="section" id="reviews-datasets"> <section id="reviews-datasets">
<h2>Reviews Datasets<a class="headerlink" href="#reviews-datasets" title="Permalink to this headline"></a></h2> <h2>Reviews Datasets<a class="headerlink" href="#reviews-datasets" title="Permalink to this heading"></a></h2>
<p>Three datasets of reviews about Kindle devices, Harry Potter’s series, and <p>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. the well-known IMDb movie reviews can be fetched using a unified interface.
For example:</p> For example:</p>
@ -150,47 +153,47 @@ For example:</p>
</pre></div> </pre></div>
</div> </div>
<p>Some statistics of the fhe available datasets are summarized below:</p> <p>Some statistics of the fhe available datasets are summarized below:</p>
<table class="colwidths-auto docutils align-default"> <table class="docutils align-default">
<thead> <thead>
<tr class="row-odd"><th class="head"><p>Dataset</p></th> <tr class="row-odd"><th class="head"><p>Dataset</p></th>
<th class="text-align:center head"><p>classes</p></th> <th class="head text-center"><p>classes</p></th>
<th class="text-align:center head"><p>train size</p></th> <th class="head text-center"><p>train size</p></th>
<th class="text-align:center head"><p>test size</p></th> <th class="head text-center"><p>test size</p></th>
<th class="text-align:center head"><p>train prev</p></th> <th class="head text-center"><p>train prev</p></th>
<th class="text-align:center head"><p>test prev</p></th> <th class="head text-center"><p>test prev</p></th>
<th class="head"><p>type</p></th> <th class="head"><p>type</p></th>
</tr> </tr>
</thead> </thead>
<tbody> <tbody>
<tr class="row-even"><td><p>hp</p></td> <tr class="row-even"><td><p>hp</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>9533</p></td> <td class="text-center"><p>9533</p></td>
<td class="text-align:center"><p>18399</p></td> <td class="text-center"><p>18399</p></td>
<td class="text-align:center"><p>[0.018, 0.982]</p></td> <td class="text-center"><p>[0.018, 0.982]</p></td>
<td class="text-align:center"><p>[0.065, 0.935]</p></td> <td class="text-center"><p>[0.065, 0.935]</p></td>
<td><p>text</p></td> <td><p>text</p></td>
</tr> </tr>
<tr class="row-odd"><td><p>kindle</p></td> <tr class="row-odd"><td><p>kindle</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>3821</p></td> <td class="text-center"><p>3821</p></td>
<td class="text-align:center"><p>21591</p></td> <td class="text-center"><p>21591</p></td>
<td class="text-align:center"><p>[0.081, 0.919]</p></td> <td class="text-center"><p>[0.081, 0.919]</p></td>
<td class="text-align:center"><p>[0.063, 0.937]</p></td> <td class="text-center"><p>[0.063, 0.937]</p></td>
<td><p>text</p></td> <td><p>text</p></td>
</tr> </tr>
<tr class="row-even"><td><p>imdb</p></td> <tr class="row-even"><td><p>imdb</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>25000</p></td> <td class="text-center"><p>25000</p></td>
<td class="text-align:center"><p>25000</p></td> <td class="text-center"><p>25000</p></td>
<td class="text-align:center"><p>[0.500, 0.500]</p></td> <td class="text-center"><p>[0.500, 0.500]</p></td>
<td class="text-align:center"><p>[0.500, 0.500]</p></td> <td class="text-center"><p>[0.500, 0.500]</p></td>
<td><p>text</p></td> <td><p>text</p></td>
</tr> </tr>
</tbody> </tbody>
</table> </table>
</div> </section>
<div class="section" id="twitter-sentiment-datasets"> <section id="twitter-sentiment-datasets">
<h2>Twitter Sentiment Datasets<a class="headerlink" href="#twitter-sentiment-datasets" title="Permalink to this headline"></a></h2> <h2>Twitter Sentiment Datasets<a class="headerlink" href="#twitter-sentiment-datasets" title="Permalink to this heading"></a></h2>
<p>11 Twitter datasets for sentiment analysis. <p>11 Twitter datasets for sentiment analysis.
Text is not accessible, and the documents were made available Text is not accessible, and the documents were made available
in tf-idf format. Each dataset presents two splits: a train/val in tf-idf format. Each dataset presents two splits: a train/val
@ -221,123 +224,123 @@ The lists of the Twitter dataset’s ids can be consulted in:</p>
</pre></div> </pre></div>
</div> </div>
<p>Some details can be found below:</p> <p>Some details can be found below:</p>
<table class="colwidths-auto docutils align-default"> <table class="docutils align-default">
<thead> <thead>
<tr class="row-odd"><th class="head"><p>Dataset</p></th> <tr class="row-odd"><th class="head"><p>Dataset</p></th>
<th class="text-align:center head"><p>classes</p></th> <th class="head text-center"><p>classes</p></th>
<th class="text-align:center head"><p>train size</p></th> <th class="head text-center"><p>train size</p></th>
<th class="text-align:center head"><p>test size</p></th> <th class="head text-center"><p>test size</p></th>
<th class="text-align:center head"><p>features</p></th> <th class="head text-center"><p>features</p></th>
<th class="text-align:center head"><p>train prev</p></th> <th class="head text-center"><p>train prev</p></th>
<th class="text-align:center head"><p>test prev</p></th> <th class="head text-center"><p>test prev</p></th>
<th class="head"><p>type</p></th> <th class="head"><p>type</p></th>
</tr> </tr>
</thead> </thead>
<tbody> <tbody>
<tr class="row-even"><td><p>gasp</p></td> <tr class="row-even"><td><p>gasp</p></td>
<td class="text-align:center"><p>3</p></td> <td class="text-center"><p>3</p></td>
<td class="text-align:center"><p>8788</p></td> <td class="text-center"><p>8788</p></td>
<td class="text-align:center"><p>3765</p></td> <td class="text-center"><p>3765</p></td>
<td class="text-align:center"><p>694582</p></td> <td class="text-center"><p>694582</p></td>
<td class="text-align:center"><p>[0.421, 0.496, 0.082]</p></td> <td class="text-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 class="text-center"><p>[0.407, 0.507, 0.086]</p></td>
<td><p>sparse</p></td> <td><p>sparse</p></td>
</tr> </tr>
<tr class="row-odd"><td><p>hcr</p></td> <tr class="row-odd"><td><p>hcr</p></td>
<td class="text-align:center"><p>3</p></td> <td class="text-center"><p>3</p></td>
<td class="text-align:center"><p>1594</p></td> <td class="text-center"><p>1594</p></td>
<td class="text-align:center"><p>798</p></td> <td class="text-center"><p>798</p></td>
<td class="text-align:center"><p>222046</p></td> <td class="text-center"><p>222046</p></td>
<td class="text-align:center"><p>[0.546, 0.211, 0.243]</p></td> <td class="text-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 class="text-center"><p>[0.640, 0.167, 0.193]</p></td>
<td><p>sparse</p></td> <td><p>sparse</p></td>
</tr> </tr>
<tr class="row-even"><td><p>omd</p></td> <tr class="row-even"><td><p>omd</p></td>
<td class="text-align:center"><p>3</p></td> <td class="text-center"><p>3</p></td>
<td class="text-align:center"><p>1839</p></td> <td class="text-center"><p>1839</p></td>
<td class="text-align:center"><p>787</p></td> <td class="text-center"><p>787</p></td>
<td class="text-align:center"><p>199151</p></td> <td class="text-center"><p>199151</p></td>
<td class="text-align:center"><p>[0.463, 0.271, 0.266]</p></td> <td class="text-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 class="text-center"><p>[0.437, 0.283, 0.280]</p></td>
<td><p>sparse</p></td> <td><p>sparse</p></td>
</tr> </tr>
<tr class="row-odd"><td><p>sanders</p></td> <tr class="row-odd"><td><p>sanders</p></td>
<td class="text-align:center"><p>3</p></td> <td class="text-center"><p>3</p></td>
<td class="text-align:center"><p>2155</p></td> <td class="text-center"><p>2155</p></td>
<td class="text-align:center"><p>923</p></td> <td class="text-center"><p>923</p></td>
<td class="text-align:center"><p>229399</p></td> <td class="text-center"><p>229399</p></td>
<td class="text-align:center"><p>[0.161, 0.691, 0.148]</p></td> <td class="text-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 class="text-center"><p>[0.164, 0.688, 0.148]</p></td>
<td><p>sparse</p></td> <td><p>sparse</p></td>
</tr> </tr>
<tr class="row-even"><td><p>semeval13</p></td> <tr class="row-even"><td><p>semeval13</p></td>
<td class="text-align:center"><p>3</p></td> <td class="text-center"><p>3</p></td>
<td class="text-align:center"><p>11338</p></td> <td class="text-center"><p>11338</p></td>
<td class="text-align:center"><p>3813</p></td> <td class="text-center"><p>3813</p></td>
<td class="text-align:center"><p>1215742</p></td> <td class="text-center"><p>1215742</p></td>
<td class="text-align:center"><p>[0.159, 0.470, 0.372]</p></td> <td class="text-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 class="text-center"><p>[0.158, 0.430, 0.412]</p></td>
<td><p>sparse</p></td> <td><p>sparse</p></td>
</tr> </tr>
<tr class="row-odd"><td><p>semeval14</p></td> <tr class="row-odd"><td><p>semeval14</p></td>
<td class="text-align:center"><p>3</p></td> <td class="text-center"><p>3</p></td>
<td class="text-align:center"><p>11338</p></td> <td class="text-center"><p>11338</p></td>
<td class="text-align:center"><p>1853</p></td> <td class="text-center"><p>1853</p></td>
<td class="text-align:center"><p>1215742</p></td> <td class="text-center"><p>1215742</p></td>
<td class="text-align:center"><p>[0.159, 0.470, 0.372]</p></td> <td class="text-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 class="text-center"><p>[0.109, 0.361, 0.530]</p></td>
<td><p>sparse</p></td> <td><p>sparse</p></td>
</tr> </tr>
<tr class="row-even"><td><p>semeval15</p></td> <tr class="row-even"><td><p>semeval15</p></td>
<td class="text-align:center"><p>3</p></td> <td class="text-center"><p>3</p></td>
<td class="text-align:center"><p>11338</p></td> <td class="text-center"><p>11338</p></td>
<td class="text-align:center"><p>2390</p></td> <td class="text-center"><p>2390</p></td>
<td class="text-align:center"><p>1215742</p></td> <td class="text-center"><p>1215742</p></td>
<td class="text-align:center"><p>[0.159, 0.470, 0.372]</p></td> <td class="text-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 class="text-center"><p>[0.153, 0.413, 0.434]</p></td>
<td><p>sparse</p></td> <td><p>sparse</p></td>
</tr> </tr>
<tr class="row-odd"><td><p>semeval16</p></td> <tr class="row-odd"><td><p>semeval16</p></td>
<td class="text-align:center"><p>3</p></td> <td class="text-center"><p>3</p></td>
<td class="text-align:center"><p>8000</p></td> <td class="text-center"><p>8000</p></td>
<td class="text-align:center"><p>2000</p></td> <td class="text-center"><p>2000</p></td>
<td class="text-align:center"><p>889504</p></td> <td class="text-center"><p>889504</p></td>
<td class="text-align:center"><p>[0.157, 0.351, 0.492]</p></td> <td class="text-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 class="text-center"><p>[0.163, 0.341, 0.497]</p></td>
<td><p>sparse</p></td> <td><p>sparse</p></td>
</tr> </tr>
<tr class="row-even"><td><p>sst</p></td> <tr class="row-even"><td><p>sst</p></td>
<td class="text-align:center"><p>3</p></td> <td class="text-center"><p>3</p></td>
<td class="text-align:center"><p>2971</p></td> <td class="text-center"><p>2971</p></td>
<td class="text-align:center"><p>1271</p></td> <td class="text-center"><p>1271</p></td>
<td class="text-align:center"><p>376132</p></td> <td class="text-center"><p>376132</p></td>
<td class="text-align:center"><p>[0.261, 0.452, 0.288]</p></td> <td class="text-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 class="text-center"><p>[0.207, 0.481, 0.312]</p></td>
<td><p>sparse</p></td> <td><p>sparse</p></td>
</tr> </tr>
<tr class="row-odd"><td><p>wa</p></td> <tr class="row-odd"><td><p>wa</p></td>
<td class="text-align:center"><p>3</p></td> <td class="text-center"><p>3</p></td>
<td class="text-align:center"><p>2184</p></td> <td class="text-center"><p>2184</p></td>
<td class="text-align:center"><p>936</p></td> <td class="text-center"><p>936</p></td>
<td class="text-align:center"><p>248563</p></td> <td class="text-center"><p>248563</p></td>
<td class="text-align:center"><p>[0.305, 0.414, 0.281]</p></td> <td class="text-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 class="text-center"><p>[0.282, 0.446, 0.272]</p></td>
<td><p>sparse</p></td> <td><p>sparse</p></td>
</tr> </tr>
<tr class="row-even"><td><p>wb</p></td> <tr class="row-even"><td><p>wb</p></td>
<td class="text-align:center"><p>3</p></td> <td class="text-center"><p>3</p></td>
<td class="text-align:center"><p>4259</p></td> <td class="text-center"><p>4259</p></td>
<td class="text-align:center"><p>1823</p></td> <td class="text-center"><p>1823</p></td>
<td class="text-align:center"><p>404333</p></td> <td class="text-center"><p>404333</p></td>
<td class="text-align:center"><p>[0.270, 0.392, 0.337]</p></td> <td class="text-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 class="text-center"><p>[0.274, 0.392, 0.335]</p></td>
<td><p>sparse</p></td> <td><p>sparse</p></td>
</tr> </tr>
</tbody> </tbody>
</table> </table>
</div> </section>
<div class="section" id="uci-machine-learning"> <section id="uci-machine-learning">
<h2>UCI Machine Learning<a class="headerlink" href="#uci-machine-learning" title="Permalink to this headline"></a></h2> <h2>UCI Machine Learning<a class="headerlink" href="#uci-machine-learning" title="Permalink to this heading"></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> <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> 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">&amp;</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> <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">&amp;</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>
@ -371,252 +374,252 @@ training+test dataset at a time, following a kFCV protocol:</p>
<p>Above code will allow to conduct a 2x5FCV evaluation on the “yeast” dataset.</p> <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 <p>All datasets come in numerical form (dense matrices); some statistics
are summarized below.</p> are summarized below.</p>
<table class="colwidths-auto docutils align-default"> <table class="docutils align-default">
<thead> <thead>
<tr class="row-odd"><th class="head"><p>Dataset</p></th> <tr class="row-odd"><th class="head"><p>Dataset</p></th>
<th class="text-align:center head"><p>classes</p></th> <th class="head text-center"><p>classes</p></th>
<th class="text-align:center head"><p>instances</p></th> <th class="head text-center"><p>instances</p></th>
<th class="text-align:center head"><p>features</p></th> <th class="head text-center"><p>features</p></th>
<th class="text-align:center head"><p>prev</p></th> <th class="head text-center"><p>prev</p></th>
<th class="head"><p>type</p></th> <th class="head"><p>type</p></th>
</tr> </tr>
</thead> </thead>
<tbody> <tbody>
<tr class="row-even"><td><p>acute.a</p></td> <tr class="row-even"><td><p>acute.a</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>120</p></td> <td class="text-center"><p>120</p></td>
<td class="text-align:center"><p>6</p></td> <td class="text-center"><p>6</p></td>
<td class="text-align:center"><p>[0.508, 0.492]</p></td> <td class="text-center"><p>[0.508, 0.492]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-odd"><td><p>acute.b</p></td> <tr class="row-odd"><td><p>acute.b</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>120</p></td> <td class="text-center"><p>120</p></td>
<td class="text-align:center"><p>6</p></td> <td class="text-center"><p>6</p></td>
<td class="text-align:center"><p>[0.583, 0.417]</p></td> <td class="text-center"><p>[0.583, 0.417]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-even"><td><p>balance.1</p></td> <tr class="row-even"><td><p>balance.1</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>625</p></td> <td class="text-center"><p>625</p></td>
<td class="text-align:center"><p>4</p></td> <td class="text-center"><p>4</p></td>
<td class="text-align:center"><p>[0.539, 0.461]</p></td> <td class="text-center"><p>[0.539, 0.461]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-odd"><td><p>balance.2</p></td> <tr class="row-odd"><td><p>balance.2</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>625</p></td> <td class="text-center"><p>625</p></td>
<td class="text-align:center"><p>4</p></td> <td class="text-center"><p>4</p></td>
<td class="text-align:center"><p>[0.922, 0.078]</p></td> <td class="text-center"><p>[0.922, 0.078]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-even"><td><p>balance.3</p></td> <tr class="row-even"><td><p>balance.3</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>625</p></td> <td class="text-center"><p>625</p></td>
<td class="text-align:center"><p>4</p></td> <td class="text-center"><p>4</p></td>
<td class="text-align:center"><p>[0.539, 0.461]</p></td> <td class="text-center"><p>[0.539, 0.461]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-odd"><td><p>breast-cancer</p></td> <tr class="row-odd"><td><p>breast-cancer</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>683</p></td> <td class="text-center"><p>683</p></td>
<td class="text-align:center"><p>9</p></td> <td class="text-center"><p>9</p></td>
<td class="text-align:center"><p>[0.350, 0.650]</p></td> <td class="text-center"><p>[0.350, 0.650]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-even"><td><p>cmc.1</p></td> <tr class="row-even"><td><p>cmc.1</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>1473</p></td> <td class="text-center"><p>1473</p></td>
<td class="text-align:center"><p>9</p></td> <td class="text-center"><p>9</p></td>
<td class="text-align:center"><p>[0.573, 0.427]</p></td> <td class="text-center"><p>[0.573, 0.427]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-odd"><td><p>cmc.2</p></td> <tr class="row-odd"><td><p>cmc.2</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>1473</p></td> <td class="text-center"><p>1473</p></td>
<td class="text-align:center"><p>9</p></td> <td class="text-center"><p>9</p></td>
<td class="text-align:center"><p>[0.774, 0.226]</p></td> <td class="text-center"><p>[0.774, 0.226]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-even"><td><p>cmc.3</p></td> <tr class="row-even"><td><p>cmc.3</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>1473</p></td> <td class="text-center"><p>1473</p></td>
<td class="text-align:center"><p>9</p></td> <td class="text-center"><p>9</p></td>
<td class="text-align:center"><p>[0.653, 0.347]</p></td> <td class="text-center"><p>[0.653, 0.347]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-odd"><td><p>ctg.1</p></td> <tr class="row-odd"><td><p>ctg.1</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>2126</p></td> <td class="text-center"><p>2126</p></td>
<td class="text-align:center"><p>22</p></td> <td class="text-center"><p>22</p></td>
<td class="text-align:center"><p>[0.222, 0.778]</p></td> <td class="text-center"><p>[0.222, 0.778]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-even"><td><p>ctg.2</p></td> <tr class="row-even"><td><p>ctg.2</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>2126</p></td> <td class="text-center"><p>2126</p></td>
<td class="text-align:center"><p>22</p></td> <td class="text-center"><p>22</p></td>
<td class="text-align:center"><p>[0.861, 0.139]</p></td> <td class="text-center"><p>[0.861, 0.139]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-odd"><td><p>ctg.3</p></td> <tr class="row-odd"><td><p>ctg.3</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>2126</p></td> <td class="text-center"><p>2126</p></td>
<td class="text-align:center"><p>22</p></td> <td class="text-center"><p>22</p></td>
<td class="text-align:center"><p>[0.917, 0.083]</p></td> <td class="text-center"><p>[0.917, 0.083]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-even"><td><p>german</p></td> <tr class="row-even"><td><p>german</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>1000</p></td> <td class="text-center"><p>1000</p></td>
<td class="text-align:center"><p>24</p></td> <td class="text-center"><p>24</p></td>
<td class="text-align:center"><p>[0.300, 0.700]</p></td> <td class="text-center"><p>[0.300, 0.700]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-odd"><td><p>haberman</p></td> <tr class="row-odd"><td><p>haberman</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>306</p></td> <td class="text-center"><p>306</p></td>
<td class="text-align:center"><p>3</p></td> <td class="text-center"><p>3</p></td>
<td class="text-align:center"><p>[0.735, 0.265]</p></td> <td class="text-center"><p>[0.735, 0.265]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-even"><td><p>ionosphere</p></td> <tr class="row-even"><td><p>ionosphere</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>351</p></td> <td class="text-center"><p>351</p></td>
<td class="text-align:center"><p>34</p></td> <td class="text-center"><p>34</p></td>
<td class="text-align:center"><p>[0.641, 0.359]</p></td> <td class="text-center"><p>[0.641, 0.359]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-odd"><td><p>iris.1</p></td> <tr class="row-odd"><td><p>iris.1</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>150</p></td> <td class="text-center"><p>150</p></td>
<td class="text-align:center"><p>4</p></td> <td class="text-center"><p>4</p></td>
<td class="text-align:center"><p>[0.667, 0.333]</p></td> <td class="text-center"><p>[0.667, 0.333]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-even"><td><p>iris.2</p></td> <tr class="row-even"><td><p>iris.2</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>150</p></td> <td class="text-center"><p>150</p></td>
<td class="text-align:center"><p>4</p></td> <td class="text-center"><p>4</p></td>
<td class="text-align:center"><p>[0.667, 0.333]</p></td> <td class="text-center"><p>[0.667, 0.333]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-odd"><td><p>iris.3</p></td> <tr class="row-odd"><td><p>iris.3</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>150</p></td> <td class="text-center"><p>150</p></td>
<td class="text-align:center"><p>4</p></td> <td class="text-center"><p>4</p></td>
<td class="text-align:center"><p>[0.667, 0.333]</p></td> <td class="text-center"><p>[0.667, 0.333]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-even"><td><p>mammographic</p></td> <tr class="row-even"><td><p>mammographic</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>830</p></td> <td class="text-center"><p>830</p></td>
<td class="text-align:center"><p>5</p></td> <td class="text-center"><p>5</p></td>
<td class="text-align:center"><p>[0.514, 0.486]</p></td> <td class="text-center"><p>[0.514, 0.486]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-odd"><td><p>pageblocks.5</p></td> <tr class="row-odd"><td><p>pageblocks.5</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>5473</p></td> <td class="text-center"><p>5473</p></td>
<td class="text-align:center"><p>10</p></td> <td class="text-center"><p>10</p></td>
<td class="text-align:center"><p>[0.979, 0.021]</p></td> <td class="text-center"><p>[0.979, 0.021]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-even"><td><p>semeion</p></td> <tr class="row-even"><td><p>semeion</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>1593</p></td> <td class="text-center"><p>1593</p></td>
<td class="text-align:center"><p>256</p></td> <td class="text-center"><p>256</p></td>
<td class="text-align:center"><p>[0.901, 0.099]</p></td> <td class="text-center"><p>[0.901, 0.099]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-odd"><td><p>sonar</p></td> <tr class="row-odd"><td><p>sonar</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>208</p></td> <td class="text-center"><p>208</p></td>
<td class="text-align:center"><p>60</p></td> <td class="text-center"><p>60</p></td>
<td class="text-align:center"><p>[0.534, 0.466]</p></td> <td class="text-center"><p>[0.534, 0.466]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-even"><td><p>spambase</p></td> <tr class="row-even"><td><p>spambase</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>4601</p></td> <td class="text-center"><p>4601</p></td>
<td class="text-align:center"><p>57</p></td> <td class="text-center"><p>57</p></td>
<td class="text-align:center"><p>[0.606, 0.394]</p></td> <td class="text-center"><p>[0.606, 0.394]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-odd"><td><p>spectf</p></td> <tr class="row-odd"><td><p>spectf</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>267</p></td> <td class="text-center"><p>267</p></td>
<td class="text-align:center"><p>44</p></td> <td class="text-center"><p>44</p></td>
<td class="text-align:center"><p>[0.794, 0.206]</p></td> <td class="text-center"><p>[0.794, 0.206]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-even"><td><p>tictactoe</p></td> <tr class="row-even"><td><p>tictactoe</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>958</p></td> <td class="text-center"><p>958</p></td>
<td class="text-align:center"><p>9</p></td> <td class="text-center"><p>9</p></td>
<td class="text-align:center"><p>[0.653, 0.347]</p></td> <td class="text-center"><p>[0.653, 0.347]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-odd"><td><p>transfusion</p></td> <tr class="row-odd"><td><p>transfusion</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>748</p></td> <td class="text-center"><p>748</p></td>
<td class="text-align:center"><p>4</p></td> <td class="text-center"><p>4</p></td>
<td class="text-align:center"><p>[0.762, 0.238]</p></td> <td class="text-center"><p>[0.762, 0.238]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-even"><td><p>wdbc</p></td> <tr class="row-even"><td><p>wdbc</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>569</p></td> <td class="text-center"><p>569</p></td>
<td class="text-align:center"><p>30</p></td> <td class="text-center"><p>30</p></td>
<td class="text-align:center"><p>[0.627, 0.373]</p></td> <td class="text-center"><p>[0.627, 0.373]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-odd"><td><p>wine.1</p></td> <tr class="row-odd"><td><p>wine.1</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>178</p></td> <td class="text-center"><p>178</p></td>
<td class="text-align:center"><p>13</p></td> <td class="text-center"><p>13</p></td>
<td class="text-align:center"><p>[0.669, 0.331]</p></td> <td class="text-center"><p>[0.669, 0.331]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-even"><td><p>wine.2</p></td> <tr class="row-even"><td><p>wine.2</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>178</p></td> <td class="text-center"><p>178</p></td>
<td class="text-align:center"><p>13</p></td> <td class="text-center"><p>13</p></td>
<td class="text-align:center"><p>[0.601, 0.399]</p></td> <td class="text-center"><p>[0.601, 0.399]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-odd"><td><p>wine.3</p></td> <tr class="row-odd"><td><p>wine.3</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>178</p></td> <td class="text-center"><p>178</p></td>
<td class="text-align:center"><p>13</p></td> <td class="text-center"><p>13</p></td>
<td class="text-align:center"><p>[0.730, 0.270]</p></td> <td class="text-center"><p>[0.730, 0.270]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-even"><td><p>wine-q-red</p></td> <tr class="row-even"><td><p>wine-q-red</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>1599</p></td> <td class="text-center"><p>1599</p></td>
<td class="text-align:center"><p>11</p></td> <td class="text-center"><p>11</p></td>
<td class="text-align:center"><p>[0.465, 0.535]</p></td> <td class="text-center"><p>[0.465, 0.535]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-odd"><td><p>wine-q-white</p></td> <tr class="row-odd"><td><p>wine-q-white</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>4898</p></td> <td class="text-center"><p>4898</p></td>
<td class="text-align:center"><p>11</p></td> <td class="text-center"><p>11</p></td>
<td class="text-align:center"><p>[0.335, 0.665]</p></td> <td class="text-center"><p>[0.335, 0.665]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
<tr class="row-even"><td><p>yeast</p></td> <tr class="row-even"><td><p>yeast</p></td>
<td class="text-align:center"><p>2</p></td> <td class="text-center"><p>2</p></td>
<td class="text-align:center"><p>1484</p></td> <td class="text-center"><p>1484</p></td>
<td class="text-align:center"><p>8</p></td> <td class="text-center"><p>8</p></td>
<td class="text-align:center"><p>[0.711, 0.289]</p></td> <td class="text-center"><p>[0.711, 0.289]</p></td>
<td><p>dense</p></td> <td><p>dense</p></td>
</tr> </tr>
</tbody> </tbody>
</table> </table>
<div class="section" id="issues"> <section id="issues">
<h3>Issues:<a class="headerlink" href="#issues" title="Permalink to this headline"></a></h3> <h3>Issues:<a class="headerlink" href="#issues" title="Permalink to this heading"></a></h3>
<p>All datasets will be downloaded automatically the first time they are requested, and <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. 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 However, some datasets require special actions that at the moment are not fully
@ -631,10 +634,10 @@ standard Pythons packages like gzip or zip. This file would need to be uncompres
OS-dependent software manually. Information on how to do it will be printed the first OS-dependent software manually. Information on how to do it will be printed the first
time the dataset is invoked.</p></li> time the dataset is invoked.</p></li>
</ul> </ul>
</div> </section>
</div> </section>
<div class="section" id="adding-custom-datasets"> <section id="adding-custom-datasets">
<h2>Adding Custom Datasets<a class="headerlink" href="#adding-custom-datasets" title="Permalink to this headline"></a></h2> <h2>Adding Custom Datasets<a class="headerlink" href="#adding-custom-datasets" title="Permalink to this heading"></a></h2>
<p>QuaPy provides data loaders for simple formats dealing with <p>QuaPy provides data loaders for simple formats dealing with
text, following the format:</p> 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">&#39;s pre-processed text </span><span class="se">\n</span> <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">&#39;s pre-processed text </span><span class="se">\n</span>
@ -673,8 +676,8 @@ e.g.:</p>
<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> <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> </pre></div>
</div> </div>
<div class="section" id="data-processing"> <section id="data-processing">
<h3>Data Processing<a class="headerlink" href="#data-processing" title="Permalink to this headline"></a></h3> <h3>Data Processing<a class="headerlink" href="#data-processing" title="Permalink to this heading"></a></h3>
<p>QuaPy implements a number of preprocessing functions in the package <em>qp.data.preprocessing</em>, including:</p> <p>QuaPy implements a number of preprocessing functions in the package <em>qp.data.preprocessing</em>, including:</p>
<ul class="simple"> <ul class="simple">
<li><p><em>text2tfidf</em>: tfidf vectorization</p></li> <li><p><em>text2tfidf</em>: tfidf vectorization</p></li>
@ -683,9 +686,9 @@ e.g.:</p>
that the column values have zero mean and unit variance).</p></li> 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> <li><p><em>index</em>: transforms textual tokens into lists of numeric ids)</p></li>
</ul> </ul>
</div> </section>
</div> </section>
</div> </section>
<div class="clearer"></div> <div class="clearer"></div>
@ -694,6 +697,7 @@ that the column values have zero mean and unit variance).</p></li>
</div> </div>
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<h3><a href="index.html">Table of Contents</a></h3> <h3><a href="index.html">Table of Contents</a></h3>
<ul> <ul>
<li><a class="reference internal" href="#">Datasets</a><ul> <li><a class="reference internal" href="#">Datasets</a><ul>
@ -711,12 +715,17 @@ that the column values have zero mean and unit variance).</p></li>
</li> </li>
</ul> </ul>
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<h4>Previous topic</h4> <h4>Previous topic</h4>
<p class="topless"><a href="readme.html" <p class="topless"><a href="Installation.html"
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<p class="topless"><a href="modules.html" <p class="topless"><a href="Evaluation.html"
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<h3>This Page</h3> <h3>This Page</h3>
<ul class="this-page-menu"> <ul class="this-page-menu">
@ -733,7 +742,7 @@ that the column values have zero mean and unit variance).</p></li>
</form> </form>
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@ -748,18 +757,18 @@ that the column values have zero mean and unit variance).</p></li>
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>modules</a> |</li> >modules</a> |</li>
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&#169; Copyright 2021, Alejandro Moreo. &#169; Copyright 2021, Alejandro Moreo.
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41
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@ -2,18 +2,21 @@
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<title>Evaluation &#8212; QuaPy 0.1.6 documentation</title>
<title>Evaluation &#8212; QuaPy 0.1.7 documentation</title>
<link rel="stylesheet" type="text/css" href="_static/pygments.css" /> <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
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@ -39,7 +42,7 @@
<li class="right" > <li class="right" >
<a href="Datasets.html" title="Datasets" <a href="Datasets.html" title="Datasets"
accesskey="P">previous</a> |</li> accesskey="P">previous</a> |</li>
<li class="nav-item nav-item-0"><a href="index.html">QuaPy 0.1.6 documentation</a> &#187;</li> <li class="nav-item nav-item-0"><a href="index.html">QuaPy 0.1.7 documentation</a> &#187;</li>
<li class="nav-item nav-item-this"><a href="">Evaluation</a></li> <li class="nav-item nav-item-this"><a href="">Evaluation</a></li>
</ul> </ul>
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@ -49,8 +52,8 @@
<div class="bodywrapper"> <div class="bodywrapper">
<div class="body" role="main"> <div class="body" role="main">
<div class="tex2jax_ignore mathjax_ignore section" id="evaluation"> <section id="evaluation">
<h1>Evaluation<a class="headerlink" href="#evaluation" title="Permalink to this headline"></a></h1> <h1>Evaluation<a class="headerlink" href="#evaluation" title="Permalink to this heading"></a></h1>
<p>Quantification is an appealing tool in scenarios of dataset shift, <p>Quantification is an appealing tool in scenarios of dataset shift,
and particularly in scenarios of prior-probability shift. and particularly in scenarios of prior-probability shift.
That is, the interest in estimating the class prevalences arises That is, the interest in estimating the class prevalences arises
@ -62,8 +65,8 @@ to be unlikely (as is the case in general scenarios of
machine learning governed by the iid assumption). machine learning governed by the iid assumption).
In brief, quantification requires dedicated evaluation protocols, In brief, quantification requires dedicated evaluation protocols,
which are implemented in QuaPy and explained here.</p> which are implemented in QuaPy and explained here.</p>
<div class="section" id="error-measures"> <section id="error-measures">
<h2>Error Measures<a class="headerlink" href="#error-measures" title="Permalink to this headline"></a></h2> <h2>Error Measures<a class="headerlink" href="#error-measures" title="Permalink to this heading"></a></h2>
<p>The module quapy.error implements the following error measures for quantification:</p> <p>The module quapy.error implements the following error measures for quantification:</p>
<ul class="simple"> <ul class="simple">
<li><p><em>mae</em>: mean absolute error</p></li> <li><p><em>mae</em>: mean absolute error</p></li>
@ -116,9 +119,9 @@ error functions from strings using, e.g.:</p>
<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> <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> </pre></div>
</div> </div>
</div> </section>
<div class="section" id="evaluation-protocols"> <section id="evaluation-protocols">
<h2>Evaluation Protocols<a class="headerlink" href="#evaluation-protocols" title="Permalink to this headline"></a></h2> <h2>Evaluation Protocols<a class="headerlink" href="#evaluation-protocols" title="Permalink to this heading"></a></h2>
<p>QuaPy implements the so-called “artificial sampling protocol”, <p>QuaPy implements the so-called “artificial sampling protocol”,
according to which a test set is used to generate samplings at according to which a test set is used to generate samplings at
desired prevalences of fixed size and covering the full spectrum desired prevalences of fixed size and covering the full spectrum
@ -254,8 +257,8 @@ given evaluation metric, returning the average instead of a dataframe.</p></li>
true prevalences and the estimated prevalences.</p></li> true prevalences and the estimated prevalences.</p></li>
</ul> </ul>
<p>See the documentation for further details.</p> <p>See the documentation for further details.</p>
</div> </section>
</div> </section>
<div class="clearer"></div> <div class="clearer"></div>
@ -264,6 +267,7 @@ true prevalences and the estimated prevalences.</p></li>
</div> </div>
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<h3><a href="index.html">Table of Contents</a></h3> <h3><a href="index.html">Table of Contents</a></h3>
<ul> <ul>
<li><a class="reference internal" href="#">Evaluation</a><ul> <li><a class="reference internal" href="#">Evaluation</a><ul>
@ -273,12 +277,17 @@ true prevalences and the estimated prevalences.</p></li>
</li> </li>
</ul> </ul>
</div>
<div>
<h4>Previous topic</h4> <h4>Previous topic</h4>
<p class="topless"><a href="Datasets.html" <p class="topless"><a href="Datasets.html"
title="previous chapter">Datasets</a></p> title="previous chapter">Datasets</a></p>
</div>
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<h4>Next topic</h4> <h4>Next topic</h4>
<p class="topless"><a href="Methods.html" <p class="topless"><a href="Methods.html"
title="next chapter">Quantification Methods</a></p> title="next chapter">Quantification Methods</a></p>
</div>
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<h3>This Page</h3> <h3>This Page</h3>
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@ -295,7 +304,7 @@ true prevalences and the estimated prevalences.</p></li>
</form> </form>
</div> </div>
</div> </div>
<script>$('#searchbox').show(0);</script> <script>document.getElementById('searchbox').style.display = "block"</script>
</div> </div>
</div> </div>
<div class="clearer"></div> <div class="clearer"></div>
@ -315,13 +324,13 @@ true prevalences and the estimated prevalences.</p></li>
<li class="right" > <li class="right" >
<a href="Datasets.html" title="Datasets" <a href="Datasets.html" title="Datasets"
>previous</a> |</li> >previous</a> |</li>
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<li class="nav-item nav-item-this"><a href="">Evaluation</a></li> <li class="nav-item nav-item-this"><a href="">Evaluation</a></li>
</ul> </ul>
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&#169; Copyright 2021, Alejandro Moreo. &#169; Copyright 2021, Alejandro Moreo.
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41
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@ -2,18 +2,21 @@
<!doctype html> <!doctype html>
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<meta charset="utf-8" /> <meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" /> <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.19: https://docutils.sourceforge.io/" />
<title>Installation &#8212; QuaPy 0.1.6 documentation</title>
<title>Installation &#8212; QuaPy 0.1.7 documentation</title>
<link rel="stylesheet" type="text/css" href="_static/pygments.css" /> <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
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@ -39,7 +42,7 @@
<li class="right" > <li class="right" >
<a href="index.html" title="Welcome to QuaPy’s documentation!" <a href="index.html" title="Welcome to QuaPy’s documentation!"
accesskey="P">previous</a> |</li> accesskey="P">previous</a> |</li>
<li class="nav-item nav-item-0"><a href="index.html">QuaPy 0.1.6 documentation</a> &#187;</li> <li class="nav-item nav-item-0"><a href="index.html">QuaPy 0.1.7 documentation</a> &#187;</li>
<li class="nav-item nav-item-this"><a href="">Installation</a></li> <li class="nav-item nav-item-this"><a href="">Installation</a></li>
</ul> </ul>
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@ -49,15 +52,15 @@
<div class="bodywrapper"> <div class="bodywrapper">
<div class="body" role="main"> <div class="body" role="main">
<div class="section" id="installation"> <section id="installation">
<h1>Installation<a class="headerlink" href="#installation" title="Permalink to this headline"></a></h1> <h1>Installation<a class="headerlink" href="#installation" title="Permalink to this heading"></a></h1>
<p>QuaPy can be easily installed via <cite>pip</cite></p> <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> <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> </pre></div>
</div> </div>
<p>See <a class="reference external" href="https://pypi.org/project/QuaPy/">pip page</a> for older versions.</p> <p>See <a class="reference external" href="https://pypi.org/project/QuaPy/">pip page</a> for older versions.</p>
<div class="section" id="requirements"> <section id="requirements">
<h2>Requirements<a class="headerlink" href="#requirements" title="Permalink to this headline"></a></h2> <h2>Requirements<a class="headerlink" href="#requirements" title="Permalink to this heading"></a></h2>
<ul class="simple"> <ul class="simple">
<li><p>scikit-learn, numpy, scipy</p></li> <li><p>scikit-learn, numpy, scipy</p></li>
<li><p>pytorch (for QuaNet)</p></li> <li><p>pytorch (for QuaNet)</p></li>
@ -67,9 +70,9 @@
<li><p>pandas, xlrd</p></li> <li><p>pandas, xlrd</p></li>
<li><p>matplotlib</p></li> <li><p>matplotlib</p></li>
</ul> </ul>
</div> </section>
<div class="section" id="svm-perf-with-quantification-oriented-losses"> <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> <h2>SVM-perf with quantification-oriented losses<a class="headerlink" href="#svm-perf-with-quantification-oriented-losses" title="Permalink to this heading"></a></h2>
<p>In order to run experiments involving SVM(Q), SVM(KLD), SVM(NKLD), <p>In order to run experiments involving SVM(Q), SVM(KLD), SVM(NKLD),
SVM(AE), or SVM(RAE), you have to first download the 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> <a class="reference external" href="http://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html">svmperf</a>
@ -96,8 +99,8 @@ and for the <cite>KLD</cite> and <cite>NKLD</cite> as proposed by
for quantification. for quantification.
This patch extends the former by also allowing SVMperf to optimize for This patch extends the former by also allowing SVMperf to optimize for
<cite>AE</cite> and <cite>RAE</cite>.</p> <cite>AE</cite> and <cite>RAE</cite>.</p>
</div> </section>
</div> </section>
<div class="clearer"></div> <div class="clearer"></div>
@ -106,6 +109,7 @@ This patch extends the former by also allowing SVMperf to optimize for
</div> </div>
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<h3><a href="index.html">Table of Contents</a></h3> <h3><a href="index.html">Table of Contents</a></h3>
<ul> <ul>
<li><a class="reference internal" href="#">Installation</a><ul> <li><a class="reference internal" href="#">Installation</a><ul>
@ -115,12 +119,17 @@ This patch extends the former by also allowing SVMperf to optimize for
</li> </li>
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<h4>Previous topic</h4> <h4>Previous topic</h4>
<p class="topless"><a href="index.html" <p class="topless"><a href="index.html"
title="previous chapter">Welcome to QuaPy’s documentation!</a></p> title="previous chapter">Welcome to QuaPy’s documentation!</a></p>
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<h4>Next topic</h4> <h4>Next topic</h4>
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<h3>This Page</h3> <h3>This Page</h3>
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@ -137,7 +146,7 @@ This patch extends the former by also allowing SVMperf to optimize for
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@ -157,13 +166,13 @@ This patch extends the former by also allowing SVMperf to optimize for
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docs/build/html/Methods.html vendored
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@ -2,22 +2,25 @@
<!doctype html> <!doctype html>
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<meta charset="utf-8" /> <meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" /> <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.19: https://docutils.sourceforge.io/" />
<title>Quantification Methods &#8212; QuaPy 0.1.6 documentation</title>
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<link rel="next" title="Plotting" href="Plotting.html" /> <link rel="next" title="Model Selection" href="Model-Selection.html" />
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@ -34,12 +37,12 @@
<a href="py-modindex.html" title="Python Module Index" <a href="py-modindex.html" title="Python Module Index"
>modules</a> |</li> >modules</a> |</li>
<li class="right" > <li class="right" >
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accesskey="P">previous</a> |</li> accesskey="P">previous</a> |</li>
<li class="nav-item nav-item-0"><a href="index.html">QuaPy 0.1.6 documentation</a> &#187;</li> <li class="nav-item nav-item-0"><a href="index.html">QuaPy 0.1.7 documentation</a> &#187;</li>
<li class="nav-item nav-item-this"><a href="">Quantification Methods</a></li> <li class="nav-item nav-item-this"><a href="">Quantification Methods</a></li>
</ul> </ul>
</div> </div>
@ -49,8 +52,8 @@
<div class="bodywrapper"> <div class="bodywrapper">
<div class="body" role="main"> <div class="body" role="main">
<div class="tex2jax_ignore mathjax_ignore section" id="quantification-methods"> <section id="quantification-methods">
<h1>Quantification Methods<a class="headerlink" href="#quantification-methods" title="Permalink to this headline"></a></h1> <h1>Quantification Methods<a class="headerlink" href="#quantification-methods" title="Permalink to this heading"></a></h1>
<p>Quantification methods can be categorized as belonging to <p>Quantification methods can be categorized as belonging to
<em>aggregative</em> and <em>non-aggregative</em> groups. <em>aggregative</em> and <em>non-aggregative</em> groups.
Most methods included in QuaPy at the moment are of type <em>aggregative</em> Most methods included in QuaPy at the moment are of type <em>aggregative</em>
@ -86,8 +89,8 @@ is one single array of class prevalences), while functions <em>set_params</em>
and <em>get_params</em> allow a and <em>get_params</em> allow a
<a class="reference external" href="https://github.com/HLT-ISTI/QuaPy/wiki/Model-Selection">model selector</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> to automate the process of hyperparameter search.</p>
<div class="section" id="aggregative-methods"> <section id="aggregative-methods">
<h2>Aggregative Methods<a class="headerlink" href="#aggregative-methods" title="Permalink to this headline"></a></h2> <h2>Aggregative Methods<a class="headerlink" href="#aggregative-methods" title="Permalink to this heading"></a></h2>
<p>All quantification methods are implemented as part of the <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</em> package. In particular, <em>aggregative</em> methods are defined in
<em>qp.method.aggregative</em>, and extend <em>AggregativeQuantifier(BaseQuantifier)</em>. <em>qp.method.aggregative</em>, and extend <em>AggregativeQuantifier(BaseQuantifier)</em>.
@ -133,8 +136,8 @@ reuse these predictions, without requiring to classify each element every time.
QuaPy leverages this property to speed-up any procedure having to do with QuaPy leverages this property to speed-up any procedure having to do with
quantification over samples, as is customarily done in model selection or quantification over samples, as is customarily done in model selection or
in evaluation.</p> in evaluation.</p>
<div class="section" id="the-classify-count-variants"> <section id="the-classify-count-variants">
<h3>The Classify &amp; Count variants<a class="headerlink" href="#the-classify-count-variants" title="Permalink to this headline"></a></h3> <h3>The Classify &amp; Count variants<a class="headerlink" href="#the-classify-count-variants" title="Permalink to this heading"></a></h3>
<p>QuaPy implements the four CC variants, i.e.:</p> <p>QuaPy implements the four CC variants, i.e.:</p>
<ul class="simple"> <ul class="simple">
<li><p><em>CC</em> (Classify &amp; Count), the simplest aggregative quantifier; one that <li><p><em>CC</em> (Classify &amp; Count), the simplest aggregative quantifier; one that
@ -214,9 +217,9 @@ be applied to hard classifiers when <em>fit_learner=True</em>; an exception
will be raised otherwise.</p> will be raised otherwise.</p>
<p>Lastly, everything we said aboud ACC and PCC <p>Lastly, everything we said aboud ACC and PCC
applies to PACC as well.</p> applies to PACC as well.</p>
</div> </section>
<div class="section" id="expectation-maximization-emq"> <section id="expectation-maximization-emq">
<h3>Expectation Maximization (EMQ)<a class="headerlink" href="#expectation-maximization-emq" title="Permalink to this headline"></a></h3> <h3>Expectation Maximization (EMQ)<a class="headerlink" href="#expectation-maximization-emq" title="Permalink to this heading"></a></h3>
<p>The Expectation Maximization Quantifier (EMQ), also known as <p>The Expectation Maximization Quantifier (EMQ), also known as
the SLD, is available at <em>qp.method.aggregative.EMQ</em> or via the the SLD, is available at <em>qp.method.aggregative.EMQ</em> or via the
alias <em>qp.method.aggregative.ExpectationMaximizationQuantifier</em>. alias <em>qp.method.aggregative.ExpectationMaximizationQuantifier</em>.
@ -241,9 +244,9 @@ experiments we have carried out.</p>
<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> <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> </pre></div>
</div> </div>
</div> </section>
<div class="section" id="hellinger-distance-y-hdy"> <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> <h3>Hellinger Distance y (HDy)<a class="headerlink" href="#hellinger-distance-y-hdy" title="Permalink to this heading"></a></h3>
<p>The method HDy is described in:</p> <p>The method HDy is described in:</p>
<p><em>Implementation of the method based on the Hellinger Distance y (HDy) proposed by <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 González-Castro, V., Alaiz-Rodrı́guez, R., and Alegre, E. (2013). Class distribution
@ -274,9 +277,9 @@ provided in QuaPy accepts only binary datasets.</p>
<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> <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> </pre></div>
</div> </div>
</div> </section>
<div class="section" id="explicit-loss-minimization"> <section id="explicit-loss-minimization">
<h3>Explicit Loss Minimization<a class="headerlink" href="#explicit-loss-minimization" title="Permalink to this headline"></a></h3> <h3>Explicit Loss Minimization<a class="headerlink" href="#explicit-loss-minimization" title="Permalink to this heading"></a></h3>
<p>The Explicit Loss Minimization (ELM) represent a family of methods <p>The Explicit Loss Minimization (ELM) represent a family of methods
based on structured output learning, i.e., quantifiers relying on based on structured output learning, i.e., quantifiers relying on
classifiers that have been optimized targeting a classifiers that have been optimized targeting a
@ -344,17 +347,17 @@ In QuaPy this is possible by using the <em>OneVsAll</em> class:</p>
<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> <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> </pre></div>
</div> </div>
</div> </section>
</div> </section>
<div class="section" id="meta-models"> <section id="meta-models">
<h2>Meta Models<a class="headerlink" href="#meta-models" title="Permalink to this headline"></a></h2> <h2>Meta Models<a class="headerlink" href="#meta-models" title="Permalink to this heading"></a></h2>
<p>By <em>meta</em> models we mean quantification methods that are defined on top of other <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 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 the non-aggregative group (indeed, <em>meta</em> models could use quantifiers from any of those
groups). groups).
<em>Meta</em> models are implemented in the <em>qp.method.meta</em> module.</p> <em>Meta</em> models are implemented in the <em>qp.method.meta</em> module.</p>
<div class="section" id="ensembles"> <section id="ensembles">
<h3>Ensembles<a class="headerlink" href="#ensembles" title="Permalink to this headline"></a></h3> <h3>Ensembles<a class="headerlink" href="#ensembles" title="Permalink to this heading"></a></h3>
<p>QuaPy implements (some of) the variants proposed in:</p> <p>QuaPy implements (some of) the variants proposed in:</p>
<ul class="simple"> <ul class="simple">
<li><p><em>Pérez-Gállego, P., Quevedo, J. R., &amp; del Coz, J. J. (2017). <li><p><em>Pérez-Gállego, P., Quevedo, J. R., &amp; del Coz, J. J. (2017).
@ -391,9 +394,9 @@ the performance estimated for each member of the ensemble in terms of that evalu
informs of the number of members to retain.</p> 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> <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> wiki if you want to optimize the hyperparameters of ensemble for classification or quantification.</p>
</div> </section>
<div class="section" id="the-quanet-neural-network"> <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> <h3>The QuaNet neural network<a class="headerlink" href="#the-quanet-neural-network" title="Permalink to this heading"></a></h3>
<p>QuaPy offers an implementation of QuaNet, a deep learning model presented in:</p> <p>QuaPy offers an implementation of QuaNet, a deep learning model presented in:</p>
<p><em>Esuli, A., Moreo, A., &amp; Sebastiani, F. (2018, October). <p><em>Esuli, A., Moreo, A., &amp; Sebastiani, F. (2018, October).
A recurrent neural network for sentiment quantification. A recurrent neural network for sentiment quantification.
@ -425,9 +428,9 @@ In the following example, we show an instantiation of QuaNet that instead uses C
<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> <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> </pre></div>
</div> </div>
</div> </section>
</div> </section>
</div> </section>
<div class="clearer"></div> <div class="clearer"></div>
@ -436,6 +439,7 @@ In the following example, we show an instantiation of QuaNet that instead uses C
</div> </div>
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<h3><a href="index.html">Table of Contents</a></h3> <h3><a href="index.html">Table of Contents</a></h3>
<ul> <ul>
<li><a class="reference internal" href="#">Quantification Methods</a><ul> <li><a class="reference internal" href="#">Quantification Methods</a><ul>
@ -455,12 +459,17 @@ In the following example, we show an instantiation of QuaNet that instead uses C
</li> </li>
</ul> </ul>
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<div>
<h4>Previous topic</h4> <h4>Previous topic</h4>
<p class="topless"><a href="Evaluation.html" <p class="topless"><a href="Evaluation.html"
title="previous chapter">Evaluation</a></p> title="previous chapter">Evaluation</a></p>
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<h4>Next topic</h4> <h4>Next topic</h4>
<p class="topless"><a href="Plotting.html" <p class="topless"><a href="Model-Selection.html"
title="next chapter">Plotting</a></p> title="next chapter">Model Selection</a></p>
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<h3>This Page</h3> <h3>This Page</h3>
<ul class="this-page-menu"> <ul class="this-page-menu">
@ -477,7 +486,7 @@ In the following example, we show an instantiation of QuaNet that instead uses C
</form> </form>
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<script>$('#searchbox').show(0);</script> <script>document.getElementById('searchbox').style.display = "block"</script>
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@ -492,18 +501,18 @@ In the following example, we show an instantiation of QuaNet that instead uses C
<a href="py-modindex.html" title="Python Module Index" <a href="py-modindex.html" title="Python Module Index"
>modules</a> |</li> >modules</a> |</li>
<li class="right" > <li class="right" >
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<li class="right" > <li class="right" >
<a href="Evaluation.html" title="Evaluation" <a href="Evaluation.html" title="Evaluation"
>previous</a> |</li> >previous</a> |</li>
<li class="nav-item nav-item-0"><a href="index.html">QuaPy 0.1.6 documentation</a> &#187;</li> <li class="nav-item nav-item-0"><a href="index.html">QuaPy 0.1.7 documentation</a> &#187;</li>
<li class="nav-item nav-item-this"><a href="">Quantification Methods</a></li> <li class="nav-item nav-item-this"><a href="">Quantification Methods</a></li>
</ul> </ul>
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&#169; Copyright 2021, Alejandro Moreo. &#169; Copyright 2021, Alejandro Moreo.
Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.2.0. Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 5.3.0.
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61
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@ -2,21 +2,26 @@
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<meta charset="utf-8" /> <meta charset="utf-8" />
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@ -31,7 +36,13 @@
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>modules</a> |</li> >modules</a> |</li>
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accesskey="P">previous</a> |</li>
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<li class="nav-item nav-item-this"><a href="">Model Selection</a></li> <li class="nav-item nav-item-this"><a href="">Model Selection</a></li>
</ul> </ul>
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@ -41,8 +52,8 @@
<div class="bodywrapper"> <div class="bodywrapper">
<div class="body" role="main"> <div class="body" role="main">
<div class="tex2jax_ignore mathjax_ignore section" id="model-selection"> <section id="model-selection">
<h1>Model Selection<a class="headerlink" href="#model-selection" title="Permalink to this headline"></a></h1> <h1>Model Selection<a class="headerlink" href="#model-selection" title="Permalink to this heading"></a></h1>
<p>As a supervised machine learning task, quantification methods <p>As a supervised machine learning task, quantification methods
can strongly depend on a good choice of model hyper-parameters. can strongly depend on a good choice of model hyper-parameters.
The process whereby those hyper-parameters are chosen is The process whereby those hyper-parameters are chosen is
@ -50,8 +61,8 @@ typically known as <em>Model Selection</em>, and typically consists of
testing different settings and picking the one that performed testing different settings and picking the one that performed
best in a held-out validation set in terms of any given best in a held-out validation set in terms of any given
evaluation measure.</p> evaluation measure.</p>
<div class="section" id="targeting-a-quantification-oriented-loss"> <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> <h2>Targeting a Quantification-oriented loss<a class="headerlink" href="#targeting-a-quantification-oriented-loss" title="Permalink to this heading"></a></h2>
<p>The task being optimized determines the evaluation protocol, <p>The task being optimized determines the evaluation protocol,
i.e., the criteria according to which the performance of i.e., the criteria according to which the performance of
any given method for solving is to be assessed. any given method for solving is to be assessed.
@ -145,9 +156,9 @@ 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 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 on the specific data split to be used across different model selection
experiments.</p> experiments.</p>
</div> </section>
<div class="section" id="targeting-a-classification-oriented-loss"> <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> <h2>Targeting a Classification-oriented loss<a class="headerlink" href="#targeting-a-classification-oriented-loss" title="Permalink to this heading"></a></h2>
<p>Optimizing a model for quantification could rather be <p>Optimizing a model for quantification could rather be
computationally costly. computationally costly.
In aggregative methods, one could alternatively try to optimize In aggregative methods, one could alternatively try to optimize
@ -185,8 +196,8 @@ find examples
in which optimizing for classification ends up resulting in a better in which optimizing for classification ends up resulting in a better
quantifier than when optimizing for quantification. quantifier than when optimizing for quantification.
Nonetheless, this is theoretically unlikely to happen.</p> Nonetheless, this is theoretically unlikely to happen.</p>
</div> </section>
</div> </section>
<div class="clearer"></div> <div class="clearer"></div>
@ -195,6 +206,7 @@ Nonetheless, this is theoretically unlikely to happen.</p>
</div> </div>
<div class="sphinxsidebar" role="navigation" aria-label="main navigation"> <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
<div class="sphinxsidebarwrapper"> <div class="sphinxsidebarwrapper">
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<h3><a href="index.html">Table of Contents</a></h3> <h3><a href="index.html">Table of Contents</a></h3>
<ul> <ul>
<li><a class="reference internal" href="#">Model Selection</a><ul> <li><a class="reference internal" href="#">Model Selection</a><ul>
@ -204,6 +216,17 @@ Nonetheless, this is theoretically unlikely to happen.</p>
</li> </li>
</ul> </ul>
</div>
<div>
<h4>Previous topic</h4>
<p class="topless"><a href="Methods.html"
title="previous chapter">Quantification Methods</a></p>
</div>
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title="next chapter">Plotting</a></p>
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<h3>This Page</h3> <h3>This Page</h3>
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@ -220,7 +243,7 @@ Nonetheless, this is theoretically unlikely to happen.</p>
</form> </form>
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<script>$('#searchbox').show(0);</script> <script>document.getElementById('searchbox').style.display = "block"</script>
</div> </div>
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@ -234,13 +257,19 @@ Nonetheless, this is theoretically unlikely to happen.</p>
<li class="right" > <li class="right" >
<a href="py-modindex.html" title="Python Module Index" <a href="py-modindex.html" title="Python Module Index"
>modules</a> |</li> >modules</a> |</li>
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>previous</a> |</li>
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<li class="nav-item nav-item-this"><a href="">Model Selection</a></li> <li class="nav-item nav-item-this"><a href="">Model Selection</a></li>
</ul> </ul>
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&#169; Copyright 2021, Alejandro Moreo. &#169; Copyright 2021, Alejandro Moreo.
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59
docs/build/html/Plotting.html vendored
View File

@ -2,23 +2,26 @@
<!doctype html> <!doctype html>
<html> <html lang="en">
<head> <head>
<meta charset="utf-8" /> <meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" /> <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.19: https://docutils.sourceforge.io/" />
<title>Plotting &#8212; QuaPy 0.1.6 documentation</title>
<title>Plotting &#8212; QuaPy 0.1.7 documentation</title>
<link rel="stylesheet" type="text/css" href="_static/pygments.css" /> <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
<link rel="stylesheet" type="text/css" href="_static/bizstyle.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 data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
<script src="_static/jquery.js"></script> <script src="_static/jquery.js"></script>
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<link rel="index" title="Index" href="genindex.html" /> <link rel="index" title="Index" href="genindex.html" />
<link rel="search" title="Search" href="search.html" /> <link rel="search" title="Search" href="search.html" />
<link rel="next" title="quapy" href="modules.html" /> <link rel="next" title="quapy" href="modules.html" />
<link rel="prev" title="Quantification Methods" href="Methods.html" /> <link rel="prev" title="Model Selection" href="Model-Selection.html" />
<meta name="viewport" content="width=device-width,initial-scale=1.0" /> <meta name="viewport" content="width=device-width,initial-scale=1.0" />
<!--[if lt IE 9]> <!--[if lt IE 9]>
<script src="_static/css3-mediaqueries.js"></script> <script src="_static/css3-mediaqueries.js"></script>
@ -37,9 +40,9 @@
<a href="modules.html" title="quapy" <a href="modules.html" title="quapy"
accesskey="N">next</a> |</li> accesskey="N">next</a> |</li>
<li class="right" > <li class="right" >
<a href="Methods.html" title="Quantification Methods" <a href="Model-Selection.html" title="Model Selection"
accesskey="P">previous</a> |</li> accesskey="P">previous</a> |</li>
<li class="nav-item nav-item-0"><a href="index.html">QuaPy 0.1.6 documentation</a> &#187;</li> <li class="nav-item nav-item-0"><a href="index.html">QuaPy 0.1.7 documentation</a> &#187;</li>
<li class="nav-item nav-item-this"><a href="">Plotting</a></li> <li class="nav-item nav-item-this"><a href="">Plotting</a></li>
</ul> </ul>
</div> </div>
@ -49,8 +52,8 @@
<div class="bodywrapper"> <div class="bodywrapper">
<div class="body" role="main"> <div class="body" role="main">
<div class="tex2jax_ignore mathjax_ignore section" id="plotting"> <section id="plotting">
<h1>Plotting<a class="headerlink" href="#plotting" title="Permalink to this headline"></a></h1> <h1>Plotting<a class="headerlink" href="#plotting" title="Permalink to this heading"></a></h1>
<p>The module <em>qp.plot</em> implements some basic plotting functions <p>The module <em>qp.plot</em> implements some basic plotting functions
that can help analyse the performance of a quantification method.</p> that can help analyse the performance of a quantification method.</p>
<p>All plotting functions receive as inputs the outcomes of <p>All plotting functions receive as inputs the outcomes of
@ -137,8 +140,8 @@ generating 100 random samples at each prevalence).</p>
</pre></div> </pre></div>
</div> </div>
<p>the plots that can be generated are explained below.</p> <p>the plots that can be generated are explained below.</p>
<div class="section" id="diagonal-plot"> <section id="diagonal-plot">
<h2>Diagonal Plot<a class="headerlink" href="#diagonal-plot" title="Permalink to this headline"></a></h2> <h2>Diagonal Plot<a class="headerlink" href="#diagonal-plot" title="Permalink to this heading"></a></h2>
<p>The <em>diagonal</em> plot shows a very insightful view of the <p>The <em>diagonal</em> plot shows a very insightful view of the
quantifier’s performance. It plots the predicted class quantifier’s performance. It plots the predicted class
prevalence (in the y-axis) against the true class prevalence prevalence (in the y-axis) against the true class prevalence
@ -164,9 +167,9 @@ the complete list of arguments in the documentation).</p>
<p>Finally, note how most quantifiers, and specially the “unadjusted” <p>Finally, note how most quantifiers, and specially the “unadjusted”
variants CC and PCC, are strongly biased towards the variants CC and PCC, are strongly biased towards the
prevalence seen during training.</p> prevalence seen during training.</p>
</div> </section>
<div class="section" id="quantification-bias"> <section id="quantification-bias">
<h2>Quantification bias<a class="headerlink" href="#quantification-bias" title="Permalink to this headline"></a></h2> <h2>Quantification bias<a class="headerlink" href="#quantification-bias" title="Permalink to this heading"></a></h2>
<p>This plot aims at evincing the bias that any quantifier <p>This plot aims at evincing the bias that any quantifier
displays with respect to the training prevalences by 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>. means of <a class="reference external" href="https://en.wikipedia.org/wiki/Box_plot">box plots</a>.
@ -209,7 +212,7 @@ like this:</p>
<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">&#39;SAMPLE_SIZE&#39;</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="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">&#39;SAMPLE_SIZE&#39;</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="p">)</span>
<span class="c1"># method names can contain Latex syntax</span> <span class="c1"># method names can contain Latex syntax</span>
<span class="n">method_name</span> <span class="o">=</span> <span class="s1">&#39;CC$_{&#39;</span> <span class="o">+</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="nb">int</span><span class="p">(</span><span class="mi">100</span> <span class="o">*</span> <span class="n">training_prevalence</span><span class="p">)</span><span class="si">}</span><span class="s1">&#39;</span> <span class="o">+</span> <span class="s1">&#39;\%}$&#39;</span> <span class="n">method_name</span> <span class="o">=</span> <span class="s1">&#39;CC$_{&#39;</span> <span class="o">+</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="nb">int</span><span class="p">(</span><span class="mi">100</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="n">training_prevalence</span><span class="p">)</span><span class="si">}</span><span class="s1">&#39;</span> <span class="o">+</span> <span class="s1">&#39;\%}$&#39;</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="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> <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>
@ -237,9 +240,9 @@ and a negative bias (or a tendency to underestimate) in cases of high prevalence
<p><img alt="diag plot on IMDb" src="_images/bin_diag_cc.png" /></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 <p>showing pretty clearly the dependency of CC on the prior probabilities
of the labeled set it was trained on.</p> of the labeled set it was trained on.</p>
</div> </section>
<div class="section" id="error-by-drift"> <section id="error-by-drift">
<h2>Error by Drift<a class="headerlink" href="#error-by-drift" title="Permalink to this headline"></a></h2> <h2>Error by Drift<a class="headerlink" href="#error-by-drift" title="Permalink to this heading"></a></h2>
<p>Above discussed plots are useful for analyzing and comparing <p>Above discussed plots are useful for analyzing and comparing
the performance of different quantification methods, but are the performance of different quantification methods, but are
limited to the binary case. The “error by drift” is a plot limited to the binary case. The “error by drift” is a plot
@ -270,8 +273,8 @@ In those cases, however, it is likely that the variances of each
method get higher, to the detriment of the visualization. method get higher, to the detriment of the visualization.
We recommend to set <em>show_std=False</em> in those cases We recommend to set <em>show_std=False</em> in those cases
in order to hide the color bands.</p> in order to hide the color bands.</p>
</div> </section>
</div> </section>
<div class="clearer"></div> <div class="clearer"></div>
@ -280,6 +283,7 @@ in order to hide the color bands.</p>
</div> </div>
<div class="sphinxsidebar" role="navigation" aria-label="main navigation"> <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
<div class="sphinxsidebarwrapper"> <div class="sphinxsidebarwrapper">
<div>
<h3><a href="index.html">Table of Contents</a></h3> <h3><a href="index.html">Table of Contents</a></h3>
<ul> <ul>
<li><a class="reference internal" href="#">Plotting</a><ul> <li><a class="reference internal" href="#">Plotting</a><ul>
@ -290,12 +294,17 @@ in order to hide the color bands.</p>
</li> </li>
</ul> </ul>
</div>
<div>
<h4>Previous topic</h4> <h4>Previous topic</h4>
<p class="topless"><a href="Methods.html" <p class="topless"><a href="Model-Selection.html"
title="previous chapter">Quantification Methods</a></p> title="previous chapter">Model Selection</a></p>
</div>
<div>
<h4>Next topic</h4> <h4>Next topic</h4>
<p class="topless"><a href="modules.html" <p class="topless"><a href="modules.html"
title="next chapter">quapy</a></p> title="next chapter">quapy</a></p>
</div>
<div role="note" aria-label="source link"> <div role="note" aria-label="source link">
<h3>This Page</h3> <h3>This Page</h3>
<ul class="this-page-menu"> <ul class="this-page-menu">
@ -312,7 +321,7 @@ in order to hide the color bands.</p>
</form> </form>
</div> </div>
</div> </div>
<script>$('#searchbox').show(0);</script> <script>document.getElementById('searchbox').style.display = "block"</script>
</div> </div>
</div> </div>
<div class="clearer"></div> <div class="clearer"></div>
@ -330,15 +339,15 @@ in order to hide the color bands.</p>
<a href="modules.html" title="quapy" <a href="modules.html" title="quapy"
>next</a> |</li> >next</a> |</li>
<li class="right" > <li class="right" >
<a href="Methods.html" title="Quantification Methods" <a href="Model-Selection.html" title="Model Selection"
>previous</a> |</li> >previous</a> |</li>
<li class="nav-item nav-item-0"><a href="index.html">QuaPy 0.1.6 documentation</a> &#187;</li> <li class="nav-item nav-item-0"><a href="index.html">QuaPy 0.1.7 documentation</a> &#187;</li>
<li class="nav-item nav-item-this"><a href="">Plotting</a></li> <li class="nav-item nav-item-this"><a href="">Plotting</a></li>
</ul> </ul>
</div> </div>
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&#169; Copyright 2021, Alejandro Moreo. &#169; Copyright 2021, Alejandro Moreo.
Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.2.0. Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 5.3.0.
</div> </div>
</body> </body>
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2
docs/build/html/_sources/index.rst.txt vendored
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@ -76,7 +76,7 @@ Features
Datasets Datasets
Evaluation Evaluation
Methods Methods
Model Selection Model-Selection
Plotting Plotting
API Developers documentation<modules> API Developers documentation<modules>

23
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@ -1,27 +1,38 @@
:tocdepth: 2
quapy.classification package quapy.classification package
============================ ============================
Submodules Submodules
---------- ----------
quapy.classification.methods module quapy.classification.calibration
----------------------------------- --------------------------------
.. versionadded:: 0.1.7
.. automodule:: quapy.classification.calibration
:members:
:undoc-members:
:show-inheritance:
quapy.classification.methods
----------------------------
.. automodule:: quapy.classification.methods .. automodule:: quapy.classification.methods
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
quapy.classification.neural module quapy.classification.neural
---------------------------------- ---------------------------
.. automodule:: quapy.classification.neural .. automodule:: quapy.classification.neural
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
quapy.classification.svmperf module quapy.classification.svmperf
----------------------------------- ----------------------------
.. automodule:: quapy.classification.svmperf .. automodule:: quapy.classification.svmperf
:members: :members:

18
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@ -1,35 +1,37 @@
:tocdepth: 2
quapy.data package quapy.data package
================== ==================
Submodules Submodules
---------- ----------
quapy.data.base module quapy.data.base
---------------------- ---------------
.. automodule:: quapy.data.base .. automodule:: quapy.data.base
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
quapy.data.datasets module quapy.data.datasets
-------------------------- -------------------
.. automodule:: quapy.data.datasets .. automodule:: quapy.data.datasets
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
quapy.data.preprocessing module quapy.data.preprocessing
------------------------------- ------------------------
.. automodule:: quapy.data.preprocessing .. automodule:: quapy.data.preprocessing
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
quapy.data.reader module quapy.data.reader
------------------------ -----------------
.. automodule:: quapy.data.reader .. automodule:: quapy.data.reader
:members: :members:

22
docs/build/html/_sources/quapy.method.rst.txt vendored
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@ -1,43 +1,45 @@
:tocdepth: 2
quapy.method package quapy.method package
==================== ====================
Submodules Submodules
---------- ----------
quapy.method.aggregative module quapy.method.aggregative
------------------------------- ------------------------
.. automodule:: quapy.method.aggregative .. automodule:: quapy.method.aggregative
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
quapy.method.base module quapy.method.base
------------------------ -----------------
.. automodule:: quapy.method.base .. automodule:: quapy.method.base
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
quapy.method.meta module quapy.method.meta
------------------------ -----------------
.. automodule:: quapy.method.meta .. automodule:: quapy.method.meta
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
quapy.method.neural module quapy.method.neural
-------------------------- -------------------
.. automodule:: quapy.method.neural .. automodule:: quapy.method.neural
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
quapy.method.non\_aggregative module quapy.method.non\_aggregative
------------------------------------ -----------------------------
.. automodule:: quapy.method.non_aggregative .. automodule:: quapy.method.non_aggregative
:members: :members:

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@ -1,68 +1,79 @@
:tocdepth: 2
quapy package quapy package
============= =============
Subpackages
-----------
.. toctree::
:maxdepth: 4
quapy.classification
quapy.data
quapy.method
quapy.tests
Submodules Submodules
---------- ----------
quapy.error module quapy.error
------------------ -----------
.. automodule:: quapy.error .. automodule:: quapy.error
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
quapy.evaluation module quapy.evaluation
----------------------- ----------------
.. automodule:: quapy.evaluation .. automodule:: quapy.evaluation
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
quapy.functional module quapy.protocol
----------------------- --------------
.. versionadded:: 0.1.7
.. automodule:: quapy.protocol
:members:
:undoc-members:
:show-inheritance:
quapy.functional
----------------
.. automodule:: quapy.functional .. automodule:: quapy.functional
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
quapy.model\_selection module quapy.model\_selection
----------------------------- ----------------------
.. automodule:: quapy.model_selection .. automodule:: quapy.model_selection
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
quapy.plot module quapy.plot
----------------- ----------
.. automodule:: quapy.plot .. automodule:: quapy.plot
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
quapy.util module quapy.util
----------------- ----------
.. automodule:: quapy.util .. automodule:: quapy.util
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
Subpackages
-----------
.. toctree::
:maxdepth: 3
quapy.classification
quapy.data
quapy.method
Module contents Module contents
--------------- ---------------
@ -70,3 +81,4 @@ Module contents
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:

37
docs/build/html/_sources/quapy.tests.rst.txt vendored
View File

@ -1,37 +0,0 @@
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:

7
docs/build/html/_sources/readme.rst.txt vendored
View File

@ -1,7 +0,0 @@
Getting Started
===============
QuaPy is an open source framework for Quantification (a.k.a. Supervised Prevalence Estimation) written in Python.
Installation
------------
>>> pip install quapy

1
docs/build/html/_sources/readme2.md.txt vendored
View File

@ -1 +0,0 @@
.. include:: ../../README.md

701
docs/build/html/_static/alabaster.css vendored
View File

@ -1,701 +0,0 @@
@import url("basic.css");
/* -- page layout ----------------------------------------------------------- */
body {
font-family: Georgia, serif;
font-size: 17px;
background-color: #fff;
color: #000;
margin: 0;
padding: 0;
}
div.document {
width: 940px;
margin: 30px auto 0 auto;
}
div.documentwrapper {
float: left;
width: 100%;
}
div.bodywrapper {
margin: 0 0 0 220px;
}
div.sphinxsidebar {
width: 220px;
font-size: 14px;
line-height: 1.5;
}
hr {
border: 1px solid #B1B4B6;
}
div.body {
background-color: #fff;
color: #3E4349;
padding: 0 30px 0 30px;
}
div.body > .section {
text-align: left;
}
div.footer {
width: 940px;
margin: 20px auto 30px auto;
font-size: 14px;
color: #888;
text-align: right;
}
div.footer a {
color: #888;
}
p.caption {
font-family: inherit;
font-size: inherit;
}
div.relations {
display: none;
}
div.sphinxsidebar a {
color: #444;
text-decoration: none;
border-bottom: 1px dotted #999;
}
div.sphinxsidebar a:hover {
border-bottom: 1px solid #999;
}
div.sphinxsidebarwrapper {
padding: 18px 10px;
}
div.sphinxsidebarwrapper p.logo {
padding: 0;
margin: -10px 0 0 0px;
text-align: center;
}
div.sphinxsidebarwrapper h1.logo {
margin-top: -10px;
text-align: center;
margin-bottom: 5px;
text-align: left;
}
div.sphinxsidebarwrapper h1.logo-name {
margin-top: 0px;
}
div.sphinxsidebarwrapper p.blurb {
margin-top: 0;
font-style: normal;
}
div.sphinxsidebar h3,
div.sphinxsidebar h4 {
font-family: Georgia, serif;
color: #444;
font-size: 24px;
font-weight: normal;
margin: 0 0 5px 0;
padding: 0;
}
div.sphinxsidebar h4 {
font-size: 20px;
}
div.sphinxsidebar h3 a {
color: #444;
}
div.sphinxsidebar p.logo a,
div.sphinxsidebar h3 a,
div.sphinxsidebar p.logo a:hover,
div.sphinxsidebar h3 a:hover {
border: none;
}
div.sphinxsidebar p {
color: #555;
margin: 10px 0;
}
div.sphinxsidebar ul {
margin: 10px 0;
padding: 0;
color: #000;
}
div.sphinxsidebar ul li.toctree-l1 > a {
font-size: 120%;
}
div.sphinxsidebar ul li.toctree-l2 > a {
font-size: 110%;
}
div.sphinxsidebar input {
border: 1px solid #CCC;
font-family: Georgia, serif;
font-size: 1em;
}
div.sphinxsidebar hr {
border: none;
height: 1px;
color: #AAA;
background: #AAA;
text-align: left;
margin-left: 0;
width: 50%;
}
div.sphinxsidebar .badge {
border-bottom: none;
}
div.sphinxsidebar .badge:hover {
border-bottom: none;
}
/* To address an issue with donation coming after search */
div.sphinxsidebar h3.donation {
margin-top: 10px;
}
/* -- body styles ----------------------------------------------------------- */
a {
color: #004B6B;
text-decoration: underline;
}
a:hover {
color: #6D4100;
text-decoration: underline;
}
div.body h1,
div.body h2,
div.body h3,
div.body h4,
div.body h5,
div.body h6 {
font-family: Georgia, serif;
font-weight: normal;
margin: 30px 0px 10px 0px;
padding: 0;
}
div.body h1 { margin-top: 0; padding-top: 0; font-size: 240%; }
div.body h2 { font-size: 180%; }
div.body h3 { font-size: 150%; }
div.body h4 { font-size: 130%; }
div.body h5 { font-size: 100%; }
div.body h6 { font-size: 100%; }
a.headerlink {
color: #DDD;
padding: 0 4px;
text-decoration: none;
}
a.headerlink:hover {
color: #444;
background: #EAEAEA;
}
div.body p, div.body dd, div.body li {
line-height: 1.4em;
}
div.admonition {
margin: 20px 0px;
padding: 10px 30px;
background-color: #EEE;
border: 1px solid #CCC;
}
div.admonition tt.xref, div.admonition code.xref, div.admonition a tt {
background-color: #FBFBFB;
border-bottom: 1px solid #fafafa;
}
div.admonition p.admonition-title {
font-family: Georgia, serif;
font-weight: normal;
font-size: 24px;
margin: 0 0 10px 0;
padding: 0;
line-height: 1;
}
div.admonition p.last {
margin-bottom: 0;
}
div.highlight {
background-color: #fff;
}
dt:target, .highlight {
background: #FAF3E8;
}
div.warning {
background-color: #FCC;
border: 1px solid #FAA;
}
div.danger {
background-color: #FCC;
border: 1px solid #FAA;
-moz-box-shadow: 2px 2px 4px #D52C2C;
-webkit-box-shadow: 2px 2px 4px #D52C2C;
box-shadow: 2px 2px 4px #D52C2C;
}
div.error {
background-color: #FCC;
border: 1px solid #FAA;
-moz-box-shadow: 2px 2px 4px #D52C2C;
-webkit-box-shadow: 2px 2px 4px #D52C2C;
box-shadow: 2px 2px 4px #D52C2C;
}
div.caution {
background-color: #FCC;
border: 1px solid #FAA;
}
div.attention {
background-color: #FCC;
border: 1px solid #FAA;
}
div.important {
background-color: #EEE;
border: 1px solid #CCC;
}
div.note {
background-color: #EEE;
border: 1px solid #CCC;
}
div.tip {
background-color: #EEE;
border: 1px solid #CCC;
}
div.hint {
background-color: #EEE;
border: 1px solid #CCC;
}
div.seealso {
background-color: #EEE;
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;
}
}

62
docs/build/html/_static/basic.css vendored
View File

@ -4,7 +4,7 @@
* *
* Sphinx stylesheet -- basic theme. * Sphinx stylesheet -- basic theme.
* *
* :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS. * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details. * :license: BSD, see LICENSE for details.
* *
*/ */
@ -222,7 +222,7 @@ table.modindextable td {
/* -- general body styles --------------------------------------------------- */ /* -- general body styles --------------------------------------------------- */
div.body { div.body {
min-width: 450px; min-width: 360px;
max-width: 800px; max-width: 800px;
} }
@ -237,16 +237,6 @@ a.headerlink {
visibility: hidden; visibility: hidden;
} }
a.brackets:before,
span.brackets > a:before{
content: "[";
}
a.brackets:after,
span.brackets > a:after {
content: "]";
}
h1:hover > a.headerlink, h1:hover > a.headerlink,
h2:hover > a.headerlink, h2:hover > a.headerlink,
h3:hover > a.headerlink, h3:hover > a.headerlink,
@ -334,13 +324,15 @@ aside.sidebar {
p.sidebar-title { p.sidebar-title {
font-weight: bold; font-weight: bold;
} }
nav.contents,
aside.topic,
div.admonition, div.topic, blockquote { div.admonition, div.topic, blockquote {
clear: left; clear: left;
} }
/* -- topics ---------------------------------------------------------------- */ /* -- topics ---------------------------------------------------------------- */
nav.contents,
aside.topic,
div.topic { div.topic {
border: 1px solid #ccc; border: 1px solid #ccc;
padding: 7px; padding: 7px;
@ -379,6 +371,8 @@ div.body p.centered {
div.sidebar > :last-child, div.sidebar > :last-child,
aside.sidebar > :last-child, aside.sidebar > :last-child,
nav.contents > :last-child,
aside.topic > :last-child,
div.topic > :last-child, div.topic > :last-child,
div.admonition > :last-child { div.admonition > :last-child {
margin-bottom: 0; margin-bottom: 0;
@ -386,6 +380,8 @@ div.admonition > :last-child {
div.sidebar::after, div.sidebar::after,
aside.sidebar::after, aside.sidebar::after,
nav.contents::after,
aside.topic::after,
div.topic::after, div.topic::after,
div.admonition::after, div.admonition::after,
blockquote::after { blockquote::after {
@ -428,10 +424,6 @@ table.docutils td, table.docutils th {
border-bottom: 1px solid #aaa; border-bottom: 1px solid #aaa;
} }
table.footnote td, table.footnote th {
border: 0 !important;
}
th { th {
text-align: left; text-align: left;
padding-right: 5px; padding-right: 5px;
@ -614,20 +606,26 @@ ol.simple p,
ul.simple p { ul.simple p {
margin-bottom: 0; margin-bottom: 0;
} }
aside.footnote > span,
dl.footnote > dt, div.citation > span {
dl.citation > dt {
float: left; float: left;
margin-right: 0.5em;
} }
aside.footnote > span:last-of-type,
dl.footnote > dd, div.citation > span:last-of-type {
dl.citation > dd { padding-right: 0.5em;
}
aside.footnote > p {
margin-left: 2em;
}
div.citation > p {
margin-left: 4em;
}
aside.footnote > p:last-of-type,
div.citation > p:last-of-type {
margin-bottom: 0em; margin-bottom: 0em;
} }
aside.footnote > p:last-of-type:after,
dl.footnote > dd:after, div.citation > p:last-of-type:after {
dl.citation > dd:after {
content: ""; content: "";
clear: both; clear: both;
} }
@ -644,10 +642,6 @@ dl.field-list > dt {
padding-right: 5px; padding-right: 5px;
} }
dl.field-list > dt:after {
content: ":";
}
dl.field-list > dd { dl.field-list > dd {
padding-left: 0.5em; padding-left: 0.5em;
margin-top: 0em; margin-top: 0em;
@ -731,8 +725,9 @@ dl.glossary dt {
.classifier:before { .classifier:before {
font-style: normal; font-style: normal;
margin: 0.5em; margin: 0 0.5em;
content: ":"; content: ":";
display: inline-block;
} }
abbr, acronym { abbr, acronym {
@ -756,6 +751,7 @@ span.pre {
-ms-hyphens: none; -ms-hyphens: none;
-webkit-hyphens: none; -webkit-hyphens: none;
hyphens: none; hyphens: none;
white-space: nowrap;
} }
div[class*="highlight-"] { div[class*="highlight-"] {

2
docs/build/html/_static/bizstyle.css vendored
View File

@ -294,6 +294,8 @@ div.quotebar {
padding: 2px 7px; padding: 2px 7px;
border: 1px solid #ccc; border: 1px solid #ccc;
} }
nav.contents,
aside.topic,
div.topic { div.topic {
background-color: #f8f8f8; background-color: #f8f8f8;

43
docs/build/html/_static/bizstyle.js vendored
View File

@ -9,33 +9,22 @@
// :copyright: Copyright 2012-2014 by Sphinx team, see AUTHORS. // :copyright: Copyright 2012-2014 by Sphinx team, see AUTHORS.
// :license: BSD, see LICENSE for details. // :license: BSD, see LICENSE for details.
// //
$(document).ready(function(){ const initialiseBizStyle = () => {
if (navigator.userAgent.indexOf('iPhone') > 0 || if (navigator.userAgent.indexOf("iPhone") > 0 || navigator.userAgent.indexOf("Android") > 0) {
navigator.userAgent.indexOf('Android') > 0) { document.querySelector("li.nav-item-0 a").innerText = "Top"
$("li.nav-item-0 a").text("Top"); }
const truncator = item => {if (item.textContent.length > 20) {
item.title = item.innerText
item.innerText = item.innerText.substr(0, 17) + "..."
}
}
document.querySelectorAll("div.related:first ul li:not(.right) a").slice(1).forEach(truncator);
document.querySelectorAll("div.related:last ul li:not(.right) a").slice(1).forEach(truncator);
} }
$("div.related:first ul li:not(.right) a").slice(1).each(function(i, item){ window.addEventListener("resize",
if (item.text.length > 20) { () => (document.querySelector("li.nav-item-0 a").innerText = (window.innerWidth <= 776) ? "Top" : "QuaPy 0.1.7 documentation")
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) + "...");
}
});
});
$(window).resize(function(){ if (document.readyState !== "loading") initialiseBizStyle()
if ($(window).width() <= 776) { else document.addEventListener("DOMContentLoaded", initialiseBizStyle)
$("li.nav-item-0 a").text("Top");
}
else {
$("li.nav-item-0 a").text("QuaPy 0.1.6 documentation");
}
});

1
docs/build/html/_static/custom.css vendored
View File

@ -1 +0,0 @@
/* This file intentionally left blank. */

395
docs/build/html/_static/doctools.js vendored
View File

@ -2,322 +2,155 @@
* doctools.js * doctools.js
* ~~~~~~~~~~~ * ~~~~~~~~~~~
* *
* Sphinx JavaScript utilities for all documentation. * Base JavaScript utilities for all Sphinx HTML documentation.
* *
* :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS. * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details. * :license: BSD, see LICENSE for details.
* *
*/ */
"use strict";
/** const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
* select a different prefix for underscore "TEXTAREA",
*/ "INPUT",
$u = _.noConflict(); "SELECT",
"BUTTON",
]);
/** const _ready = (callback) => {
* make the code below compatible with browsers without if (document.readyState !== "loading") {
* an installed firebug like debugger callback();
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, ' '));
};
/**
* 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 = {};
for (var i = 0; i < parts.length; i++) {
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;
};
/**
* 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 { } else {
span = document.createElement("span"); document.addEventListener("DOMContentLoaded", callback);
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,
"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. * Small JavaScript module for the documentation.
*/ */
var Documentation = { const Documentation = {
init: () => {
init : function() { Documentation.initDomainIndexTable();
this.fixFirefoxAnchorBug(); Documentation.initOnKeyListeners();
this.highlightSearchWords();
this.initIndexTable();
if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) {
this.initOnKeyListeners();
}
}, },
/** /**
* i18n support * i18n support
*/ */
TRANSLATIONS: {}, TRANSLATIONS: {},
PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; }, PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
LOCALE : 'unknown', LOCALE: "unknown",
// gettext and ngettext don't access this so that the functions // gettext and ngettext don't access this so that the functions
// can safely bound to a different name (_ = Documentation.gettext) // can safely bound to a different name (_ = Documentation.gettext)
gettext : function(string) { gettext: (string) => {
var translated = Documentation.TRANSLATIONS[string]; const translated = Documentation.TRANSLATIONS[string];
if (typeof translated === 'undefined') switch (typeof translated) {
return string; case "undefined":
return (typeof translated === 'string') ? translated : translated[0]; return string; // no translation
}, case "string":
return translated; // translation exists
ngettext : function(singular, plural, n) { default:
var translated = Documentation.TRANSLATIONS[singular]; return translated[0]; // (singular, plural) translation tuple exists
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'));
} }
}, },
/** ngettext: (singular, plural, n) => {
* init the domain index toggle buttons const translated = Documentation.TRANSLATIONS[singular];
*/ if (typeof translated !== "undefined")
initIndexTable : function() { return translated[Documentation.PLURAL_EXPR(n)];
var togglers = $('img.toggler').click(function() { return n === 1 ? singular : plural;
var src = $(this).attr('src'); },
var idnum = $(this).attr('id').substr(7);
$('tr.cg-' + idnum).toggle(); addTranslations: (catalog) => {
if (src.substr(-9) === 'minus.png') Object.assign(Documentation.TRANSLATIONS, catalog.messages);
$(this).attr('src', src.substr(0, src.length-9) + 'plus.png'); Documentation.PLURAL_EXPR = new Function(
else "n",
$(this).attr('src', src.substr(0, src.length-8) + 'minus.png'); `return (${catalog.plural_expr})`
}).css('display', ''); );
if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) { Documentation.LOCALE = catalog.locale;
togglers.click();
}
}, },
/** /**
* helper function to hide the search marks again * helper function to focus on search bar
*/ */
hideSearchWords : function() { focusSearchBar: () => {
$('#searchbox .highlight-link').fadeOut(300); document.querySelectorAll("input[name=q]")[0]?.focus();
$('span.highlighted').removeClass('highlighted');
}, },
/** /**
* make the url absolute * Initialise the domain index toggle buttons
*/ */
makeURL : function(relativeURL) { initDomainIndexTable: () => {
return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL; const toggler = (el) => {
}, const idNumber = el.id.substr(7);
const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
/** if (el.src.substr(-9) === "minus.png") {
* get the current relative url el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
*/ toggledRows.forEach((el) => (el.style.display = "none"));
getCurrentURL : function() { } else {
var path = document.location.pathname; el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
var parts = path.split(/\//); toggledRows.forEach((el) => (el.style.display = ""));
$.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 const togglerElements = document.querySelectorAll("img.toggler");
_ = Documentation.gettext; togglerElements.forEach((el) =>
el.addEventListener("click", (event) => toggler(event.currentTarget))
);
togglerElements.forEach((el) => (el.style.display = ""));
if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
},
$(document).ready(function() { initOnKeyListeners: () => {
Documentation.init(); // only install a listener if it is really needed
if (
!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
)
return;
document.addEventListener("keydown", (event) => {
// bail for input elements
if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
// bail with special keys
if (event.altKey || event.ctrlKey || event.metaKey) return;
if (!event.shiftKey) {
switch (event.key) {
case "ArrowLeft":
if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
const prevLink = document.querySelector('link[rel="prev"]');
if (prevLink && prevLink.href) {
window.location.href = prevLink.href;
event.preventDefault();
}
break;
case "ArrowRight":
if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
const nextLink = document.querySelector('link[rel="next"]');
if (nextLink && nextLink.href) {
window.location.href = nextLink.href;
event.preventDefault();
}
break;
}
}
// some keyboard layouts may need Shift to get /
switch (event.key) {
case "/":
if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
Documentation.focusSearchBar();
event.preventDefault();
}
}); });
},
};
// quick alias for translations
const _ = Documentation.gettext;
_ready(Documentation.init);

8
docs/build/html/_static/documentation_options.js vendored
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@ -1,12 +1,14 @@
var DOCUMENTATION_OPTIONS = { var DOCUMENTATION_OPTIONS = {
URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
VERSION: '0.1.6', VERSION: '0.1.7',
LANGUAGE: 'None', LANGUAGE: 'en',
COLLAPSE_INDEX: false, COLLAPSE_INDEX: false,
BUILDER: 'html', BUILDER: 'html',
FILE_SUFFIX: '.html', FILE_SUFFIX: '.html',
LINK_SUFFIX: '.html', LINK_SUFFIX: '.html',
HAS_SOURCE: true, HAS_SOURCE: true,
SOURCELINK_SUFFIX: '.txt', SOURCELINK_SUFFIX: '.txt',
NAVIGATION_WITH_KEYS: false NAVIGATION_WITH_KEYS: false,
SHOW_SEARCH_SUMMARY: true,
ENABLE_SEARCH_SHORTCUTS: true,
}; };

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100
docs/build/html/_static/language_data.js vendored
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@ -5,7 +5,7 @@
* This script contains the language-specific data used by searchtools.js, * This script contains the language-specific data used by searchtools.js,
* namely the list of stopwords, stemmer, scorer and splitter. * namely the list of stopwords, stemmer, scorer and splitter.
* *
* :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS. * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details. * :license: BSD, see LICENSE for details.
* *
*/ */
@ -197,101 +197,3 @@ var Stemmer = function() {
} }
} }
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;
}

774
docs/build/html/_static/searchtools.js vendored
View File

@ -4,22 +4,24 @@
* *
* Sphinx JavaScript utilities for the full-text search. * Sphinx JavaScript utilities for the full-text search.
* *
* :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS. * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details. * :license: BSD, see LICENSE for details.
* *
*/ */
"use strict";
if (!Scorer) {
/** /**
* Simple result scoring code. * Simple result scoring code.
*/ */
if (typeof Scorer === "undefined") {
var Scorer = { var Scorer = {
// Implement the following function to further tweak the score for each result // Implement the following function to further tweak the score for each result
// The function takes a result array [filename, title, anchor, descr, score] // The function takes a result array [docname, title, anchor, descr, score, filename]
// and returns the new score. // and returns the new score.
/* /*
score: function(result) { score: result => {
return result[4]; const [docname, title, anchor, descr, score, filename] = result
return score
}, },
*/ */
@ -28,9 +30,11 @@ if (!Scorer) {
// or matches in the last dotted part of the object name // or matches in the last dotted part of the object name
objPartialMatch: 6, objPartialMatch: 6,
// Additive scores depending on the priority of the object // Additive scores depending on the priority of the object
objPrio: {0: 15, // used to be importantResults objPrio: {
0: 15, // used to be importantResults
1: 5, // used to be objectResults 1: 5, // used to be objectResults
2: -5}, // used to be unimportantResults 2: -5, // used to be unimportantResults
},
// Used when the priority is not in the mapping. // Used when the priority is not in the mapping.
objPrioDefault: 0, objPrioDefault: 0,
@ -39,455 +43,495 @@ if (!Scorer) {
partialTitle: 7, partialTitle: 7,
// query found in terms // query found in terms
term: 5, term: 5,
partialTerm: 2 partialTerm: 2,
}; };
} }
if (!splitQuery) { const _removeChildren = (element) => {
function splitQuery(query) { while (element && element.lastChild) element.removeChild(element.lastChild);
return query.split(/\s+/); };
/**
* See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
*/
const _escapeRegExp = (string) =>
string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
const _displayItem = (item, searchTerms) => {
const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT;
const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
const [docName, title, anchor, descr, score, _filename] = item;
let listItem = document.createElement("li");
let requestUrl;
let linkUrl;
if (docBuilder === "dirhtml") {
// dirhtml builder
let dirname = docName + "/";
if (dirname.match(/\/index\/$/))
dirname = dirname.substring(0, dirname.length - 6);
else if (dirname === "index/") dirname = "";
requestUrl = docUrlRoot + dirname;
linkUrl = requestUrl;
} else {
// normal html builders
requestUrl = docUrlRoot + docName + docFileSuffix;
linkUrl = docName + docLinkSuffix;
} }
let linkEl = listItem.appendChild(document.createElement("a"));
linkEl.href = linkUrl + anchor;
linkEl.dataset.score = score;
linkEl.innerHTML = title;
if (descr)
listItem.appendChild(document.createElement("span")).innerHTML =
" (" + descr + ")";
else if (showSearchSummary)
fetch(requestUrl)
.then((responseData) => responseData.text())
.then((data) => {
if (data)
listItem.appendChild(
Search.makeSearchSummary(data, searchTerms)
);
});
Search.output.appendChild(listItem);
};
const _finishSearch = (resultCount) => {
Search.stopPulse();
Search.title.innerText = _("Search Results");
if (!resultCount)
Search.status.innerText = Documentation.gettext(
"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.innerText = _(
`Search finished, found ${resultCount} page(s) matching the search query.`
);
};
const _displayNextItem = (
results,
resultCount,
searchTerms
) => {
// results left, load the summary and display it
// this is intended to be dynamic (don't sub resultsCount)
if (results.length) {
_displayItem(results.pop(), searchTerms);
setTimeout(
() => _displayNextItem(results, resultCount, searchTerms),
5
);
}
// search finished, update title and status message
else _finishSearch(resultCount);
};
/**
* Default splitQuery function. Can be overridden in ``sphinx.search`` with a
* custom function per language.
*
* The regular expression works by splitting the string on consecutive characters
* that are not Unicode letters, numbers, underscores, or emoji characters.
* This is the same as ``\W+`` in Python, preserving the surrogate pair area.
*/
if (typeof splitQuery === "undefined") {
var splitQuery = (query) => query
.split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
.filter(term => term) // remove remaining empty strings
} }
/** /**
* Search Module * Search Module
*/ */
var Search = { const Search = {
_index: null, _index: null,
_queued_query: null, _queued_query: null,
_pulse_status: -1, _pulse_status: -1,
htmlToText : function(htmlString) { htmlToText: (htmlString) => {
var virtualDocument = document.implementation.createHTMLDocument('virtual'); const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
var htmlElement = $(htmlString, virtualDocument); htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() });
htmlElement.find('.headerlink').remove(); const docContent = htmlElement.querySelector('[role="main"]');
docContent = htmlElement.find('[role=main]')[0]; if (docContent !== undefined) return docContent.textContent;
if(docContent === undefined) { console.warn(
console.warn("Content block not found. Sphinx search tries to obtain it " + "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template."
"via '[role=main]'. Could you check your theme or template."); );
return ""; return "";
}
return docContent.textContent || docContent.innerText;
}, },
init : function() { init: () => {
var params = $.getQueryParameters(); const query = new URLSearchParams(window.location.search).get("q");
if (params.q) { document
var query = params.q[0]; .querySelectorAll('input[name="q"]')
$('input[name="q"]')[0].value = query; .forEach((el) => (el.value = query));
this.performSearch(query); if (query) Search.performSearch(query);
},
loadIndex: (url) =>
(document.body.appendChild(document.createElement("script")).src = url),
setIndex: (index) => {
Search._index = index;
if (Search._queued_query !== null) {
const query = Search._queued_query;
Search._queued_query = null;
Search.query(query);
} }
}, },
loadIndex : function(url) { hasIndex: () => Search._index !== null,
$.ajax({type: "GET", url: url, data: null,
dataType: "script", cache: true,
complete: function(jqxhr, textstatus) {
if (textstatus != "success") {
document.getElementById("searchindexloader").src = url;
}
}});
},
setIndex : function(index) { deferQuery: (query) => (Search._queued_query = query),
var q;
this._index = index;
if ((q = this._queued_query) !== null) {
this._queued_query = null;
Search.query(q);
}
},
hasIndex : function() { stopPulse: () => (Search._pulse_status = -1),
return this._index !== null;
},
deferQuery : function(query) { startPulse: () => {
this._queued_query = query; if (Search._pulse_status >= 0) return;
},
stopPulse : function() { const pulse = () => {
this._pulse_status = 0;
},
startPulse : function() {
if (this._pulse_status >= 0)
return;
function pulse() {
var i;
Search._pulse_status = (Search._pulse_status + 1) % 4; Search._pulse_status = (Search._pulse_status + 1) % 4;
var dotString = ''; Search.dots.innerText = ".".repeat(Search._pulse_status);
for (i = 0; i < Search._pulse_status; i++) if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
dotString += '.'; };
Search.dots.text(dotString);
if (Search._pulse_status > -1)
window.setTimeout(pulse, 500);
}
pulse(); pulse();
}, },
/** /**
* perform a search for something (or wait until index is loaded) * perform a search for something (or wait until index is loaded)
*/ */
performSearch : function(query) { performSearch: (query) => {
// create the required interface elements // create the required interface elements
this.out = $('#search-results'); const searchText = document.createElement("h2");
this.title = $('<h2>' + _('Searching') + '</h2>').appendTo(this.out); searchText.textContent = _("Searching");
this.dots = $('<span></span>').appendTo(this.title); const searchSummary = document.createElement("p");
this.status = $('<p class="search-summary">&nbsp;</p>').appendTo(this.out); searchSummary.classList.add("search-summary");
this.output = $('<ul class="search"/>').appendTo(this.out); searchSummary.innerText = "";
const searchList = document.createElement("ul");
searchList.classList.add("search");
$('#search-progress').text(_('Preparing search...')); const out = document.getElementById("search-results");
this.startPulse(); Search.title = out.appendChild(searchText);
Search.dots = Search.title.appendChild(document.createElement("span"));
Search.status = out.appendChild(searchSummary);
Search.output = out.appendChild(searchList);
const searchProgress = document.getElementById("search-progress");
// Some themes don't use the search progress node
if (searchProgress) {
searchProgress.innerText = _("Preparing search...");
}
Search.startPulse();
// index already loaded, the browser was quick! // index already loaded, the browser was quick!
if (this.hasIndex()) if (Search.hasIndex()) Search.query(query);
this.query(query); else Search.deferQuery(query);
else
this.deferQuery(query);
}, },
/** /**
* execute search (requires search index to be loaded) * execute search (requires search index to be loaded)
*/ */
query : function(query) { query: (query) => {
var i; const filenames = Search._index.filenames;
const docNames = Search._index.docnames;
const titles = Search._index.titles;
const allTitles = Search._index.alltitles;
const indexEntries = Search._index.indexentries;
// stem the search terms and add them to the correct list // stem the search terms and add them to the correct list
var stemmer = new Stemmer(); const stemmer = new Stemmer();
var searchterms = []; const searchTerms = new Set();
var excluded = []; const excludedTerms = new Set();
var hlterms = []; const highlightTerms = new Set();
var tmp = splitQuery(query); const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
var objectterms = []; splitQuery(query.trim()).forEach((queryTerm) => {
for (i = 0; i < tmp.length; i++) { const queryTermLower = queryTerm.toLowerCase();
if (tmp[i] !== "") {
objectterms.push(tmp[i].toLowerCase()); // maybe skip this "word"
} // stopwords array is from language_data.js
if (
stopwords.indexOf(queryTermLower) !== -1 ||
queryTerm.match(/^\d+$/)
)
return;
if ($u.indexOf(stopwords, tmp[i].toLowerCase()) != -1 || tmp[i] === "") {
// skip this "word"
continue;
}
// stem the word // stem the word
var word = stemmer.stemWord(tmp[i].toLowerCase()); let word = stemmer.stemWord(queryTermLower);
// prevent stemmer from cutting word smaller than two chars
if(word.length < 3 && tmp[i].length >= 3) {
word = tmp[i];
}
var toAppend;
// select the correct list // select the correct list
if (word[0] == '-') { if (word[0] === "-") excludedTerms.add(word.substr(1));
toAppend = excluded;
word = word.substr(1);
}
else { else {
toAppend = searchterms; searchTerms.add(word);
hlterms.push(tmp[i].toLowerCase()); highlightTerms.add(queryTermLower);
} }
// only add if not already in the list });
if (!$u.contains(toAppend, word))
toAppend.push(word); if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js
localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
} }
var highlightstring = '?highlight=' + $.urlencode(hlterms.join(" "));
// console.debug('SEARCH: searching for:'); // console.debug("SEARCH: searching for:");
// console.info('required: ', searchterms); // console.info("required: ", [...searchTerms]);
// console.info('excluded: ', excluded); // console.info("excluded: ", [...excludedTerms]);
// prepare search // array of [docname, title, anchor, descr, score, filename]
var terms = this._index.terms; let results = [];
var titleterms = this._index.titleterms; _removeChildren(document.getElementById("search-progress"));
// array of [filename, title, anchor, descr, score] const queryLower = query.toLowerCase();
var results = []; for (const [title, foundTitles] of Object.entries(allTitles)) {
$('#search-progress').empty(); if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) {
for (const [file, id] of foundTitles) {
let score = Math.round(100 * queryLower.length / title.length)
results.push([
docNames[file],
titles[file] !== title ? `${titles[file]} > ${title}` : title,
id !== null ? "#" + id : "",
null,
score,
filenames[file],
]);
}
}
}
// search for explicit entries in index directives
for (const [entry, foundEntries] of Object.entries(indexEntries)) {
if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
for (const [file, id] of foundEntries) {
let score = Math.round(100 * queryLower.length / entry.length)
results.push([
docNames[file],
titles[file],
id ? "#" + id : "",
null,
score,
filenames[file],
]);
}
}
}
// lookup as object // lookup as object
for (i = 0; i < objectterms.length; i++) { objectTerms.forEach((term) =>
var others = [].concat(objectterms.slice(0, i), results.push(...Search.performObjectSearch(term, objectTerms))
objectterms.slice(i+1, objectterms.length)); );
results = results.concat(this.performObjectSearch(objectterms[i], others));
}
// lookup as search terms in fulltext // lookup as search terms in fulltext
results = results.concat(this.performTermsSearch(searchterms, excluded, terms, titleterms)); results.push(...Search.performTermsSearch(searchTerms, excludedTerms));
// let the scorer override scores with a custom scoring function // let the scorer override scores with a custom scoring function
if (Scorer.score) { if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item)));
for (i = 0; i < results.length; i++)
results[i][4] = Scorer.score(results[i]);
}
// now sort the results by score (in opposite order of appearance, since the // now sort the results by score (in opposite order of appearance, since the
// display function below uses pop() to retrieve items) and then // display function below uses pop() to retrieve items) and then
// alphabetically // alphabetically
results.sort(function(a, b) { results.sort((a, b) => {
var left = a[4]; const leftScore = a[4];
var right = b[4]; const rightScore = b[4];
if (left > right) { if (leftScore === rightScore) {
return 1;
} else if (left < right) {
return -1;
} else {
// same score: sort alphabetically // same score: sort alphabetically
left = a[1].toLowerCase(); const leftTitle = a[1].toLowerCase();
right = b[1].toLowerCase(); const rightTitle = b[1].toLowerCase();
return (left > right) ? -1 : ((left < right) ? 1 : 0); if (leftTitle === rightTitle) return 0;
return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
} }
return leftScore > rightScore ? 1 : -1;
}); });
// remove duplicate search results
// note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
let seen = new Set();
results = results.reverse().reduce((acc, result) => {
let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
if (!seen.has(resultStr)) {
acc.push(result);
seen.add(resultStr);
}
return acc;
}, []);
results = results.reverse();
// for debugging // for debugging
//Search.lastresults = results.slice(); // a copy //Search.lastresults = results.slice(); // a copy
//console.info('search results:', Search.lastresults); // console.info("search results:", Search.lastresults);
// print the results // print the results
var resultCount = results.length; _displayNextItem(results, results.length, searchTerms);
function displayNextItem() {
// results left, load the summary and display it
if (results.length) {
var item = results.pop();
var listItem = $('<li></li>');
var requestUrl = "";
var linkUrl = "";
if (DOCUMENTATION_OPTIONS.BUILDER === 'dirhtml') {
// dirhtml builder
var dirname = item[0] + '/';
if (dirname.match(/\/index\/$/)) {
dirname = dirname.substring(0, dirname.length-6);
} 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 * search for object names
*/ */
performObjectSearch : function(object, otherterms) { performObjectSearch: (object, objectTerms) => {
var filenames = this._index.filenames; const filenames = Search._index.filenames;
var docnames = this._index.docnames; const docNames = Search._index.docnames;
var objects = this._index.objects; const objects = Search._index.objects;
var objnames = this._index.objnames; const objNames = Search._index.objnames;
var titles = this._index.titles; const titles = Search._index.titles;
var i; const results = [];
var results = [];
const objectSearchCallback = (prefix, match) => {
const name = match[4]
const fullname = (prefix ? prefix + "." : "") + name;
const fullnameLower = fullname.toLowerCase();
if (fullnameLower.indexOf(object) < 0) return;
let score = 0;
const parts = fullnameLower.split(".");
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 // check for different match types: exact matches of full name or
// "last name" (i.e. last dotted part) // "last name" (i.e. last dotted part)
if (fullnameLower == object || parts[parts.length - 1] == object) { if (fullnameLower === object || parts.slice(-1)[0] === object)
score += Scorer.objNameMatch; score += Scorer.objNameMatch;
// matches in last name else if (parts.slice(-1)[0].indexOf(object) > -1)
} else if (parts[parts.length - 1].indexOf(object) > -1) { score += Scorer.objPartialMatch; // matches in last name
score += Scorer.objPartialMatch;
} const objName = objNames[match[1]][2];
var match = objects[prefix][name]; const title = titles[match[0]];
var objname = objnames[match[1]][2];
var title = titles[match[0]];
// If more than one term searched for, we require other words to be // If more than one term searched for, we require other words to be
// found in the name/title/description // found in the name/title/description
if (otherterms.length > 0) { const otherTerms = new Set(objectTerms);
var haystack = (prefix + ' ' + name + ' ' + otherTerms.delete(object);
objname + ' ' + title).toLowerCase(); if (otherTerms.size > 0) {
var allfound = true; const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
for (i = 0; i < otherterms.length; i++) { if (
if (haystack.indexOf(otherterms[i]) == -1) { [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
allfound = false; )
break; return;
} }
}
if (!allfound) {
continue;
}
}
var descr = objname + _(', in ') + title;
var anchor = match[3]; let anchor = match[3];
if (anchor === '') if (anchor === "") anchor = fullname;
anchor = fullname; else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
else if (anchor == '-')
anchor = objnames[match[1]][1] + '-' + fullname; const descr = objName + _(", in ") + title;
// add custom score for some objects according to scorer // add custom score for some objects according to scorer
if (Scorer.objPrio.hasOwnProperty(match[2])) { if (Scorer.objPrio.hasOwnProperty(match[2]))
score += Scorer.objPrio[match[2]]; score += Scorer.objPrio[match[2]];
} else { else score += Scorer.objPrioDefault;
score += Scorer.objPrioDefault;
}
results.push([docnames[match[0]], fullname, '#'+anchor, descr, score, filenames[match[0]]]);
}
}
}
results.push([
docNames[match[0]],
fullname,
"#" + anchor,
descr,
score,
filenames[match[0]],
]);
};
Object.keys(objects).forEach((prefix) =>
objects[prefix].forEach((array) =>
objectSearchCallback(prefix, array)
)
);
return results; return results;
}, },
/**
* See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions
*/
escapeRegExp : function(string) {
return string.replace(/[.*+\-?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
},
/** /**
* search for full-text terms in the index * search for full-text terms in the index
*/ */
performTermsSearch : function(searchterms, excluded, terms, titleterms) { performTermsSearch: (searchTerms, excludedTerms) => {
var docnames = this._index.docnames; // prepare search
var filenames = this._index.filenames; const terms = Search._index.terms;
var titles = this._index.titles; const titleTerms = Search._index.titleterms;
const filenames = Search._index.filenames;
const docNames = Search._index.docnames;
const titles = Search._index.titles;
var i, j, file; const scoreMap = new Map();
var fileMap = {}; const fileMap = new Map();
var scoreMap = {};
var results = [];
// perform the search on the required terms // perform the search on the required terms
for (i = 0; i < searchterms.length; i++) { searchTerms.forEach((word) => {
var word = searchterms[i]; const files = [];
var files = []; const arr = [
var _o = [
{ files: terms[word], score: Scorer.term }, { files: terms[word], score: Scorer.term },
{files: titleterms[word], score: Scorer.title} { files: titleTerms[word], score: Scorer.title },
]; ];
// add support for partial matches // add support for partial matches
if (word.length > 2) { if (word.length > 2) {
var word_regex = this.escapeRegExp(word); const escapedWord = _escapeRegExp(word);
for (var w in terms) { Object.keys(terms).forEach((term) => {
if (w.match(word_regex) && !terms[word]) { if (term.match(escapedWord) && !terms[word])
_o.push({files: terms[w], score: Scorer.partialTerm}) arr.push({ files: terms[term], score: Scorer.partialTerm });
} });
} Object.keys(titleTerms).forEach((term) => {
for (var w in titleterms) { if (term.match(escapedWord) && !titleTerms[word])
if (w.match(word_regex) && !titleterms[word]) { arr.push({ files: titleTerms[word], score: Scorer.partialTitle });
_o.push({files: titleterms[w], score: Scorer.partialTitle}) });
}
}
} }
// no match but word was a required one // no match but word was a required one
if ($u.every(_o, function(o){return o.files === undefined;})) { if (arr.every((record) => record.files === undefined)) return;
break;
}
// found search word in contents // found search word in contents
$u.each(_o, function(o) { arr.forEach((record) => {
var _files = o.files; if (record.files === undefined) return;
if (_files === undefined)
return
if (_files.length === undefined) let recordFiles = record.files;
_files = [_files]; if (recordFiles.length === undefined) recordFiles = [recordFiles];
files = files.concat(_files); files.push(...recordFiles);
// set score for the word in each file to Scorer.term // set score for the word in each file
for (j = 0; j < _files.length; j++) { recordFiles.forEach((file) => {
file = _files[j]; if (!scoreMap.has(file)) scoreMap.set(file, {});
if (!(file in scoreMap)) scoreMap.get(file)[word] = record.score;
scoreMap[file] = {}; });
scoreMap[file][word] = o.score;
}
}); });
// create the mapping // create the mapping
for (j = 0; j < files.length; j++) { files.forEach((file) => {
file = files[j]; if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1)
if (file in fileMap && fileMap[file].indexOf(word) === -1) fileMap.get(file).push(word);
fileMap[file].push(word); else fileMap.set(file, [word]);
else });
fileMap[file] = [word]; });
}
}
// now check if the files don't contain excluded terms // now check if the files don't contain excluded terms
for (file in fileMap) { const results = [];
var valid = true; for (const [file, wordList] of fileMap) {
// check if all requirements are matched // 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 // as search terms with length < 3 are discarded
const filteredTermCount = [...searchTerms].filter(
(term) => term.length > 2
).length;
if ( if (
fileMap[file].length != searchterms.length && wordList.length !== searchTerms.size &&
fileMap[file].length != filteredTermCount wordList.length !== filteredTermCount
) continue; )
continue;
// ensure that none of the excluded terms is in the search result // ensure that none of the excluded terms is in the search result
for (i = 0; i < excluded.length; i++) { if (
if (terms[excluded[i]] == file || [...excludedTerms].some(
titleterms[excluded[i]] == file || (term) =>
$u.contains(terms[excluded[i]] || [], file) || terms[term] === file ||
$u.contains(titleterms[excluded[i]] || [], file)) { titleTerms[term] === file ||
valid = false; (terms[term] || []).includes(file) ||
(titleTerms[term] || []).includes(file)
)
)
break; 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. // select one (max) score for the file.
// for better ranking, we should calculate ranking by using words statistics like basic tf-idf... const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
var score = $u.max($u.map(fileMap[file], function(w){return scoreMap[file][w]})); // add result to the result list
results.push([docnames[file], titles[file], '', null, score, filenames[file]]); results.push([
} docNames[file],
titles[file],
"",
null,
score,
filenames[file],
]);
} }
return results; return results;
}, },
@ -495,34 +539,28 @@ var Search = {
/** /**
* helper function to return a node containing the * helper function to return a node containing the
* search summary for a given text. keywords is a list * search summary for a given text. keywords is a list
* of stemmed words, hlwords is the list of normal, unstemmed * of stemmed words.
* words. the first one is used to find the occurrence, the
* latter for highlighting it.
*/ */
makeSearchSummary : function(htmlText, keywords, hlwords) { makeSearchSummary: (htmlText, keywords) => {
var text = Search.htmlToText(htmlText); const text = Search.htmlToText(htmlText);
if (text == "") { if (text === "") return null;
return null;
} const textLower = text.toLowerCase();
var textLower = text.toLowerCase(); const actualStartPosition = [...keywords]
var start = 0; .map((k) => textLower.indexOf(k.toLowerCase()))
$.each(keywords, function() { .filter((i) => i > -1)
var i = textLower.indexOf(this.toLowerCase()); .slice(-1)[0];
if (i > -1) const startWithContext = Math.max(actualStartPosition - 120, 0);
start = i;
}); const top = startWithContext === 0 ? "" : "...";
start = Math.max(start - 120, 0); const tail = startWithContext + 240 < text.length ? "..." : "";
var excerpt = ((start > 0) ? '...' : '') +
$.trim(text.substr(start, 240)) + let summary = document.createElement("p");
((start + 240 - text.length) ? '...' : ''); summary.classList.add("context");
var rv = $('<p class="context"></p>').text(excerpt); summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
$.each(hlwords, function() {
rv = rv.highlightText(this, 'highlighted'); return summary;
}); },
return rv;
}
}; };
$(document).ready(function() { _ready(Search.init);
Search.init();
});

319
docs/build/html/genindex.html vendored
View File

@ -2,18 +2,20 @@
<!doctype html> <!doctype html>
<html> <html lang="en">
<head> <head>
<meta charset="utf-8" /> <meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" /> <meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Index &#8212; QuaPy 0.1.6 documentation</title> <title>Index &#8212; QuaPy 0.1.7 documentation</title>
<link rel="stylesheet" type="text/css" href="_static/pygments.css" /> <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
<link rel="stylesheet" type="text/css" href="_static/bizstyle.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 data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
<script src="_static/jquery.js"></script> <script src="_static/jquery.js"></script>
<script src="_static/underscore.js"></script> <script src="_static/underscore.js"></script>
<script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
<script src="_static/doctools.js"></script> <script src="_static/doctools.js"></script>
<script src="_static/sphinx_highlight.js"></script>
<script src="_static/bizstyle.js"></script> <script src="_static/bizstyle.js"></script>
<link rel="index" title="Index" href="#" /> <link rel="index" title="Index" href="#" />
<link rel="search" title="Search" href="search.html" /> <link rel="search" title="Search" href="search.html" />
@ -31,7 +33,7 @@
<li class="right" > <li class="right" >
<a href="py-modindex.html" title="Python Module Index" <a href="py-modindex.html" title="Python Module Index"
>modules</a> |</li> >modules</a> |</li>
<li class="nav-item nav-item-0"><a href="index.html">QuaPy 0.1.6 documentation</a> &#187;</li> <li class="nav-item nav-item-0"><a href="index.html">QuaPy 0.1.7 documentation</a> &#187;</li>
<li class="nav-item nav-item-this"><a href="">Index</a></li> <li class="nav-item nav-item-this"><a href="">Index</a></li>
</ul> </ul>
</div> </div>
@ -68,12 +70,17 @@
| <a href="#V"><strong>V</strong></a> | <a href="#V"><strong>V</strong></a>
| <a href="#W"><strong>W</strong></a> | <a href="#W"><strong>W</strong></a>
| <a href="#X"><strong>X</strong></a> | <a href="#X"><strong>X</strong></a>
| <a href="#Y"><strong>Y</strong></a>
</div> </div>
<h2 id="A">A</h2> <h2 id="A">A</h2>
<table style="width: 100%" class="indextable genindextable"><tr> <table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul> <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.html#quapy.error.absolute_error">absolute_error() (in module quapy.error)</a> <li><a href="quapy.html#quapy.error.absolute_error">absolute_error() (in module quapy.error)</a>
</li>
<li><a href="quapy.html#quapy.protocol.AbstractProtocol">AbstractProtocol (class in quapy.protocol)</a>
</li>
<li><a href="quapy.html#quapy.protocol.AbstractStochasticSeededProtocol">AbstractStochasticSeededProtocol (class in quapy.protocol)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.aggregative.ACC">ACC (class in quapy.method.aggregative)</a> <li><a href="quapy.method.html#quapy.method.aggregative.ACC">ACC (class in quapy.method.aggregative)</a>
</li> </li>
@ -95,6 +102,10 @@
<li><a href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.aggregate">(quapy.method.aggregative.AggregativeQuantifier method)</a> <li><a href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.aggregate">(quapy.method.aggregative.AggregativeQuantifier method)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.aggregative.CC.aggregate">(quapy.method.aggregative.CC method)</a> <li><a href="quapy.method.html#quapy.method.aggregative.CC.aggregate">(quapy.method.aggregative.CC method)</a>
</li>
<li><a href="quapy.method.html#quapy.method.aggregative.DistributionMatching.aggregate">(quapy.method.aggregative.DistributionMatching method)</a>
</li>
<li><a href="quapy.method.html#quapy.method.aggregative.DyS.aggregate">(quapy.method.aggregative.DyS method)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.aggregative.ELM.aggregate">(quapy.method.aggregative.ELM method)</a> <li><a href="quapy.method.html#quapy.method.aggregative.ELM.aggregate">(quapy.method.aggregative.ELM method)</a>
</li> </li>
@ -107,35 +118,21 @@
<li><a href="quapy.method.html#quapy.method.aggregative.PACC.aggregate">(quapy.method.aggregative.PACC method)</a> <li><a href="quapy.method.html#quapy.method.aggregative.PACC.aggregate">(quapy.method.aggregative.PACC method)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.aggregative.PCC.aggregate">(quapy.method.aggregative.PCC method)</a> <li><a href="quapy.method.html#quapy.method.aggregative.PCC.aggregate">(quapy.method.aggregative.PCC method)</a>
</li>
<li><a href="quapy.method.html#quapy.method.aggregative.SMM.aggregate">(quapy.method.aggregative.SMM method)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.aggregative.ThresholdOptimization.aggregate">(quapy.method.aggregative.ThresholdOptimization method)</a> <li><a href="quapy.method.html#quapy.method.aggregative.ThresholdOptimization.aggregate">(quapy.method.aggregative.ThresholdOptimization method)</a>
</li> </li>
</ul></li> </ul></li>
</ul></td> </ul></td>
<td style="width: 33%; vertical-align: top;"><ul> <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.aggregative">aggregative (quapy.method.aggregative.AggregativeQuantifier property)</a> <li><a href="quapy.method.html#quapy.method.meta.Ensemble.aggregative">aggregative (quapy.method.meta.Ensemble property)</a>
<ul>
<li><a href="quapy.method.html#quapy.method.base.BaseQuantifier.aggregative">(quapy.method.base.BaseQuantifier property)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.meta.Ensemble.aggregative">(quapy.method.meta.Ensemble property)</a>
</li>
</ul></li>
<li><a href="quapy.method.html#quapy.method.aggregative.AggregativeProbabilisticQuantifier">AggregativeProbabilisticQuantifier (class in quapy.method.aggregative)</a> <li><a href="quapy.method.html#quapy.method.aggregative.AggregativeProbabilisticQuantifier">AggregativeProbabilisticQuantifier (class in quapy.method.aggregative)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier">AggregativeQuantifier (class in quapy.method.aggregative)</a> <li><a href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier">AggregativeQuantifier (class in quapy.method.aggregative)</a>
</li> </li>
<li><a href="quapy.html#quapy.evaluation.artificial_prevalence_prediction">artificial_prevalence_prediction() (in module quapy.evaluation)</a> <li><a href="quapy.html#quapy.protocol.APP">APP (class in quapy.protocol)</a>
</li>
<li><a href="quapy.html#quapy.evaluation.artificial_prevalence_protocol">artificial_prevalence_protocol() (in module quapy.evaluation)</a>
</li>
<li><a href="quapy.html#quapy.evaluation.artificial_prevalence_report">artificial_prevalence_report() (in module quapy.evaluation)</a>
</li>
<li><a href="quapy.html#quapy.functional.artificial_prevalence_sampling">artificial_prevalence_sampling() (in module quapy.functional)</a>
</li>
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection.artificial_sampling_generator">artificial_sampling_generator() (quapy.data.base.LabelledCollection method)</a>
</li>
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection.artificial_sampling_index_generator">artificial_sampling_index_generator() (quapy.data.base.LabelledCollection method)</a>
</li> </li>
<li><a href="quapy.classification.html#quapy.classification.neural.TorchDataset.asDataloader">asDataloader() (quapy.classification.neural.TorchDataset method)</a> <li><a href="quapy.classification.html#quapy.classification.neural.TorchDataset.asDataloader">asDataloader() (quapy.classification.neural.TorchDataset method)</a>
</li> </li>
@ -146,6 +143,8 @@
<table style="width: 100%" class="indextable genindextable"><tr> <table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul> <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.method.html#quapy.method.base.BaseQuantifier">BaseQuantifier (class in quapy.method.base)</a> <li><a href="quapy.method.html#quapy.method.base.BaseQuantifier">BaseQuantifier (class in quapy.method.base)</a>
</li>
<li><a href="quapy.classification.html#quapy.classification.calibration.BCTSCalibration">BCTSCalibration (class in quapy.classification.calibration)</a>
</li> </li>
<li><a href="quapy.html#quapy.model_selection.GridSearchQ.best_model">best_model() (quapy.model_selection.GridSearchQ method)</a> <li><a href="quapy.html#quapy.model_selection.GridSearchQ.best_model">best_model() (quapy.model_selection.GridSearchQ method)</a>
</li> </li>
@ -155,14 +154,6 @@
<ul> <ul>
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection.binary">(quapy.data.base.LabelledCollection property)</a> <li><a href="quapy.data.html#quapy.data.base.LabelledCollection.binary">(quapy.data.base.LabelledCollection property)</a>
</li>
<li><a href="quapy.method.html#quapy.method.aggregative.OneVsAll.binary">(quapy.method.aggregative.OneVsAll property)</a>
</li>
<li><a href="quapy.method.html#quapy.method.base.BaseQuantifier.binary">(quapy.method.base.BaseQuantifier property)</a>
</li>
<li><a href="quapy.method.html#quapy.method.base.BinaryQuantifier.binary">(quapy.method.base.BinaryQuantifier property)</a>
</li>
<li><a href="quapy.method.html#quapy.method.meta.Ensemble.binary">(quapy.method.meta.Ensemble property)</a>
</li> </li>
</ul></li> </ul></li>
</ul></td> </ul></td>
@ -185,27 +176,29 @@
<td style="width: 33%; vertical-align: top;"><ul> <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.method.html#quapy.method.aggregative.CC">CC (class in quapy.method.aggregative)</a> <li><a href="quapy.method.html#quapy.method.aggregative.CC">CC (class in quapy.method.aggregative)</a>
</li> </li>
<li><a href="quapy.data.html#quapy.data.base.Dataset.classes_">classes_ (quapy.data.base.Dataset property)</a> <li><a href="quapy.html#quapy.functional.check_prevalence_vector">check_prevalence_vector() (in module quapy.functional)</a>
</li>
<li><a href="quapy.method.html#quapy.method.base.OneVsAllGeneric.classes">classes (quapy.method.base.OneVsAllGeneric property)</a>
</li>
<li><a href="quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.classes_">classes_ (quapy.classification.calibration.RecalibratedProbabilisticClassifierBase property)</a>
<ul> <ul>
<li><a href="quapy.data.html#quapy.data.base.Dataset.classes_">(quapy.data.base.Dataset property)</a>
</li>
<li><a href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.classes_">(quapy.method.aggregative.AggregativeQuantifier property)</a> <li><a href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.classes_">(quapy.method.aggregative.AggregativeQuantifier property)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.aggregative.OneVsAll.classes_">(quapy.method.aggregative.OneVsAll property)</a> <li><a href="quapy.method.html#quapy.method.aggregative.OneVsAll.classes_">(quapy.method.aggregative.OneVsAll property)</a>
</li>
<li><a href="quapy.method.html#quapy.method.base.BaseQuantifier.classes_">(quapy.method.base.BaseQuantifier property)</a>
</li>
<li><a href="quapy.method.html#quapy.method.meta.Ensemble.classes_">(quapy.method.meta.Ensemble property)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.neural.QuaNetTrainer.classes_">(quapy.method.neural.QuaNetTrainer property)</a> <li><a href="quapy.method.html#quapy.method.neural.QuaNetTrainer.classes_">(quapy.method.neural.QuaNetTrainer property)</a>
</li>
<li><a href="quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.classes_">(quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation property)</a>
</li>
<li><a href="quapy.html#quapy.model_selection.GridSearchQ.classes_">(quapy.model_selection.GridSearchQ property)</a>
</li> </li>
</ul></li> </ul></li>
<li><a href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.classifier">classifier (quapy.method.aggregative.AggregativeQuantifier property)</a>
</li>
<li><a href="quapy.method.html#quapy.method.aggregative.ACC.classify">classify() (quapy.method.aggregative.ACC method)</a> <li><a href="quapy.method.html#quapy.method.aggregative.ACC.classify">classify() (quapy.method.aggregative.ACC method)</a>
<ul> <ul>
<li><a href="quapy.method.html#quapy.method.aggregative.AggregativeProbabilisticQuantifier.classify">(quapy.method.aggregative.AggregativeProbabilisticQuantifier method)</a>
</li>
<li><a href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.classify">(quapy.method.aggregative.AggregativeQuantifier method)</a> <li><a href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.classify">(quapy.method.aggregative.AggregativeQuantifier method)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.aggregative.ELM.classify">(quapy.method.aggregative.ELM method)</a> <li><a href="quapy.method.html#quapy.method.aggregative.ELM.classify">(quapy.method.aggregative.ELM method)</a>
@ -224,12 +217,20 @@
<li><a href="quapy.method.html#quapy.method.neural.QuaNetTrainer.clean_checkpoint_dir">clean_checkpoint_dir() (quapy.method.neural.QuaNetTrainer method)</a> <li><a href="quapy.method.html#quapy.method.neural.QuaNetTrainer.clean_checkpoint_dir">clean_checkpoint_dir() (quapy.method.neural.QuaNetTrainer method)</a>
</li> </li>
<li><a href="quapy.classification.html#quapy.classification.neural.CNNnet">CNNnet (class in quapy.classification.neural)</a> <li><a href="quapy.classification.html#quapy.classification.neural.CNNnet">CNNnet (class in quapy.classification.neural)</a>
</li>
<li><a href="quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.collator">collator() (quapy.protocol.AbstractStochasticSeededProtocol method)</a>
</li> </li>
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection.counts">counts() (quapy.data.base.LabelledCollection method)</a> <li><a href="quapy.data.html#quapy.data.base.LabelledCollection.counts">counts() (quapy.data.base.LabelledCollection method)</a>
</li> </li>
<li><a href="quapy.html#quapy.util.create_if_not_exist">create_if_not_exist() (in module quapy.util)</a> <li><a href="quapy.html#quapy.util.create_if_not_exist">create_if_not_exist() (in module quapy.util)</a>
</li> </li>
<li><a href="quapy.html#quapy.util.create_parent_dir">create_parent_dir() (in module quapy.util)</a> <li><a href="quapy.html#quapy.util.create_parent_dir">create_parent_dir() (in module quapy.util)</a>
</li>
<li><a href="quapy.method.html#quapy.method.aggregative.cross_generate_predictions">cross_generate_predictions() (in module quapy.method.aggregative)</a>
</li>
<li><a href="quapy.method.html#quapy.method.aggregative.cross_generate_predictions_depr">cross_generate_predictions_depr() (in module quapy.method.aggregative)</a>
</li>
<li><a href="quapy.html#quapy.model_selection.cross_val_predict">cross_val_predict() (in module quapy.model_selection)</a>
</li> </li>
</ul></td> </ul></td>
</tr></table> </tr></table>
@ -248,6 +249,8 @@
</li> </li>
</ul></li> </ul></li>
<li><a href="quapy.classification.html#quapy.classification.neural.TextClassifierNet.dimensions">dimensions() (quapy.classification.neural.TextClassifierNet method)</a> <li><a href="quapy.classification.html#quapy.classification.neural.TextClassifierNet.dimensions">dimensions() (quapy.classification.neural.TextClassifierNet method)</a>
</li>
<li><a href="quapy.method.html#quapy.method.aggregative.DistributionMatching">DistributionMatching (class in quapy.method.aggregative)</a>
</li> </li>
</ul></td> </ul></td>
<td style="width: 33%; vertical-align: top;"><ul> <td style="width: 33%; vertical-align: top;"><ul>
@ -259,9 +262,13 @@
<li><a href="quapy.classification.html#quapy.classification.neural.TextClassifierNet.document_embedding">(quapy.classification.neural.TextClassifierNet method)</a> <li><a href="quapy.classification.html#quapy.classification.neural.TextClassifierNet.document_embedding">(quapy.classification.neural.TextClassifierNet method)</a>
</li> </li>
</ul></li> </ul></li>
<li><a href="quapy.html#quapy.protocol.DomainMixer">DomainMixer (class in quapy.protocol)</a>
</li>
<li><a href="quapy.html#quapy.util.download_file">download_file() (in module quapy.util)</a> <li><a href="quapy.html#quapy.util.download_file">download_file() (in module quapy.util)</a>
</li> </li>
<li><a href="quapy.html#quapy.util.download_file_if_not_exists">download_file_if_not_exists() (in module quapy.util)</a> <li><a href="quapy.html#quapy.util.download_file_if_not_exists">download_file_if_not_exists() (in module quapy.util)</a>
</li>
<li><a href="quapy.method.html#quapy.method.aggregative.DyS">DyS (class in quapy.method.aggregative)</a>
</li> </li>
</ul></td> </ul></td>
</tr></table> </tr></table>
@ -298,6 +305,8 @@
<li><a href="quapy.html#quapy.plot.error_by_drift">error_by_drift() (in module quapy.plot)</a> <li><a href="quapy.html#quapy.plot.error_by_drift">error_by_drift() (in module quapy.plot)</a>
</li> </li>
<li><a href="quapy.html#quapy.evaluation.evaluate">evaluate() (in module quapy.evaluation)</a> <li><a href="quapy.html#quapy.evaluation.evaluate">evaluate() (in module quapy.evaluation)</a>
</li>
<li><a href="quapy.html#quapy.evaluation.evaluation_report">evaluation_report() (in module quapy.evaluation)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.aggregative.ExpectationMaximizationQuantifier">ExpectationMaximizationQuantifier (in module quapy.method.aggregative)</a> <li><a href="quapy.method.html#quapy.method.aggregative.ExpectationMaximizationQuantifier">ExpectationMaximizationQuantifier (in module quapy.method.aggregative)</a>
</li> </li>
@ -312,6 +321,8 @@
<li><a href="quapy.html#quapy.error.f1_error">f1_error() (in module quapy.error)</a> <li><a href="quapy.html#quapy.error.f1_error">f1_error() (in module quapy.error)</a>
</li> </li>
<li><a href="quapy.html#quapy.error.f1e">f1e() (in module quapy.error)</a> <li><a href="quapy.html#quapy.error.f1e">f1e() (in module quapy.error)</a>
</li>
<li><a href="quapy.data.html#quapy.data.datasets.fetch_lequa2022">fetch_lequa2022() (in module quapy.data.datasets)</a>
</li> </li>
<li><a href="quapy.data.html#quapy.data.datasets.fetch_reviews">fetch_reviews() (in module quapy.data.datasets)</a> <li><a href="quapy.data.html#quapy.data.datasets.fetch_reviews">fetch_reviews() (in module quapy.data.datasets)</a>
</li> </li>
@ -321,9 +332,11 @@
</li> </li>
<li><a href="quapy.data.html#quapy.data.datasets.fetch_UCILabelledCollection">fetch_UCILabelledCollection() (in module quapy.data.datasets)</a> <li><a href="quapy.data.html#quapy.data.datasets.fetch_UCILabelledCollection">fetch_UCILabelledCollection() (in module quapy.data.datasets)</a>
</li> </li>
<li><a href="quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.fit">fit() (quapy.classification.methods.LowRankLogisticRegression method)</a> <li><a href="quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit">fit() (quapy.classification.calibration.RecalibratedProbabilisticClassifierBase method)</a>
<ul> <ul>
<li><a href="quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.fit">(quapy.classification.methods.LowRankLogisticRegression method)</a>
</li>
<li><a href="quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.fit">(quapy.classification.neural.NeuralClassifierTrainer method)</a> <li><a href="quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.fit">(quapy.classification.neural.NeuralClassifierTrainer method)</a>
</li> </li>
<li><a href="quapy.classification.html#quapy.classification.svmperf.SVMperf.fit">(quapy.classification.svmperf.SVMperf method)</a> <li><a href="quapy.classification.html#quapy.classification.svmperf.SVMperf.fit">(quapy.classification.svmperf.SVMperf method)</a>
@ -335,6 +348,10 @@
<li><a href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.fit">(quapy.method.aggregative.AggregativeQuantifier method)</a> <li><a href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.fit">(quapy.method.aggregative.AggregativeQuantifier method)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.aggregative.CC.fit">(quapy.method.aggregative.CC method)</a> <li><a href="quapy.method.html#quapy.method.aggregative.CC.fit">(quapy.method.aggregative.CC method)</a>
</li>
<li><a href="quapy.method.html#quapy.method.aggregative.DistributionMatching.fit">(quapy.method.aggregative.DistributionMatching method)</a>
</li>
<li><a href="quapy.method.html#quapy.method.aggregative.DyS.fit">(quapy.method.aggregative.DyS method)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.aggregative.ELM.fit">(quapy.method.aggregative.ELM method)</a> <li><a href="quapy.method.html#quapy.method.aggregative.ELM.fit">(quapy.method.aggregative.ELM method)</a>
</li> </li>
@ -347,10 +364,14 @@
<li><a href="quapy.method.html#quapy.method.aggregative.PACC.fit">(quapy.method.aggregative.PACC method)</a> <li><a href="quapy.method.html#quapy.method.aggregative.PACC.fit">(quapy.method.aggregative.PACC method)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.aggregative.PCC.fit">(quapy.method.aggregative.PCC method)</a> <li><a href="quapy.method.html#quapy.method.aggregative.PCC.fit">(quapy.method.aggregative.PCC method)</a>
</li>
<li><a href="quapy.method.html#quapy.method.aggregative.SMM.fit">(quapy.method.aggregative.SMM method)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.aggregative.ThresholdOptimization.fit">(quapy.method.aggregative.ThresholdOptimization method)</a> <li><a href="quapy.method.html#quapy.method.aggregative.ThresholdOptimization.fit">(quapy.method.aggregative.ThresholdOptimization method)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.base.BaseQuantifier.fit">(quapy.method.base.BaseQuantifier method)</a> <li><a href="quapy.method.html#quapy.method.base.BaseQuantifier.fit">(quapy.method.base.BaseQuantifier method)</a>
</li>
<li><a href="quapy.method.html#quapy.method.base.OneVsAllGeneric.fit">(quapy.method.base.OneVsAllGeneric method)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.meta.Ensemble.fit">(quapy.method.meta.Ensemble method)</a> <li><a href="quapy.method.html#quapy.method.meta.Ensemble.fit">(quapy.method.meta.Ensemble method)</a>
</li> </li>
@ -363,6 +384,10 @@
</ul></li> </ul></li>
</ul></td> </ul></td>
<td style="width: 33%; vertical-align: top;"><ul> <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_cv">fit_cv() (quapy.classification.calibration.RecalibratedProbabilisticClassifierBase method)</a>
</li>
<li><a href="quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_tr_val">fit_tr_val() (quapy.classification.calibration.RecalibratedProbabilisticClassifierBase method)</a>
</li>
<li><a href="quapy.data.html#quapy.data.preprocessing.IndexTransformer.fit_transform">fit_transform() (quapy.data.preprocessing.IndexTransformer method)</a> <li><a href="quapy.data.html#quapy.data.preprocessing.IndexTransformer.fit_transform">fit_transform() (quapy.data.preprocessing.IndexTransformer method)</a>
</li> </li>
<li><a href="quapy.classification.html#quapy.classification.neural.TextClassifierNet.forward">forward() (quapy.classification.neural.TextClassifierNet method)</a> <li><a href="quapy.classification.html#quapy.classification.neural.TextClassifierNet.forward">forward() (quapy.classification.neural.TextClassifierNet method)</a>
@ -385,9 +410,9 @@
<h2 id="G">G</h2> <h2 id="G">G</h2>
<table style="width: 100%" class="indextable genindextable"><tr> <table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul> <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.html#quapy.evaluation.gen_prevalence_prediction">gen_prevalence_prediction() (in module quapy.evaluation)</a> <li><a href="quapy.html#quapy.protocol.OnLabelledCollectionProtocol.get_collator">get_collator() (quapy.protocol.OnLabelledCollectionProtocol class method)</a>
</li> </li>
<li><a href="quapy.html#quapy.evaluation.gen_prevalence_report">gen_prevalence_report() (in module quapy.evaluation)</a> <li><a href="quapy.html#quapy.protocol.OnLabelledCollectionProtocol.get_labelled_collection">get_labelled_collection() (quapy.protocol.OnLabelledCollectionProtocol method)</a>
</li> </li>
<li><a href="quapy.html#quapy.functional.get_nprevpoints_approximation">get_nprevpoints_approximation() (in module quapy.functional)</a> <li><a href="quapy.html#quapy.functional.get_nprevpoints_approximation">get_nprevpoints_approximation() (in module quapy.functional)</a>
</li> </li>
@ -401,18 +426,14 @@
<li><a href="quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.get_params">(quapy.classification.neural.NeuralClassifierTrainer method)</a> <li><a href="quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.get_params">(quapy.classification.neural.NeuralClassifierTrainer method)</a>
</li> </li>
<li><a href="quapy.classification.html#quapy.classification.neural.TextClassifierNet.get_params">(quapy.classification.neural.TextClassifierNet method)</a> <li><a href="quapy.classification.html#quapy.classification.neural.TextClassifierNet.get_params">(quapy.classification.neural.TextClassifierNet method)</a>
</li>
<li><a href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.get_params">(quapy.method.aggregative.AggregativeQuantifier method)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.aggregative.OneVsAll.get_params">(quapy.method.aggregative.OneVsAll method)</a> <li><a href="quapy.method.html#quapy.method.aggregative.OneVsAll.get_params">(quapy.method.aggregative.OneVsAll method)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.base.BaseQuantifier.get_params">(quapy.method.base.BaseQuantifier method)</a> <li><a href="quapy.method.html#quapy.method.base.OneVsAllGeneric.get_params">(quapy.method.base.OneVsAllGeneric method)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.meta.Ensemble.get_params">(quapy.method.meta.Ensemble method)</a> <li><a href="quapy.method.html#quapy.method.meta.Ensemble.get_params">(quapy.method.meta.Ensemble method)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.neural.QuaNetTrainer.get_params">(quapy.method.neural.QuaNetTrainer method)</a> <li><a href="quapy.method.html#quapy.method.neural.QuaNetTrainer.get_params">(quapy.method.neural.QuaNetTrainer method)</a>
</li>
<li><a href="quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.get_params">(quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation method)</a>
</li> </li>
<li><a href="quapy.html#quapy.model_selection.GridSearchQ.get_params">(quapy.model_selection.GridSearchQ method)</a> <li><a href="quapy.html#quapy.model_selection.GridSearchQ.get_params">(quapy.model_selection.GridSearchQ method)</a>
</li> </li>
@ -423,6 +444,12 @@
</li> </li>
<li><a href="quapy.html#quapy.util.get_quapy_home">get_quapy_home() (in module quapy.util)</a> <li><a href="quapy.html#quapy.util.get_quapy_home">get_quapy_home() (in module quapy.util)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.aggregative.ACC.getPteCondEstim">getPteCondEstim() (quapy.method.aggregative.ACC class method)</a>
<ul>
<li><a href="quapy.method.html#quapy.method.aggregative.PACC.getPteCondEstim">(quapy.method.aggregative.PACC class method)</a>
</li>
</ul></li>
<li><a href="quapy.html#quapy.model_selection.GridSearchQ">GridSearchQ (class in quapy.model_selection)</a> <li><a href="quapy.html#quapy.model_selection.GridSearchQ">GridSearchQ (class in quapy.model_selection)</a>
</li> </li>
</ul></td> </ul></td>
@ -446,22 +473,10 @@
<table style="width: 100%" class="indextable genindextable"><tr> <table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul> <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.data.html#quapy.data.preprocessing.index">index() (in module quapy.data.preprocessing)</a> <li><a href="quapy.data.html#quapy.data.preprocessing.index">index() (in module quapy.data.preprocessing)</a>
</li>
<li><a href="quapy.data.html#quapy.data.preprocessing.IndexTransformer">IndexTransformer (class in quapy.data.preprocessing)</a>
</li>
<li><a href="quapy.method.html#quapy.method.base.isaggregative">isaggregative() (in module quapy.method.base)</a>
</li> </li>
</ul></td> </ul></td>
<td style="width: 33%; vertical-align: top;"><ul> <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.html#quapy.isbinary">isbinary() (in module quapy)</a> <li><a href="quapy.data.html#quapy.data.preprocessing.IndexTransformer">IndexTransformer (class in quapy.data.preprocessing)</a>
<ul>
<li><a href="quapy.data.html#quapy.data.base.isbinary">(in module quapy.data.base)</a>
</li>
<li><a href="quapy.method.html#quapy.method.base.isbinary">(in module quapy.method.base)</a>
</li>
</ul></li>
<li><a href="quapy.method.html#quapy.method.base.isprobabilistic">isprobabilistic() (in module quapy.method.base)</a>
</li> </li>
</ul></td> </ul></td>
</tr></table> </tr></table>
@ -486,8 +501,6 @@
<table style="width: 100%" class="indextable genindextable"><tr> <table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul> <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection">LabelledCollection (class in quapy.data.base)</a> <li><a href="quapy.data.html#quapy.data.base.LabelledCollection">LabelledCollection (class in quapy.data.base)</a>
</li>
<li><a href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.learner">learner (quapy.method.aggregative.AggregativeQuantifier property)</a>
</li> </li>
<li><a href="quapy.data.html#quapy.data.base.Dataset.load">load() (quapy.data.base.Dataset class method)</a> <li><a href="quapy.data.html#quapy.data.base.Dataset.load">load() (quapy.data.base.Dataset class method)</a>
@ -538,6 +551,8 @@
<li><a href="quapy.html#module-quapy">quapy</a> <li><a href="quapy.html#module-quapy">quapy</a>
</li> </li>
<li><a href="quapy.classification.html#module-quapy.classification">quapy.classification</a> <li><a href="quapy.classification.html#module-quapy.classification">quapy.classification</a>
</li>
<li><a href="quapy.classification.html#module-quapy.classification.calibration">quapy.classification.calibration</a>
</li> </li>
<li><a href="quapy.classification.html#module-quapy.classification.methods">quapy.classification.methods</a> <li><a href="quapy.classification.html#module-quapy.classification.methods">quapy.classification.methods</a>
</li> </li>
@ -576,6 +591,8 @@
<li><a href="quapy.html#module-quapy.model_selection">quapy.model_selection</a> <li><a href="quapy.html#module-quapy.model_selection">quapy.model_selection</a>
</li> </li>
<li><a href="quapy.html#module-quapy.plot">quapy.plot</a> <li><a href="quapy.html#module-quapy.plot">quapy.plot</a>
</li>
<li><a href="quapy.html#module-quapy.protocol">quapy.protocol</a>
</li> </li>
<li><a href="quapy.html#module-quapy.util">quapy.util</a> <li><a href="quapy.html#module-quapy.util">quapy.util</a>
</li> </li>
@ -600,27 +617,19 @@
<ul> <ul>
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection.n_classes">(quapy.data.base.LabelledCollection property)</a> <li><a href="quapy.data.html#quapy.data.base.LabelledCollection.n_classes">(quapy.data.base.LabelledCollection property)</a>
</li>
<li><a href="quapy.method.html#quapy.method.base.BaseQuantifier.n_classes">(quapy.method.base.BaseQuantifier property)</a>
</li> </li>
</ul></li> </ul></li>
<li><a href="quapy.html#quapy.evaluation.natural_prevalence_prediction">natural_prevalence_prediction() (in module quapy.evaluation)</a> <li><a href="quapy.classification.html#quapy.classification.calibration.NBVSCalibration">NBVSCalibration (class in quapy.classification.calibration)</a>
</li>
<li><a href="quapy.html#quapy.evaluation.natural_prevalence_protocol">natural_prevalence_protocol() (in module quapy.evaluation)</a>
</li>
<li><a href="quapy.html#quapy.evaluation.natural_prevalence_report">natural_prevalence_report() (in module quapy.evaluation)</a>
</li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection.natural_sampling_generator">natural_sampling_generator() (quapy.data.base.LabelledCollection method)</a>
</li>
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection.natural_sampling_index_generator">natural_sampling_index_generator() (quapy.data.base.LabelledCollection method)</a>
</li> </li>
<li><a href="quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer">NeuralClassifierTrainer (class in quapy.classification.neural)</a> <li><a href="quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer">NeuralClassifierTrainer (class in quapy.classification.neural)</a>
</li> </li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.html#quapy.error.nkld">nkld() (in module quapy.error)</a> <li><a href="quapy.html#quapy.error.nkld">nkld() (in module quapy.error)</a>
</li> </li>
<li><a href="quapy.html#quapy.functional.normalize_prevalence">normalize_prevalence() (in module quapy.functional)</a> <li><a href="quapy.html#quapy.functional.normalize_prevalence">normalize_prevalence() (in module quapy.functional)</a>
</li>
<li><a href="quapy.html#quapy.protocol.NPP">NPP (class in quapy.protocol)</a>
</li> </li>
<li><a href="quapy.html#quapy.functional.num_prevalence_combinations">num_prevalence_combinations() (in module quapy.functional)</a> <li><a href="quapy.html#quapy.functional.num_prevalence_combinations">num_prevalence_combinations() (in module quapy.functional)</a>
</li> </li>
@ -630,7 +639,15 @@
<h2 id="O">O</h2> <h2 id="O">O</h2>
<table style="width: 100%" class="indextable genindextable"><tr> <table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul> <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.html#quapy.protocol.OnLabelledCollectionProtocol.on_preclassified_instances">on_preclassified_instances() (quapy.protocol.OnLabelledCollectionProtocol method)</a>
</li>
<li><a href="quapy.method.html#quapy.method.aggregative.OneVsAll">OneVsAll (class in quapy.method.aggregative)</a> <li><a href="quapy.method.html#quapy.method.aggregative.OneVsAll">OneVsAll (class in quapy.method.aggregative)</a>
</li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.method.html#quapy.method.base.OneVsAllGeneric">OneVsAllGeneric (class in quapy.method.base)</a>
</li>
<li><a href="quapy.html#quapy.protocol.OnLabelledCollectionProtocol">OnLabelledCollectionProtocol (class in quapy.protocol)</a>
</li> </li>
</ul></td> </ul></td>
</tr></table> </tr></table>
@ -638,6 +655,8 @@
<h2 id="P">P</h2> <h2 id="P">P</h2>
<table style="width: 100%" class="indextable genindextable"><tr> <table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul> <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection.p">p (quapy.data.base.LabelledCollection property)</a>
</li>
<li><a href="quapy.method.html#quapy.method.aggregative.PACC">PACC (class in quapy.method.aggregative)</a> <li><a href="quapy.method.html#quapy.method.aggregative.PACC">PACC (class in quapy.method.aggregative)</a>
</li> </li>
<li><a href="quapy.html#quapy.util.parallel">parallel() (in module quapy.util)</a> <li><a href="quapy.html#quapy.util.parallel">parallel() (in module quapy.util)</a>
@ -646,52 +665,44 @@
</li> </li>
<li><a href="quapy.html#quapy.util.pickled_resource">pickled_resource() (in module quapy.util)</a> <li><a href="quapy.html#quapy.util.pickled_resource">pickled_resource() (in module quapy.util)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.aggregative.AggregativeProbabilisticQuantifier.posterior_probabilities">posterior_probabilities() (quapy.method.aggregative.AggregativeProbabilisticQuantifier method)</a> <li><a href="quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.predict">predict() (quapy.classification.calibration.RecalibratedProbabilisticClassifierBase method)</a>
<ul> <ul>
<li><a href="quapy.method.html#quapy.method.aggregative.OneVsAll.posterior_probabilities">(quapy.method.aggregative.OneVsAll method)</a> <li><a href="quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.predict">(quapy.classification.methods.LowRankLogisticRegression method)</a>
</li> </li>
</ul></li>
<li><a href="quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.predict">predict() (quapy.classification.methods.LowRankLogisticRegression method)</a>
<ul>
<li><a href="quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.predict">(quapy.classification.neural.NeuralClassifierTrainer method)</a> <li><a href="quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.predict">(quapy.classification.neural.NeuralClassifierTrainer method)</a>
</li> </li>
<li><a href="quapy.classification.html#quapy.classification.svmperf.SVMperf.predict">(quapy.classification.svmperf.SVMperf method)</a> <li><a href="quapy.classification.html#quapy.classification.svmperf.SVMperf.predict">(quapy.classification.svmperf.SVMperf method)</a>
</li> </li>
</ul></li> </ul></li>
<li><a href="quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.predict_proba">predict_proba() (quapy.classification.methods.LowRankLogisticRegression method)</a> <li><a href="quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.predict_proba">predict_proba() (quapy.classification.calibration.RecalibratedProbabilisticClassifierBase method)</a>
<ul> <ul>
<li><a href="quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.predict_proba">(quapy.classification.methods.LowRankLogisticRegression method)</a>
</li>
<li><a href="quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.predict_proba">(quapy.classification.neural.NeuralClassifierTrainer method)</a> <li><a href="quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.predict_proba">(quapy.classification.neural.NeuralClassifierTrainer method)</a>
</li> </li>
<li><a href="quapy.classification.html#quapy.classification.neural.TextClassifierNet.predict_proba">(quapy.classification.neural.TextClassifierNet method)</a> <li><a href="quapy.classification.html#quapy.classification.neural.TextClassifierNet.predict_proba">(quapy.classification.neural.TextClassifierNet method)</a>
</li>
<li><a href="quapy.method.html#quapy.method.aggregative.AggregativeProbabilisticQuantifier.predict_proba">(quapy.method.aggregative.AggregativeProbabilisticQuantifier method)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.aggregative.EMQ.predict_proba">(quapy.method.aggregative.EMQ method)</a> <li><a href="quapy.method.html#quapy.method.aggregative.EMQ.predict_proba">(quapy.method.aggregative.EMQ method)</a>
</li> </li>
</ul></li> </ul></li>
</ul></td> </ul></td>
<td style="width: 33%; vertical-align: top;"><ul> <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.html#quapy.evaluation.prediction">prediction() (in module quapy.evaluation)</a>
</li>
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection.prevalence">prevalence() (quapy.data.base.LabelledCollection method)</a> <li><a href="quapy.data.html#quapy.data.base.LabelledCollection.prevalence">prevalence() (quapy.data.base.LabelledCollection method)</a>
</li> </li>
<li><a href="quapy.html#quapy.functional.prevalence_from_labels">prevalence_from_labels() (in module quapy.functional)</a> <li><a href="quapy.html#quapy.functional.prevalence_from_labels">prevalence_from_labels() (in module quapy.functional)</a>
</li> </li>
<li><a href="quapy.html#quapy.functional.prevalence_from_probabilities">prevalence_from_probabilities() (in module quapy.functional)</a> <li><a href="quapy.html#quapy.functional.prevalence_from_probabilities">prevalence_from_probabilities() (in module quapy.functional)</a>
</li>
<li><a href="quapy.html#quapy.protocol.APP.prevalence_grid">prevalence_grid() (quapy.protocol.APP method)</a>
</li> </li>
<li><a href="quapy.html#quapy.functional.prevalence_linspace">prevalence_linspace() (in module quapy.functional)</a> <li><a href="quapy.html#quapy.functional.prevalence_linspace">prevalence_linspace() (in module quapy.functional)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.aggregative.AggregativeProbabilisticQuantifier.probabilistic">probabilistic (quapy.method.aggregative.AggregativeProbabilisticQuantifier property)</a> <li><a href="quapy.method.html#quapy.method.meta.Ensemble.probabilistic">probabilistic (quapy.method.meta.Ensemble property)</a>
<ul>
<li><a href="quapy.method.html#quapy.method.aggregative.OneVsAll.probabilistic">(quapy.method.aggregative.OneVsAll property)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.base.BaseQuantifier.probabilistic">(quapy.method.base.BaseQuantifier property)</a>
</li>
<li><a href="quapy.method.html#quapy.method.meta.Ensemble.probabilistic">(quapy.method.meta.Ensemble property)</a>
</li>
</ul></li>
<li><a href="quapy.method.html#quapy.method.aggregative.ProbabilisticAdjustedClassifyAndCount">ProbabilisticAdjustedClassifyAndCount (in module quapy.method.aggregative)</a> <li><a href="quapy.method.html#quapy.method.aggregative.ProbabilisticAdjustedClassifyAndCount">ProbabilisticAdjustedClassifyAndCount (in module quapy.method.aggregative)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.aggregative.ProbabilisticClassifyAndCount">ProbabilisticClassifyAndCount (in module quapy.method.aggregative)</a> <li><a href="quapy.method.html#quapy.method.aggregative.ProbabilisticClassifyAndCount">ProbabilisticClassifyAndCount (in module quapy.method.aggregative)</a>
@ -706,14 +717,12 @@
</li> </li>
<li><a href="quapy.method.html#quapy.method.neural.QuaNetTrainer">QuaNetTrainer (class in quapy.method.neural)</a> <li><a href="quapy.method.html#quapy.method.neural.QuaNetTrainer">QuaNetTrainer (class in quapy.method.neural)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.aggregative.AggregativeProbabilisticQuantifier.quantify">quantify() (quapy.method.aggregative.AggregativeProbabilisticQuantifier method)</a> <li><a href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.quantify">quantify() (quapy.method.aggregative.AggregativeQuantifier method)</a>
<ul> <ul>
<li><a href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.quantify">(quapy.method.aggregative.AggregativeQuantifier method)</a>
</li>
<li><a href="quapy.method.html#quapy.method.aggregative.OneVsAll.quantify">(quapy.method.aggregative.OneVsAll method)</a>
</li>
<li><a href="quapy.method.html#quapy.method.base.BaseQuantifier.quantify">(quapy.method.base.BaseQuantifier method)</a> <li><a href="quapy.method.html#quapy.method.base.BaseQuantifier.quantify">(quapy.method.base.BaseQuantifier method)</a>
</li>
<li><a href="quapy.method.html#quapy.method.base.OneVsAllGeneric.quantify">(quapy.method.base.OneVsAllGeneric method)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.meta.Ensemble.quantify">(quapy.method.meta.Ensemble method)</a> <li><a href="quapy.method.html#quapy.method.meta.Ensemble.quantify">(quapy.method.meta.Ensemble method)</a>
</li> </li>
@ -736,6 +745,13 @@
<ul> <ul>
<li><a href="quapy.classification.html#module-quapy.classification">module</a> <li><a href="quapy.classification.html#module-quapy.classification">module</a>
</li>
</ul></li>
<li>
quapy.classification.calibration
<ul>
<li><a href="quapy.classification.html#module-quapy.classification.calibration">module</a>
</li> </li>
</ul></li> </ul></li>
<li> <li>
@ -871,6 +887,13 @@
<ul> <ul>
<li><a href="quapy.html#module-quapy.plot">module</a> <li><a href="quapy.html#module-quapy.plot">module</a>
</li>
</ul></li>
<li>
quapy.protocol
<ul>
<li><a href="quapy.html#module-quapy.protocol">module</a>
</li> </li>
</ul></li> </ul></li>
<li> <li>
@ -888,15 +911,23 @@
<td style="width: 33%; vertical-align: top;"><ul> <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.html#quapy.error.rae">rae() (in module quapy.error)</a> <li><a href="quapy.html#quapy.error.rae">rae() (in module quapy.error)</a>
</li> </li>
<li><a href="quapy.data.html#quapy.data.preprocessing.reduce_columns">reduce_columns() (in module quapy.data.preprocessing)</a> <li><a href="quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.random_state">random_state (quapy.protocol.AbstractStochasticSeededProtocol property)</a>
</li>
<li><a href="quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifier">RecalibratedProbabilisticClassifier (class in quapy.classification.calibration)</a>
</li>
<li><a href="quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase">RecalibratedProbabilisticClassifierBase (class in quapy.classification.calibration)</a>
</li> </li>
</ul></td> </ul></td>
<td style="width: 33%; vertical-align: top;"><ul> <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.data.html#quapy.data.preprocessing.reduce_columns">reduce_columns() (in module quapy.data.preprocessing)</a>
</li>
<li><a href="quapy.data.html#quapy.data.reader.reindex_labels">reindex_labels() (in module quapy.data.reader)</a> <li><a href="quapy.data.html#quapy.data.reader.reindex_labels">reindex_labels() (in module quapy.data.reader)</a>
</li> </li>
<li><a href="quapy.html#quapy.error.relative_absolute_error">relative_absolute_error() (in module quapy.error)</a> <li><a href="quapy.html#quapy.error.relative_absolute_error">relative_absolute_error() (in module quapy.error)</a>
</li> </li>
<li><a href="quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.reset_net_params">reset_net_params() (quapy.classification.neural.NeuralClassifierTrainer method)</a> <li><a href="quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.reset_net_params">reset_net_params() (quapy.classification.neural.NeuralClassifierTrainer method)</a>
</li>
<li><a href="quapy.html#quapy.protocol.OnLabelledCollectionProtocol.RETURN_TYPES">RETURN_TYPES (quapy.protocol.OnLabelledCollectionProtocol attribute)</a>
</li> </li>
</ul></td> </ul></td>
</tr></table> </tr></table>
@ -904,6 +935,30 @@
<h2 id="S">S</h2> <h2 id="S">S</h2>
<table style="width: 100%" class="indextable genindextable"><tr> <table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul> <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.sample">sample() (quapy.protocol.AbstractStochasticSeededProtocol method)</a>
<ul>
<li><a href="quapy.html#quapy.protocol.APP.sample">(quapy.protocol.APP method)</a>
</li>
<li><a href="quapy.html#quapy.protocol.DomainMixer.sample">(quapy.protocol.DomainMixer method)</a>
</li>
<li><a href="quapy.html#quapy.protocol.NPP.sample">(quapy.protocol.NPP method)</a>
</li>
<li><a href="quapy.html#quapy.protocol.USimplexPP.sample">(quapy.protocol.USimplexPP method)</a>
</li>
</ul></li>
<li><a href="quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.samples_parameters">samples_parameters() (quapy.protocol.AbstractStochasticSeededProtocol method)</a>
<ul>
<li><a href="quapy.html#quapy.protocol.APP.samples_parameters">(quapy.protocol.APP method)</a>
</li>
<li><a href="quapy.html#quapy.protocol.DomainMixer.samples_parameters">(quapy.protocol.DomainMixer method)</a>
</li>
<li><a href="quapy.html#quapy.protocol.NPP.samples_parameters">(quapy.protocol.NPP method)</a>
</li>
<li><a href="quapy.html#quapy.protocol.USimplexPP.samples_parameters">(quapy.protocol.USimplexPP method)</a>
</li>
</ul></li>
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection.sampling">sampling() (quapy.data.base.LabelledCollection method)</a> <li><a href="quapy.data.html#quapy.data.base.LabelledCollection.sampling">sampling() (quapy.data.base.LabelledCollection method)</a>
</li> </li>
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection.sampling_from_index">sampling_from_index() (quapy.data.base.LabelledCollection method)</a> <li><a href="quapy.data.html#quapy.data.base.LabelledCollection.sampling_from_index">sampling_from_index() (quapy.data.base.LabelledCollection method)</a>
@ -920,20 +975,14 @@
<li><a href="quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.set_params">(quapy.classification.neural.NeuralClassifierTrainer method)</a> <li><a href="quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.set_params">(quapy.classification.neural.NeuralClassifierTrainer method)</a>
</li> </li>
<li><a href="quapy.classification.html#quapy.classification.svmperf.SVMperf.set_params">(quapy.classification.svmperf.SVMperf method)</a> <li><a href="quapy.classification.html#quapy.classification.svmperf.SVMperf.set_params">(quapy.classification.svmperf.SVMperf method)</a>
</li>
<li><a href="quapy.method.html#quapy.method.aggregative.AggregativeProbabilisticQuantifier.set_params">(quapy.method.aggregative.AggregativeProbabilisticQuantifier method)</a>
</li>
<li><a href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.set_params">(quapy.method.aggregative.AggregativeQuantifier method)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.aggregative.OneVsAll.set_params">(quapy.method.aggregative.OneVsAll method)</a> <li><a href="quapy.method.html#quapy.method.aggregative.OneVsAll.set_params">(quapy.method.aggregative.OneVsAll method)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.base.BaseQuantifier.set_params">(quapy.method.base.BaseQuantifier method)</a> <li><a href="quapy.method.html#quapy.method.base.OneVsAllGeneric.set_params">(quapy.method.base.OneVsAllGeneric method)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.meta.Ensemble.set_params">(quapy.method.meta.Ensemble method)</a> <li><a href="quapy.method.html#quapy.method.meta.Ensemble.set_params">(quapy.method.meta.Ensemble method)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.neural.QuaNetTrainer.set_params">(quapy.method.neural.QuaNetTrainer method)</a> <li><a href="quapy.method.html#quapy.method.neural.QuaNetTrainer.set_params">(quapy.method.neural.QuaNetTrainer method)</a>
</li>
<li><a href="quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.set_params">(quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation method)</a>
</li> </li>
<li><a href="quapy.html#quapy.model_selection.GridSearchQ.set_params">(quapy.model_selection.GridSearchQ method)</a> <li><a href="quapy.html#quapy.model_selection.GridSearchQ.set_params">(quapy.model_selection.GridSearchQ method)</a>
</li> </li>
@ -941,10 +990,14 @@
</ul></td> </ul></td>
<td style="width: 33%; vertical-align: top;"><ul> <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.method.html#quapy.method.aggregative.SLD">SLD (in module quapy.method.aggregative)</a> <li><a href="quapy.method.html#quapy.method.aggregative.SLD">SLD (in module quapy.method.aggregative)</a>
</li>
<li><a href="quapy.method.html#quapy.method.aggregative.SMM">SMM (class in quapy.method.aggregative)</a>
</li> </li>
<li><a href="quapy.html#quapy.error.smooth">smooth() (in module quapy.error)</a> <li><a href="quapy.html#quapy.error.smooth">smooth() (in module quapy.error)</a>
</li> </li>
<li><a href="quapy.method.html#quapy.method.aggregative.ACC.solve_adjustment">solve_adjustment() (quapy.method.aggregative.ACC class method)</a> <li><a href="quapy.method.html#quapy.method.aggregative.ACC.solve_adjustment">solve_adjustment() (quapy.method.aggregative.ACC class method)</a>
</li>
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection.split_random">split_random() (quapy.data.base.LabelledCollection method)</a>
</li> </li>
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection.split_stratified">split_stratified() (quapy.data.base.LabelledCollection method)</a> <li><a href="quapy.data.html#quapy.data.base.LabelledCollection.split_stratified">split_stratified() (quapy.data.base.LabelledCollection method)</a>
</li> </li>
@ -986,12 +1039,38 @@
</li> </li>
<li><a href="quapy.classification.html#quapy.classification.neural.TextClassifierNet">TextClassifierNet (class in quapy.classification.neural)</a> <li><a href="quapy.classification.html#quapy.classification.neural.TextClassifierNet">TextClassifierNet (class in quapy.classification.neural)</a>
</li> </li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.method.html#quapy.method.aggregative.ThresholdOptimization">ThresholdOptimization (class in quapy.method.aggregative)</a> <li><a href="quapy.method.html#quapy.method.aggregative.ThresholdOptimization">ThresholdOptimization (class in quapy.method.aggregative)</a>
</li>
<li><a href="quapy.html#quapy.functional.TopsoeDistance">TopsoeDistance() (in module quapy.functional)</a>
</li> </li>
<li><a href="quapy.classification.html#quapy.classification.neural.TorchDataset">TorchDataset (class in quapy.classification.neural)</a> <li><a href="quapy.classification.html#quapy.classification.neural.TorchDataset">TorchDataset (class in quapy.classification.neural)</a>
</li> </li>
<li><a href="quapy.html#quapy.protocol.AbstractProtocol.total">total() (quapy.protocol.AbstractProtocol method)</a>
<ul>
<li><a href="quapy.html#quapy.protocol.APP.total">(quapy.protocol.APP method)</a>
</li>
<li><a href="quapy.html#quapy.protocol.DomainMixer.total">(quapy.protocol.DomainMixer method)</a>
</li>
<li><a href="quapy.html#quapy.protocol.NPP.total">(quapy.protocol.NPP method)</a>
</li>
<li><a href="quapy.html#quapy.protocol.USimplexPP.total">(quapy.protocol.USimplexPP method)</a>
</li>
</ul></li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.data.html#quapy.data.base.Dataset.train_test">train_test (quapy.data.base.Dataset property)</a>
</li>
<li><a href="quapy.classification.html#quapy.classification.neural.CNNnet.training">training (quapy.classification.neural.CNNnet attribute)</a>
<ul>
<li><a href="quapy.classification.html#quapy.classification.neural.LSTMnet.training">(quapy.classification.neural.LSTMnet attribute)</a>
</li>
<li><a href="quapy.classification.html#quapy.classification.neural.TextClassifierNet.training">(quapy.classification.neural.TextClassifierNet attribute)</a>
</li>
<li><a href="quapy.method.html#quapy.method.neural.QuaNetModule.training">(quapy.method.neural.QuaNetModule attribute)</a>
</li>
</ul></li>
<li><a href="quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.transform">transform() (quapy.classification.methods.LowRankLogisticRegression method)</a> <li><a href="quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.transform">transform() (quapy.classification.methods.LowRankLogisticRegression method)</a>
<ul> <ul>
@ -1000,6 +1079,8 @@
<li><a href="quapy.data.html#quapy.data.preprocessing.IndexTransformer.transform">(quapy.data.preprocessing.IndexTransformer method)</a> <li><a href="quapy.data.html#quapy.data.preprocessing.IndexTransformer.transform">(quapy.data.preprocessing.IndexTransformer method)</a>
</li> </li>
</ul></li> </ul></li>
<li><a href="quapy.classification.html#quapy.classification.calibration.TSCalibration">TSCalibration (class in quapy.classification.calibration)</a>
</li>
</ul></td> </ul></td>
</tr></table> </tr></table>
@ -1015,6 +1096,8 @@
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection.uniform_sampling_index">uniform_sampling_index() (quapy.data.base.LabelledCollection method)</a> <li><a href="quapy.data.html#quapy.data.base.LabelledCollection.uniform_sampling_index">uniform_sampling_index() (quapy.data.base.LabelledCollection method)</a>
</li> </li>
<li><a href="quapy.html#quapy.functional.uniform_simplex_sampling">uniform_simplex_sampling() (in module quapy.functional)</a> <li><a href="quapy.html#quapy.functional.uniform_simplex_sampling">uniform_simplex_sampling() (in module quapy.functional)</a>
</li>
<li><a href="quapy.html#quapy.protocol.USimplexPP">USimplexPP (class in quapy.protocol)</a>
</li> </li>
</ul></td> </ul></td>
</tr></table> </tr></table>
@ -1039,6 +1122,8 @@
</ul></td> </ul></td>
<td style="width: 33%; vertical-align: top;"><ul> <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.data.html#quapy.data.preprocessing.IndexTransformer.vocabulary_size">vocabulary_size() (quapy.data.preprocessing.IndexTransformer method)</a> <li><a href="quapy.data.html#quapy.data.preprocessing.IndexTransformer.vocabulary_size">vocabulary_size() (quapy.data.preprocessing.IndexTransformer method)</a>
</li>
<li><a href="quapy.classification.html#quapy.classification.calibration.VSCalibration">VSCalibration (class in quapy.classification.calibration)</a>
</li> </li>
</ul></td> </ul></td>
</tr></table> </tr></table>
@ -1055,16 +1140,30 @@
<table style="width: 100%" class="indextable genindextable"><tr> <table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul> <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.method.html#quapy.method.aggregative.X">X (class in quapy.method.aggregative)</a> <li><a href="quapy.method.html#quapy.method.aggregative.X">X (class in quapy.method.aggregative)</a>
<ul>
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection.X">(quapy.data.base.LabelledCollection property)</a>
</li> </li>
</ul></li>
</ul></td> </ul></td>
<td style="width: 33%; vertical-align: top;"><ul> <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.classification.html#quapy.classification.neural.TextClassifierNet.xavier_uniform">xavier_uniform() (quapy.classification.neural.TextClassifierNet method)</a> <li><a href="quapy.classification.html#quapy.classification.neural.TextClassifierNet.xavier_uniform">xavier_uniform() (quapy.classification.neural.TextClassifierNet method)</a>
</li>
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection.Xp">Xp (quapy.data.base.LabelledCollection property)</a>
</li> </li>
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection.Xy">Xy (quapy.data.base.LabelledCollection property)</a> <li><a href="quapy.data.html#quapy.data.base.LabelledCollection.Xy">Xy (quapy.data.base.LabelledCollection property)</a>
</li> </li>
</ul></td> </ul></td>
</tr></table> </tr></table>
<h2 id="Y">Y</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection.y">y (quapy.data.base.LabelledCollection property)</a>
</li>
</ul></td>
</tr></table>
<div class="clearer"></div> <div class="clearer"></div>
@ -1082,7 +1181,7 @@
</form> </form>
</div> </div>
</div> </div>
<script>$('#searchbox').show(0);</script> <script>document.getElementById('searchbox').style.display = "block"</script>
</div> </div>
</div> </div>
<div class="clearer"></div> <div class="clearer"></div>
@ -1096,13 +1195,13 @@
<li class="right" > <li class="right" >
<a href="py-modindex.html" title="Python Module Index" <a href="py-modindex.html" title="Python Module Index"
>modules</a> |</li> >modules</a> |</li>
<li class="nav-item nav-item-0"><a href="index.html">QuaPy 0.1.6 documentation</a> &#187;</li> <li class="nav-item nav-item-0"><a href="index.html">QuaPy 0.1.7 documentation</a> &#187;</li>
<li class="nav-item nav-item-this"><a href="">Index</a></li> <li class="nav-item nav-item-this"><a href="">Index</a></li>
</ul> </ul>
</div> </div>
<div class="footer" role="contentinfo"> <div class="footer" role="contentinfo">
&#169; Copyright 2021, Alejandro Moreo. &#169; Copyright 2021, Alejandro Moreo.
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</div> </div>
</body> </body>
</html> </html>

35
docs/build/html/index.html vendored
View File

@ -2,19 +2,21 @@
<!doctype html> <!doctype html>
<html> <html lang="en">
<head> <head>
<meta charset="utf-8" /> <meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" /> <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.19: https://docutils.sourceforge.io/" />
<title>Welcome to QuaPy’s documentation! &#8212; QuaPy 0.1.6 documentation</title> <title>Welcome to QuaPy’s documentation! &#8212; QuaPy 0.1.7 documentation</title>
<link rel="stylesheet" type="text/css" href="_static/pygments.css" /> <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
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<link rel="search" title="Search" href="search.html" /> <link rel="search" title="Search" href="search.html" />
@ -36,7 +38,7 @@
<li class="right" > <li class="right" >
<a href="Installation.html" title="Installation" <a href="Installation.html" title="Installation"
accesskey="N">next</a> |</li> accesskey="N">next</a> |</li>
<li class="nav-item nav-item-0"><a href="#">QuaPy 0.1.6 documentation</a> &#187;</li> <li class="nav-item nav-item-0"><a href="#">QuaPy 0.1.7 documentation</a> &#187;</li>
<li class="nav-item nav-item-this"><a href="">Welcome to QuaPy’s documentation!</a></li> <li class="nav-item nav-item-this"><a href="">Welcome to QuaPy’s documentation!</a></li>
</ul> </ul>
</div> </div>
@ -47,11 +49,11 @@
<div class="body" role="main"> <div class="body" role="main">
<section id="welcome-to-quapy-s-documentation"> <section id="welcome-to-quapy-s-documentation">
<h1>Welcome to QuaPy’s documentation!<a class="headerlink" href="#welcome-to-quapy-s-documentation" title="Permalink to this headline"></a></h1> <h1>Welcome to QuaPy’s documentation!<a class="headerlink" href="#welcome-to-quapy-s-documentation" title="Permalink to this heading"></a></h1>
<p>QuaPy is an open source framework for Quantification (a.k.a. Supervised Prevalence Estimation) <p>QuaPy is an open source framework for Quantification (a.k.a. Supervised Prevalence Estimation)
written in Python.</p> written in Python.</p>
<section id="introduction"> <section id="introduction">
<h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline"></a></h2> <h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to this heading"></a></h2>
<p>QuaPy roots on the concept of data sample, and provides implementations of most important concepts <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 in quantification literature, such as the most important quantification baselines, many advanced
quantification methods, quantification-oriented model selection, many evaluation measures and protocols quantification methods, quantification-oriented model selection, many evaluation measures and protocols
@ -59,7 +61,7 @@ used for evaluating quantification methods.
QuaPy also integrates commonly used datasets and offers visualization tools for facilitating the analysis and QuaPy also integrates commonly used datasets and offers visualization tools for facilitating the analysis and
interpretation of results.</p> interpretation of results.</p>
<section id="a-quick-example"> <section id="a-quick-example">
<h3>A quick example:<a class="headerlink" href="#a-quick-example" title="Permalink to this headline"></a></h3> <h3>A quick example:<a class="headerlink" href="#a-quick-example" title="Permalink to this heading"></a></h3>
<p>The following script fetchs a Twitter dataset, trains and evaluates an <p>The following script fetchs a Twitter dataset, trains and evaluates an
<cite>Adjusted Classify &amp; Count</cite> model in terms of the <cite>Mean Absolute Error</cite> (MAE) <cite>Adjusted Classify &amp; 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 between the class prevalences estimated for the test set and the true prevalences
@ -90,7 +92,7 @@ QuaPy implements sampling procedures and evaluation protocols that automates thi
See the <a class="reference internal" href="Evaluation.html"><span class="doc">Evaluation</span></a> for detailed examples.</p> See the <a class="reference internal" href="Evaluation.html"><span class="doc">Evaluation</span></a> for detailed examples.</p>
</section> </section>
<section id="features"> <section id="features">
<h3>Features<a class="headerlink" href="#features" title="Permalink to this headline"></a></h3> <h3>Features<a class="headerlink" href="#features" title="Permalink to this heading"></a></h3>
<ul class="simple"> <ul class="simple">
<li><p>Implementation of most popular quantification methods (Classify-&amp;-Count variants, Expectation-Maximization, SVM-based variants for quantification, HDy, QuaNet, and Ensembles).</p></li> <li><p>Implementation of most popular quantification methods (Classify-&amp;-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>Versatile functionality for performing evaluation based on artificial sampling protocols.</p></li>
@ -133,6 +135,11 @@ See the <a class="reference internal" href="Evaluation.html"><span class="doc">E
<li class="toctree-l2"><a class="reference internal" href="Methods.html#meta-models">Meta Models</a></li> <li class="toctree-l2"><a class="reference internal" href="Methods.html#meta-models">Meta Models</a></li>
</ul> </ul>
</li> </li>
<li class="toctree-l1"><a class="reference internal" href="Model-Selection.html">Model Selection</a><ul>
<li class="toctree-l2"><a class="reference internal" href="Model-Selection.html#targeting-a-quantification-oriented-loss">Targeting a Quantification-oriented loss</a></li>
<li class="toctree-l2"><a class="reference internal" href="Model-Selection.html#targeting-a-classification-oriented-loss">Targeting a Classification-oriented loss</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="Plotting.html">Plotting</a><ul> <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#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#quantification-bias">Quantification bias</a></li>
@ -149,7 +156,7 @@ See the <a class="reference internal" href="Evaluation.html"><span class="doc">E
</section> </section>
</section> </section>
<section id="indices-and-tables"> <section id="indices-and-tables">
<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this headline"></a></h1> <h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this heading"></a></h1>
<ul class="simple"> <ul class="simple">
<li><p><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></p></li> <li><p><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></p></li>
<li><p><a class="reference internal" href="py-modindex.html"><span class="std std-ref">Module Index</span></a></p></li> <li><p><a class="reference internal" href="py-modindex.html"><span class="std std-ref">Module Index</span></a></p></li>
@ -164,6 +171,7 @@ See the <a class="reference internal" href="Evaluation.html"><span class="doc">E
</div> </div>
<div class="sphinxsidebar" role="navigation" aria-label="main navigation"> <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
<div class="sphinxsidebarwrapper"> <div class="sphinxsidebarwrapper">
<div>
<h3><a href="#">Table of Contents</a></h3> <h3><a href="#">Table of Contents</a></h3>
<ul> <ul>
<li><a class="reference internal" href="#">Welcome to QuaPy’s documentation!</a><ul> <li><a class="reference internal" href="#">Welcome to QuaPy’s documentation!</a><ul>
@ -177,9 +185,12 @@ See the <a class="reference internal" href="Evaluation.html"><span class="doc">E
<li><a class="reference internal" href="#indices-and-tables">Indices and tables</a></li> <li><a class="reference internal" href="#indices-and-tables">Indices and tables</a></li>
</ul> </ul>
</div>
<div>
<h4>Next topic</h4> <h4>Next topic</h4>
<p class="topless"><a href="Installation.html" <p class="topless"><a href="Installation.html"
title="next chapter">Installation</a></p> title="next chapter">Installation</a></p>
</div>
<div role="note" aria-label="source link"> <div role="note" aria-label="source link">
<h3>This Page</h3> <h3>This Page</h3>
<ul class="this-page-menu"> <ul class="this-page-menu">
@ -196,7 +207,7 @@ See the <a class="reference internal" href="Evaluation.html"><span class="doc">E
</form> </form>
</div> </div>
</div> </div>
<script>$('#searchbox').show(0);</script> <script>document.getElementById('searchbox').style.display = "block"</script>
</div> </div>
</div> </div>
<div class="clearer"></div> <div class="clearer"></div>
@ -213,13 +224,13 @@ See the <a class="reference internal" href="Evaluation.html"><span class="doc">E
<li class="right" > <li class="right" >
<a href="Installation.html" title="Installation" <a href="Installation.html" title="Installation"
>next</a> |</li> >next</a> |</li>
<li class="nav-item nav-item-0"><a href="#">QuaPy 0.1.6 documentation</a> &#187;</li> <li class="nav-item nav-item-0"><a href="#">QuaPy 0.1.7 documentation</a> &#187;</li>
<li class="nav-item nav-item-this"><a href="">Welcome to QuaPy’s documentation!</a></li> <li class="nav-item nav-item-this"><a href="">Welcome to QuaPy’s documentation!</a></li>
</ul> </ul>
</div> </div>
<div class="footer" role="contentinfo"> <div class="footer" role="contentinfo">
&#169; Copyright 2021, Alejandro Moreo. &#169; Copyright 2021, Alejandro Moreo.
Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.2.0. Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 5.3.0.
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</body> </body>
</html> </html>

66
docs/build/html/modules.html vendored
View File

@ -2,19 +2,21 @@
<!doctype html> <!doctype html>
<html> <html lang="en">
<head> <head>
<meta charset="utf-8" /> <meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" /> <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.19: https://docutils.sourceforge.io/" />
<title>quapy &#8212; QuaPy 0.1.6 documentation</title> <title>quapy &#8212; QuaPy 0.1.7 documentation</title>
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<link rel="stylesheet" type="text/css" href="_static/bizstyle.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 data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
<script src="_static/jquery.js"></script> <script src="_static/jquery.js"></script>
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<link rel="index" title="Index" href="genindex.html" /> <link rel="index" title="Index" href="genindex.html" />
<link rel="search" title="Search" href="search.html" /> <link rel="search" title="Search" href="search.html" />
@ -40,7 +42,7 @@
<li class="right" > <li class="right" >
<a href="Plotting.html" title="Plotting" <a href="Plotting.html" title="Plotting"
accesskey="P">previous</a> |</li> accesskey="P">previous</a> |</li>
<li class="nav-item nav-item-0"><a href="index.html">QuaPy 0.1.6 documentation</a> &#187;</li> <li class="nav-item nav-item-0"><a href="index.html">QuaPy 0.1.7 documentation</a> &#187;</li>
<li class="nav-item nav-item-this"><a href="">quapy</a></li> <li class="nav-item nav-item-this"><a href="">quapy</a></li>
</ul> </ul>
</div> </div>
@ -51,47 +53,19 @@
<div class="body" role="main"> <div class="body" role="main">
<section id="quapy"> <section id="quapy">
<h1>quapy<a class="headerlink" href="#quapy" title="Permalink to this headline"></a></h1> <h1>quapy<a class="headerlink" href="#quapy" title="Permalink to this heading"></a></h1>
<div class="toctree-wrapper compound"> <div class="toctree-wrapper compound">
<ul> <ul>
<li class="toctree-l1"><a class="reference internal" href="quapy.html">quapy package</a><ul> <li class="toctree-l1"><a class="reference internal" href="quapy.html">quapy package</a><ul>
<li class="toctree-l2"><a class="reference internal" href="quapy.html#subpackages">Subpackages</a><ul>
<li class="toctree-l3"><a class="reference internal" href="quapy.classification.html">quapy.classification package</a><ul>
<li class="toctree-l4"><a class="reference internal" href="quapy.classification.html#submodules">Submodules</a></li>
<li class="toctree-l4"><a class="reference internal" href="quapy.classification.html#module-quapy.classification.methods">quapy.classification.methods module</a></li>
<li class="toctree-l4"><a class="reference internal" href="quapy.classification.html#module-quapy.classification.neural">quapy.classification.neural module</a></li>
<li class="toctree-l4"><a class="reference internal" href="quapy.classification.html#module-quapy.classification.svmperf">quapy.classification.svmperf module</a></li>
<li class="toctree-l4"><a class="reference internal" href="quapy.classification.html#module-quapy.classification">Module contents</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="quapy.data.html">quapy.data package</a><ul>
<li class="toctree-l4"><a class="reference internal" href="quapy.data.html#submodules">Submodules</a></li>
<li class="toctree-l4"><a class="reference internal" href="quapy.data.html#module-quapy.data.base">quapy.data.base module</a></li>
<li class="toctree-l4"><a class="reference internal" href="quapy.data.html#module-quapy.data.datasets">quapy.data.datasets module</a></li>
<li class="toctree-l4"><a class="reference internal" href="quapy.data.html#module-quapy.data.preprocessing">quapy.data.preprocessing module</a></li>
<li class="toctree-l4"><a class="reference internal" href="quapy.data.html#module-quapy.data.reader">quapy.data.reader module</a></li>
<li class="toctree-l4"><a class="reference internal" href="quapy.data.html#module-quapy.data">Module contents</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="quapy.method.html">quapy.method package</a><ul>
<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#submodules">Submodules</a></li>
<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#module-quapy.method.aggregative">quapy.method.aggregative module</a></li>
<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#module-quapy.method.base">quapy.method.base module</a></li>
<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#module-quapy.method.meta">quapy.method.meta module</a></li>
<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#module-quapy.method.neural">quapy.method.neural module</a></li>
<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#module-quapy.method.non_aggregative">quapy.method.non_aggregative module</a></li>
<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#module-quapy.method">Module contents</a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="quapy.html#submodules">Submodules</a></li> <li class="toctree-l2"><a class="reference internal" href="quapy.html#submodules">Submodules</a></li>
<li class="toctree-l2"><a class="reference internal" href="quapy.html#module-quapy.error">quapy.error module</a></li> <li class="toctree-l2"><a class="reference internal" href="quapy.html#module-quapy.error">quapy.error</a></li>
<li class="toctree-l2"><a class="reference internal" href="quapy.html#module-quapy.evaluation">quapy.evaluation module</a></li> <li class="toctree-l2"><a class="reference internal" href="quapy.html#module-quapy.evaluation">quapy.evaluation</a></li>
<li class="toctree-l2"><a class="reference internal" href="quapy.html#module-quapy.functional">quapy.functional module</a></li> <li class="toctree-l2"><a class="reference internal" href="quapy.html#quapy-protocol">quapy.protocol</a></li>
<li class="toctree-l2"><a class="reference internal" href="quapy.html#module-quapy.model_selection">quapy.model_selection module</a></li> <li class="toctree-l2"><a class="reference internal" href="quapy.html#module-quapy.functional">quapy.functional</a></li>
<li class="toctree-l2"><a class="reference internal" href="quapy.html#module-quapy.plot">quapy.plot module</a></li> <li class="toctree-l2"><a class="reference internal" href="quapy.html#module-quapy.model_selection">quapy.model_selection</a></li>
<li class="toctree-l2"><a class="reference internal" href="quapy.html#module-quapy.util">quapy.util module</a></li> <li class="toctree-l2"><a class="reference internal" href="quapy.html#module-quapy.plot">quapy.plot</a></li>
<li class="toctree-l2"><a class="reference internal" href="quapy.html#module-quapy.util">quapy.util</a></li>
<li class="toctree-l2"><a class="reference internal" href="quapy.html#subpackages">Subpackages</a></li>
<li class="toctree-l2"><a class="reference internal" href="quapy.html#module-quapy">Module contents</a></li> <li class="toctree-l2"><a class="reference internal" href="quapy.html#module-quapy">Module contents</a></li>
</ul> </ul>
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@ -106,12 +80,16 @@
</div> </div>
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<h4>Previous topic</h4> <h4>Previous topic</h4>
<p class="topless"><a href="Plotting.html" <p class="topless"><a href="Plotting.html"
title="previous chapter">Plotting</a></p> title="previous chapter">Plotting</a></p>
</div>
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<h4>Next topic</h4> <h4>Next topic</h4>
<p class="topless"><a href="quapy.html" <p class="topless"><a href="quapy.html"
title="next chapter">quapy package</a></p> title="next chapter">quapy package</a></p>
</div>
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<h3>This Page</h3> <h3>This Page</h3>
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@ -128,7 +106,7 @@
</form> </form>
</div> </div>
</div> </div>
<script>$('#searchbox').show(0);</script> <script>document.getElementById('searchbox').style.display = "block"</script>
</div> </div>
</div> </div>
<div class="clearer"></div> <div class="clearer"></div>
@ -148,13 +126,13 @@
<li class="right" > <li class="right" >
<a href="Plotting.html" title="Plotting" <a href="Plotting.html" title="Plotting"
>previous</a> |</li> >previous</a> |</li>
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<li class="nav-item nav-item-this"><a href="">quapy</a></li> <li class="nav-item nav-item-this"><a href="">quapy</a></li>
</ul> </ul>
</div> </div>
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&#169; Copyright 2021, Alejandro Moreo. &#169; Copyright 2021, Alejandro Moreo.
Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.2.0. Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 5.3.0.
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24
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@ -2,18 +2,20 @@
<!doctype html> <!doctype html>
<html> <html lang="en">
<head> <head>
<meta charset="utf-8" /> <meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" /> <meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Python Module Index &#8212; QuaPy 0.1.6 documentation</title> <title>Python Module Index &#8212; QuaPy 0.1.7 documentation</title>
<link rel="stylesheet" type="text/css" href="_static/pygments.css" /> <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
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@ -34,7 +36,7 @@
<li class="right" > <li class="right" >
<a href="#" title="Python Module Index" <a href="#" title="Python Module Index"
>modules</a> |</li> >modules</a> |</li>
<li class="nav-item nav-item-0"><a href="index.html">QuaPy 0.1.6 documentation</a> &#187;</li> <li class="nav-item nav-item-0"><a href="index.html">QuaPy 0.1.7 documentation</a> &#187;</li>
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@ -66,6 +68,11 @@
<td>&#160;&#160;&#160; <td>&#160;&#160;&#160;
<a href="quapy.classification.html#module-quapy.classification"><code class="xref">quapy.classification</code></a></td><td> <a href="quapy.classification.html#module-quapy.classification"><code class="xref">quapy.classification</code></a></td><td>
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<section id="quapy-classification-package"> <section id="quapy-classification-package">
<h1>quapy.classification package<a class="headerlink" href="#quapy-classification-package" title="Permalink to this headline"></a></h1> <h1>quapy.classification package<a class="headerlink" href="#quapy-classification-package" title="Permalink to this heading"></a></h1>
<section id="submodules"> <section id="submodules">
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline"></a></h2> <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this heading"></a></h2>
</section>
<section id="quapy-classification-calibration">
<h2>quapy.classification.calibration<a class="headerlink" href="#quapy-classification-calibration" title="Permalink to this heading"></a></h2>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.1.7.</span></p>
</div>
<span class="target" id="module-quapy.classification.calibration"></span><dl class="py class">
<dt class="sig sig-object py" id="quapy.classification.calibration.BCTSCalibration">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.calibration.</span></span><span class="sig-name descname"><span class="pre">BCTSCalibration</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</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">5</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">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><a class="headerlink" href="#quapy.classification.calibration.BCTSCalibration" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase" title="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase"><code class="xref py py-class docutils literal notranslate"><span class="pre">RecalibratedProbabilisticClassifierBase</span></code></a></p>
<p>Applies the Bias-Corrected Temperature Scaling (BCTS) calibration method from <cite>abstention.calibration</cite>, as defined in
<a class="reference external" href="http://proceedings.mlr.press/v119/alexandari20a.html">Alexandari et al. paper</a>:</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>classifier</strong> – a scikit-learn probabilistic classifier</p></li>
<li><p><strong>val_split</strong> – indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p
in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the
training instances (the rest is used for training). In any case, the classifier is retrained in the whole
training set afterwards. Default value is 5.</p></li>
<li><p><strong>n_jobs</strong> – indicate the number of parallel workers (only when val_split is an integer)</p></li>
<li><p><strong>verbose</strong> – whether or not to display information in the standard output</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.classification.calibration.NBVSCalibration">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.calibration.</span></span><span class="sig-name descname"><span class="pre">NBVSCalibration</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</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">5</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">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><a class="headerlink" href="#quapy.classification.calibration.NBVSCalibration" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase" title="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase"><code class="xref py py-class docutils literal notranslate"><span class="pre">RecalibratedProbabilisticClassifierBase</span></code></a></p>
<p>Applies the No-Bias Vector Scaling (NBVS) calibration method from <cite>abstention.calibration</cite>, as defined in
<a class="reference external" href="http://proceedings.mlr.press/v119/alexandari20a.html">Alexandari et al. paper</a>:</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>classifier</strong> – a scikit-learn probabilistic classifier</p></li>
<li><p><strong>val_split</strong> – indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p
in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the
training instances (the rest is used for training). In any case, the classifier is retrained in the whole
training set afterwards. Default value is 5.</p></li>
<li><p><strong>n_jobs</strong> – indicate the number of parallel workers (only when val_split is an integer)</p></li>
<li><p><strong>verbose</strong> – whether or not to display information in the standard output</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.classification.calibration.RecalibratedProbabilisticClassifier">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.calibration.</span></span><span class="sig-name descname"><span class="pre">RecalibratedProbabilisticClassifier</span></span><a class="headerlink" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifier" 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>Abstract class for (re)calibration method from <cite>abstention.calibration</cite>, as defined in
<a class="reference external" href="http://proceedings.mlr.press/v119/alexandari20a.html">Alexandari, A., Kundaje, A., &amp; Shrikumar, A. (2020, November). Maximum likelihood with bias-corrected calibration
is hard-to-beat at label shift adaptation. In International Conference on Machine Learning (pp. 222-232). PMLR.</a>:</p>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.calibration.</span></span><span class="sig-name descname"><span class="pre">RecalibratedProbabilisticClassifierBase</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calibrator</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">5</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">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><a class="headerlink" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">BaseEstimator</span></code>, <a class="reference internal" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifier" title="quapy.classification.calibration.RecalibratedProbabilisticClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">RecalibratedProbabilisticClassifier</span></code></a></p>
<p>Applies a (re)calibration method from <cite>abstention.calibration</cite>, as defined in
<a class="reference external" href="http://proceedings.mlr.press/v119/alexandari20a.html">Alexandari et al. paper</a>:</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>classifier</strong> – a scikit-learn probabilistic classifier</p></li>
<li><p><strong>calibrator</strong> – the calibration object (an instance of abstention.calibration.CalibratorFactory)</p></li>
<li><p><strong>val_split</strong> – indicate an integer k for performing kFCV to obtain the posterior probabilities, or a float p
in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the
training instances (the rest is used for training). In any case, the classifier is retrained in the whole
training set afterwards. Default value is 5.</p></li>
<li><p><strong>n_jobs</strong> – indicate the number of parallel workers (only when val_split is an integer); default=None</p></li>
<li><p><strong>verbose</strong> – whether or not to display information in the standard output</p></li>
</ul>
</dd>
</dl>
<dl class="py property">
<dt class="sig sig-object py" id="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.classes_">
<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">classes_</span></span><a class="headerlink" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.classes_" title="Permalink to this definition"></a></dt>
<dd><p>Returns the classes on which the classifier has been trained on</p>
<dl class="field-list simple">
<dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>array-like of shape <cite>(n_classes)</cite></p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.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.calibration.RecalibratedProbabilisticClassifierBase.fit" title="Permalink to this definition"></a></dt>
<dd><p>Fits the calibration for the probabilistic classifier.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> with the data instances</p></li>
<li><p><strong>y</strong> – array-like of shape <cite>(n_samples,)</cite> with the class labels</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>self</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_cv">
<span class="sig-name descname"><span class="pre">fit_cv</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.calibration.RecalibratedProbabilisticClassifierBase.fit_cv" title="Permalink to this definition"></a></dt>
<dd><p>Fits the calibration in a cross-validation manner, i.e., it generates posterior probabilities for all
training instances via cross-validation, and then retrains the classifier on all training instances.
The posterior probabilities thus generated are used for calibrating the outpus of the classifier.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> with the data instances</p></li>
<li><p><strong>y</strong> – array-like of shape <cite>(n_samples,)</cite> with the class labels</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>self</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_tr_val">
<span class="sig-name descname"><span class="pre">fit_tr_val</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.calibration.RecalibratedProbabilisticClassifierBase.fit_tr_val" title="Permalink to this definition"></a></dt>
<dd><p>Fits the calibration in a train/val-split manner, i.e.t, it partitions the training instances into a
training and a validation set, and then uses the training samples to learn classifier which is then used
to generate posterior probabilities for the held-out validation data. These posteriors are used to calibrate
the classifier. The classifier is not retrained on the whole dataset.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> with the data instances</p></li>
<li><p><strong>y</strong> – array-like of shape <cite>(n_samples,)</cite> with the class labels</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>self</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.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.calibration.RecalibratedProbabilisticClassifierBase.predict" title="Permalink to this definition"></a></dt>
<dd><p>Predicts class labels for the data instances in <cite>X</cite></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> with the data instances</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>array-like of shape <cite>(n_samples,)</cite> with the class label predictions</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.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.calibration.RecalibratedProbabilisticClassifierBase.predict_proba" title="Permalink to this definition"></a></dt>
<dd><p>Generates posterior probabilities for the data instances in <cite>X</cite></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> with the data instances</p>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>array-like of shape <cite>(n_samples, n_classes)</cite> with posterior probabilities</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.classification.calibration.TSCalibration">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.calibration.</span></span><span class="sig-name descname"><span class="pre">TSCalibration</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</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">5</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">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><a class="headerlink" href="#quapy.classification.calibration.TSCalibration" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase" title="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase"><code class="xref py py-class docutils literal notranslate"><span class="pre">RecalibratedProbabilisticClassifierBase</span></code></a></p>
<p>Applies the Temperature Scaling (TS) calibration method from <cite>abstention.calibration</cite>, as defined in
<a class="reference external" href="http://proceedings.mlr.press/v119/alexandari20a.html">Alexandari et al. paper</a>:</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>classifier</strong> – a scikit-learn probabilistic classifier</p></li>
<li><p><strong>val_split</strong> – indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p
in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the
training instances (the rest is used for training). In any case, the classifier is retrained in the whole
training set afterwards. Default value is 5.</p></li>
<li><p><strong>n_jobs</strong> – indicate the number of parallel workers (only when val_split is an integer)</p></li>
<li><p><strong>verbose</strong> – whether or not to display information in the standard output</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.classification.calibration.VSCalibration">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.calibration.</span></span><span class="sig-name descname"><span class="pre">VSCalibration</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</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">5</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">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><a class="headerlink" href="#quapy.classification.calibration.VSCalibration" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase" title="quapy.classification.calibration.RecalibratedProbabilisticClassifierBase"><code class="xref py py-class docutils literal notranslate"><span class="pre">RecalibratedProbabilisticClassifierBase</span></code></a></p>
<p>Applies the Vector Scaling (VS) calibration method from <cite>abstention.calibration</cite>, as defined in
<a class="reference external" href="http://proceedings.mlr.press/v119/alexandari20a.html">Alexandari et al. paper</a>:</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>classifier</strong> – a scikit-learn probabilistic classifier</p></li>
<li><p><strong>val_split</strong> – indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p
in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the
training instances (the rest is used for training). In any case, the classifier is retrained in the whole
training set afterwards. Default value is 5.</p></li>
<li><p><strong>n_jobs</strong> – indicate the number of parallel workers (only when val_split is an integer)</p></li>
<li><p><strong>verbose</strong> – whether or not to display information in the standard output</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</section> </section>
<section id="module-quapy.classification.methods"> <section id="module-quapy.classification.methods">
<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> <span id="quapy-classification-methods"></span><h2>quapy.classification.methods<a class="headerlink" href="#module-quapy.classification.methods" title="Permalink to this heading"></a></h2>
<dl class="py class"> <dl class="py class">
<dt class="sig sig-object py" id="quapy.classification.methods.LowRankLogisticRegression"> <dt class="sig sig-object py" id="quapy.classification.methods.LowRankLogisticRegression">
<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">LowRankLogisticRegression</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.LowRankLogisticRegression" title="Permalink to this definition"></a></dt> <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.classification.methods.</span></span><span class="sig-name descname"><span class="pre">LowRankLogisticRegression</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.LowRankLogisticRegression" 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> <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">BaseEstimator</span></code></p>
<p>An example of a classification method (i.e., an object that implements <cite>fit</cite>, <cite>predict</cite>, and <cite>predict_proba</cite>) <p>An example of a classification method (i.e., an object that implements <cite>fit</cite>, <cite>predict</cite>, and <cite>predict_proba</cite>)
that also generates embedded inputs (i.e., that implements <cite>transform</cite>), as those required for that also generates embedded inputs (i.e., that implements <cite>transform</cite>), as those required for
<code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.neural.QuaNet</span></code>. This is a mock method to allow for easily instantiating <code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.neural.QuaNet</span></code>. This is a mock method to allow for easily instantiating
@ -70,7 +288,7 @@ that also generates embedded inputs (i.e., that implements <cite>transform</cite
The transformation consists of applying <code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.decomposition.TruncatedSVD</span></code> The transformation consists of applying <code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.decomposition.TruncatedSVD</span></code>
while classification is performed using <code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.linear_model.LogisticRegression</span></code> on the low-rank space.</p> while classification is performed using <code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.linear_model.LogisticRegression</span></code> on the low-rank space.</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Parameters</dt> <dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple"> <dd class="field-odd"><ul class="simple">
<li><p><strong>n_components</strong> – the number of principal components to retain</p></li> <li><p><strong>n_components</strong> – the number of principal components to retain</p></li>
<li><p><strong>kwargs</strong> – parameters for the <li><p><strong>kwargs</strong> – parameters for the
@ -84,13 +302,13 @@ while classification is performed using <code class="xref py py-class docutils l
<dd><p>Fit the model according to the given training data. The fit consists of <dd><p>Fit the model according to the given training data. The fit consists of
fitting <cite>TruncatedSVD</cite> and then <cite>LogisticRegression</cite> on the low-rank representation.</p> fitting <cite>TruncatedSVD</cite> and then <cite>LogisticRegression</cite> on the low-rank representation.</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Parameters</dt> <dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple"> <dd class="field-odd"><ul class="simple">
<li><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> with the instances</p></li> <li><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> with the instances</p></li>
<li><p><strong>y</strong> – array-like of shape <cite>(n_samples, n_classes)</cite> with the class labels</p></li> <li><p><strong>y</strong> – array-like of shape <cite>(n_samples, n_classes)</cite> with the class labels</p></li>
</ul> </ul>
</dd> </dd>
<dt class="field-even">Returns</dt> <dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><cite>self</cite></p> <dd class="field-even"><p><cite>self</cite></p>
</dd> </dd>
</dl> </dl>
@ -101,7 +319,7 @@ fitting <cite>TruncatedSVD</cite> and then <cite>LogisticRegression</cite> on th
<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.LowRankLogisticRegression.get_params" title="Permalink to this definition"></a></dt> <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.LowRankLogisticRegression.get_params" title="Permalink to this definition"></a></dt>
<dd><p>Get hyper-parameters for this estimator.</p> <dd><p>Get hyper-parameters for this estimator.</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Returns</dt> <dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>a dictionary with parameter names mapped to their values</p> <dd class="field-odd"><p>a dictionary with parameter names mapped to their values</p>
</dd> </dd>
</dl> </dl>
@ -112,10 +330,10 @@ fitting <cite>TruncatedSVD</cite> and then <cite>LogisticRegression</cite> on th
<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.LowRankLogisticRegression.predict" title="Permalink to this definition"></a></dt> <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.LowRankLogisticRegression.predict" title="Permalink to this definition"></a></dt>
<dd><p>Predicts labels for the instances <cite>X</cite> embedded into the low-rank space.</p> <dd><p>Predicts labels for the instances <cite>X</cite> embedded into the low-rank space.</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Parameters</dt> <dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> instances to classify</p> <dd class="field-odd"><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> instances to classify</p>
</dd> </dd>
<dt class="field-even">Returns</dt> <dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>a <cite>numpy</cite> array of length <cite>n</cite> containing the label predictions, where <cite>n</cite> is the number of <dd class="field-even"><p>a <cite>numpy</cite> array of length <cite>n</cite> containing the label predictions, where <cite>n</cite> is the number of
instances in <cite>X</cite></p> instances in <cite>X</cite></p>
</dd> </dd>
@ -127,10 +345,10 @@ instances in <cite>X</cite></p>
<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.LowRankLogisticRegression.predict_proba" title="Permalink to this definition"></a></dt> <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.LowRankLogisticRegression.predict_proba" title="Permalink to this definition"></a></dt>
<dd><p>Predicts posterior probabilities for the instances <cite>X</cite> embedded into the low-rank space.</p> <dd><p>Predicts posterior probabilities for the instances <cite>X</cite> embedded into the low-rank space.</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Parameters</dt> <dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> instances to classify</p> <dd class="field-odd"><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> instances to classify</p>
</dd> </dd>
<dt class="field-even">Returns</dt> <dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>array-like of shape <cite>(n_samples, n_classes)</cite> with the posterior probabilities</p> <dd class="field-even"><p>array-like of shape <cite>(n_samples, n_classes)</cite> with the posterior probabilities</p>
</dd> </dd>
</dl> </dl>
@ -141,7 +359,7 @@ instances in <cite>X</cite></p>
<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.LowRankLogisticRegression.set_params" title="Permalink to this definition"></a></dt> <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.LowRankLogisticRegression.set_params" title="Permalink to this definition"></a></dt>
<dd><p>Set the parameters of this estimator.</p> <dd><p>Set the parameters of this estimator.</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Parameters</dt> <dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>parameters</strong> – a <cite>**kwargs</cite> dictionary with the estimator parameters for <dd class="field-odd"><p><strong>parameters</strong> – a <cite>**kwargs</cite> dictionary with the estimator parameters for
<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html">Logistic Regression</a> <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html">Logistic Regression</a>
and eventually also <cite>n_components</cite> for <cite>TruncatedSVD</cite></p> and eventually also <cite>n_components</cite> for <cite>TruncatedSVD</cite></p>
@ -155,10 +373,10 @@ and eventually also <cite>n_components</cite> for <cite>TruncatedSVD</cite></p>
<dd><p>Returns the low-rank approximation of <cite>X</cite> with <cite>n_components</cite> dimensions, or <cite>X</cite> unaltered if <dd><p>Returns the low-rank approximation of <cite>X</cite> with <cite>n_components</cite> dimensions, or <cite>X</cite> unaltered if
<cite>n_components</cite> &gt;= <cite>X.shape[1]</cite>.</p> <cite>n_components</cite> &gt;= <cite>X.shape[1]</cite>.</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Parameters</dt> <dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> instances to embed</p> <dd class="field-odd"><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> instances to embed</p>
</dd> </dd>
<dt class="field-even">Returns</dt> <dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>array-like of shape <cite>(n_samples, n_components)</cite> with the embedded instances</p> <dd class="field-even"><p>array-like of shape <cite>(n_samples, n_components)</cite> with the embedded instances</p>
</dd> </dd>
</dl> </dl>
@ -168,15 +386,15 @@ and eventually also <cite>n_components</cite> for <cite>TruncatedSVD</cite></p>
</section> </section>
<section id="module-quapy.classification.neural"> <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> <span id="quapy-classification-neural"></span><h2>quapy.classification.neural<a class="headerlink" href="#module-quapy.classification.neural" title="Permalink to this heading"></a></h2>
<dl class="py class"> <dl class="py class">
<dt class="sig sig-object py" id="quapy.classification.neural.CNNnet"> <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> <em class="property"><span class="pre">class</span><span class="w"> </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> <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">TextClassifierNet</span></code></a></p>
<p>An implementation of <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> based on <p>An implementation of <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> based on
Convolutional Neural Networks.</p> Convolutional Neural Networks.</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Parameters</dt> <dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple"> <dd class="field-odd"><ul class="simple">
<li><p><strong>vocabulary_size</strong> – the size of the vocabulary</p></li> <li><p><strong>vocabulary_size</strong> – the size of the vocabulary</p></li>
<li><p><strong>n_classes</strong> – number of target classes</p></li> <li><p><strong>n_classes</strong> – number of target classes</p></li>
@ -197,11 +415,11 @@ consecutive tokens that each kernel covers</p></li>
<dd><p>Embeds documents (i.e., performs the forward pass up to the <dd><p>Embeds documents (i.e., performs the forward pass up to the
next-to-last layer).</p> next-to-last layer).</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Parameters</dt> <dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>input</strong> – a batch of instances, typically generated by a torch’s <cite>DataLoader</cite> <dd class="field-odd"><p><strong>input</strong> – a batch of instances, typically generated by a torch’s <cite>DataLoader</cite>
instance (see <a class="reference internal" href="#quapy.classification.neural.TorchDataset" title="quapy.classification.neural.TorchDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.classification.neural.TorchDataset</span></code></a>)</p> instance (see <a class="reference internal" href="#quapy.classification.neural.TorchDataset" title="quapy.classification.neural.TorchDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.classification.neural.TorchDataset</span></code></a>)</p>
</dd> </dd>
<dt class="field-even">Returns</dt> <dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>a torch tensor of shape <cite>(n_samples, n_dimensions)</cite>, where <dd class="field-even"><p>a torch tensor of shape <cite>(n_samples, n_dimensions)</cite>, where
<cite>n_samples</cite> is the number of documents, and <cite>n_dimensions</cite> is the <cite>n_samples</cite> is the number of documents, and <cite>n_dimensions</cite> is the
dimensionality of the embedding</p> dimensionality of the embedding</p>
@ -214,18 +432,23 @@ dimensionality of the embedding</p>
<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> <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><p>Get hyper-parameters for this estimator</p> <dd><p>Get hyper-parameters for this estimator</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Returns</dt> <dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>a dictionary with parameter names mapped to their values</p> <dd class="field-odd"><p>a dictionary with parameter names mapped to their values</p>
</dd> </dd>
</dl> </dl>
</dd></dl> </dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="quapy.classification.neural.CNNnet.training">
<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#quapy.classification.neural.CNNnet.training" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py property"> <dl class="py property">
<dt class="sig sig-object py" id="quapy.classification.neural.CNNnet.vocabulary_size"> <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> <em class="property"><span class="pre">property</span><span class="w"> </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><p>Return the size of the vocabulary</p> <dd><p>Return the size of the vocabulary</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Returns</dt> <dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>integer</p> <dd class="field-odd"><p>integer</p>
</dd> </dd>
</dl> </dl>
@ -235,12 +458,12 @@ dimensionality of the embedding</p>
<dl class="py class"> <dl class="py class">
<dt class="sig sig-object py" id="quapy.classification.neural.LSTMnet"> <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> <em class="property"><span class="pre">class</span><span class="w"> </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> <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">TextClassifierNet</span></code></a></p>
<p>An implementation of <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> based on <p>An implementation of <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> based on
Long Short Term Memory networks.</p> Long Short Term Memory networks.</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Parameters</dt> <dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple"> <dd class="field-odd"><ul class="simple">
<li><p><strong>vocabulary_size</strong> – the size of the vocabulary</p></li> <li><p><strong>vocabulary_size</strong> – the size of the vocabulary</p></li>
<li><p><strong>n_classes</strong> – number of target classes</p></li> <li><p><strong>n_classes</strong> – number of target classes</p></li>
@ -258,11 +481,11 @@ Long Short Term Memory networks.</p>
<dd><p>Embeds documents (i.e., performs the forward pass up to the <dd><p>Embeds documents (i.e., performs the forward pass up to the
next-to-last layer).</p> next-to-last layer).</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Parameters</dt> <dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>x</strong> – a batch of instances, typically generated by a torch’s <cite>DataLoader</cite> <dd class="field-odd"><p><strong>x</strong> – a batch of instances, typically generated by a torch’s <cite>DataLoader</cite>
instance (see <a class="reference internal" href="#quapy.classification.neural.TorchDataset" title="quapy.classification.neural.TorchDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.classification.neural.TorchDataset</span></code></a>)</p> instance (see <a class="reference internal" href="#quapy.classification.neural.TorchDataset" title="quapy.classification.neural.TorchDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.classification.neural.TorchDataset</span></code></a>)</p>
</dd> </dd>
<dt class="field-even">Returns</dt> <dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>a torch tensor of shape <cite>(n_samples, n_dimensions)</cite>, where <dd class="field-even"><p>a torch tensor of shape <cite>(n_samples, n_dimensions)</cite>, where
<cite>n_samples</cite> is the number of documents, and <cite>n_dimensions</cite> is the <cite>n_samples</cite> is the number of documents, and <cite>n_dimensions</cite> is the
dimensionality of the embedding</p> dimensionality of the embedding</p>
@ -275,18 +498,23 @@ dimensionality of the embedding</p>
<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> <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><p>Get hyper-parameters for this estimator</p> <dd><p>Get hyper-parameters for this estimator</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Returns</dt> <dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>a dictionary with parameter names mapped to their values</p> <dd class="field-odd"><p>a dictionary with parameter names mapped to their values</p>
</dd> </dd>
</dl> </dl>
</dd></dl> </dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="quapy.classification.neural.LSTMnet.training">
<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#quapy.classification.neural.LSTMnet.training" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py property"> <dl class="py property">
<dt class="sig sig-object py" id="quapy.classification.neural.LSTMnet.vocabulary_size"> <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> <em class="property"><span class="pre">property</span><span class="w"> </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><p>Return the size of the vocabulary</p> <dd><p>Return the size of the vocabulary</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Returns</dt> <dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>integer</p> <dd class="field-odd"><p>integer</p>
</dd> </dd>
</dl> </dl>
@ -296,11 +524,11 @@ dimensionality of the embedding</p>
<dl class="py class"> <dl class="py class">
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer"> <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> <em class="property"><span class="pre">class</span><span class="w"> </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="w"> </span><span class="n"><a class="reference internal" href="#quapy.classification.neural.TextClassifierNet" title="quapy.classification.neural.TextClassifierNet"><span class="pre">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> <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
<p>Trains a neural network for text classification.</p> <p>Trains a neural network for text classification.</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Parameters</dt> <dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple"> <dd class="field-odd"><ul class="simple">
<li><p><strong>net</strong> – an instance of <cite>TextClassifierNet</cite> implementing the forward pass</p></li> <li><p><strong>net</strong> – an instance of <cite>TextClassifierNet</cite> implementing the forward pass</p></li>
<li><p><strong>lr</strong> – learning rate (default 1e-3)</p></li> <li><p><strong>lr</strong> – learning rate (default 1e-3)</p></li>
@ -319,10 +547,10 @@ according to the evaluation in the held-out validation split (default ‘../chec
</dl> </dl>
<dl class="py property"> <dl class="py property">
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.device"> <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> <em class="property"><span class="pre">property</span><span class="w"> </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><p>Gets the device in which the network is allocated</p> <dd><p>Gets the device in which the network is allocated</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Returns</dt> <dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>device</p> <dd class="field-odd"><p>device</p>
</dd> </dd>
</dl> </dl>
@ -333,14 +561,14 @@ according to the evaluation in the held-out validation split (default ‘../chec
<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> <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><p>Fits the model according to the given training data.</p> <dd><p>Fits the model according to the given training data.</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Parameters</dt> <dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple"> <dd class="field-odd"><ul class="simple">
<li><p><strong>instances</strong> – list of lists of indexed tokens</p></li> <li><p><strong>instances</strong> – list of lists of indexed tokens</p></li>
<li><p><strong>labels</strong> – array-like of shape <cite>(n_samples, n_classes)</cite> with the class labels</p></li> <li><p><strong>labels</strong> – array-like of shape <cite>(n_samples, n_classes)</cite> with the class labels</p></li>
<li><p><strong>val_split</strong> – proportion of training documents to be taken as the validation set (default 0.3)</p></li> <li><p><strong>val_split</strong> – proportion of training documents to be taken as the validation set (default 0.3)</p></li>
</ul> </ul>
</dd> </dd>
<dt class="field-even">Returns</dt> <dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p></p> <dd class="field-even"><p></p>
</dd> </dd>
</dl> </dl>
@ -351,7 +579,7 @@ according to the evaluation in the held-out validation split (default ‘../chec
<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> <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><p>Get hyper-parameters for this estimator</p> <dd><p>Get hyper-parameters for this estimator</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Returns</dt> <dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>a dictionary with parameter names mapped to their values</p> <dd class="field-odd"><p>a dictionary with parameter names mapped to their values</p>
</dd> </dd>
</dl> </dl>
@ -362,10 +590,10 @@ according to the evaluation in the held-out validation split (default ‘../chec
<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> <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><p>Predicts labels for the instances</p> <dd><p>Predicts labels for the instances</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Parameters</dt> <dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>instances</strong> – list of lists of indexed tokens</p> <dd class="field-odd"><p><strong>instances</strong> – list of lists of indexed tokens</p>
</dd> </dd>
<dt class="field-even">Returns</dt> <dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>a <cite>numpy</cite> array of length <cite>n</cite> containing the label predictions, where <cite>n</cite> is the number of <dd class="field-even"><p>a <cite>numpy</cite> array of length <cite>n</cite> containing the label predictions, where <cite>n</cite> is the number of
instances in <cite>X</cite></p> instances in <cite>X</cite></p>
</dd> </dd>
@ -377,10 +605,10 @@ instances in <cite>X</cite></p>
<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> <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><p>Predicts posterior probabilities for the instances</p> <dd><p>Predicts posterior probabilities for the instances</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Parameters</dt> <dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> instances to classify</p> <dd class="field-odd"><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> instances to classify</p>
</dd> </dd>
<dt class="field-even">Returns</dt> <dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>array-like of shape <cite>(n_samples, n_classes)</cite> with the posterior probabilities</p> <dd class="field-even"><p>array-like of shape <cite>(n_samples, n_classes)</cite> with the posterior probabilities</p>
</dd> </dd>
</dl> </dl>
@ -391,7 +619,7 @@ instances in <cite>X</cite></p>
<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> <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><p>Reinitialize the network parameters</p> <dd><p>Reinitialize the network parameters</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Parameters</dt> <dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple"> <dd class="field-odd"><ul class="simple">
<li><p><strong>vocab_size</strong> – the size of the vocabulary</p></li> <li><p><strong>vocab_size</strong> – the size of the vocabulary</p></li>
<li><p><strong>n_classes</strong> – the number of target classes</p></li> <li><p><strong>n_classes</strong> – the number of target classes</p></li>
@ -407,7 +635,7 @@ instances in <cite>X</cite></p>
In this current version, parameter names for the trainer and learner should In this current version, parameter names for the trainer and learner should
be disjoint.</p> be disjoint.</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Parameters</dt> <dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>params</strong> – a <cite>**kwargs</cite> dictionary with the parameters</p> <dd class="field-odd"><p><strong>params</strong> – a <cite>**kwargs</cite> dictionary with the parameters</p>
</dd> </dd>
</dl> </dl>
@ -418,10 +646,10 @@ be disjoint.</p>
<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> <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><p>Returns the embeddings of the instances</p> <dd><p>Returns the embeddings of the instances</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Parameters</dt> <dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>instances</strong> – list of lists of indexed tokens</p> <dd class="field-odd"><p><strong>instances</strong> – list of lists of indexed tokens</p>
</dd> </dd>
<dt class="field-even">Returns</dt> <dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>array-like of shape <cite>(n_samples, embed_size)</cite> with the embedded instances, <dd class="field-even"><p>array-like of shape <cite>(n_samples, embed_size)</cite> with the embedded instances,
where <cite>embed_size</cite> is defined by the classification network</p> where <cite>embed_size</cite> is defined by the classification network</p>
</dd> </dd>
@ -432,15 +660,15 @@ where <cite>embed_size</cite> is defined by the classification network</p>
<dl class="py class"> <dl class="py class">
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet"> <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> <em class="property"><span class="pre">class</span><span class="w"> </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> <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></p>
<p>Abstract Text classifier (<cite>torch.nn.Module</cite>)</p> <p>Abstract Text classifier (<cite>torch.nn.Module</cite>)</p>
<dl class="py method"> <dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.dimensions"> <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> <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><p>Gets the number of dimensions of the embedding space</p> <dd><p>Gets the number of dimensions of the embedding space</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Returns</dt> <dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>integer</p> <dd class="field-odd"><p>integer</p>
</dd> </dd>
</dl> </dl>
@ -448,15 +676,15 @@ where <cite>embed_size</cite> is defined by the classification network</p>
<dl class="py method"> <dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.document_embedding"> <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> <em class="property"><span class="pre">abstract</span><span class="w"> </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><p>Embeds documents (i.e., performs the forward pass up to the <dd><p>Embeds documents (i.e., performs the forward pass up to the
next-to-last layer).</p> next-to-last layer).</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Parameters</dt> <dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>x</strong> – a batch of instances, typically generated by a torch’s <cite>DataLoader</cite> <dd class="field-odd"><p><strong>x</strong> – a batch of instances, typically generated by a torch’s <cite>DataLoader</cite>
instance (see <a class="reference internal" href="#quapy.classification.neural.TorchDataset" title="quapy.classification.neural.TorchDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.classification.neural.TorchDataset</span></code></a>)</p> instance (see <a class="reference internal" href="#quapy.classification.neural.TorchDataset" title="quapy.classification.neural.TorchDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.classification.neural.TorchDataset</span></code></a>)</p>
</dd> </dd>
<dt class="field-even">Returns</dt> <dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>a torch tensor of shape <cite>(n_samples, n_dimensions)</cite>, where <dd class="field-even"><p>a torch tensor of shape <cite>(n_samples, n_dimensions)</cite>, where
<cite>n_samples</cite> is the number of documents, and <cite>n_dimensions</cite> is the <cite>n_samples</cite> is the number of documents, and <cite>n_dimensions</cite> is the
dimensionality of the embedding</p> dimensionality of the embedding</p>
@ -469,11 +697,11 @@ dimensionality of the embedding</p>
<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> <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>Performs the forward pass.</p> <dd><p>Performs the forward pass.</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Parameters</dt> <dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>x</strong> – a batch of instances, typically generated by a torch’s <cite>DataLoader</cite> <dd class="field-odd"><p><strong>x</strong> – a batch of instances, typically generated by a torch’s <cite>DataLoader</cite>
instance (see <a class="reference internal" href="#quapy.classification.neural.TorchDataset" title="quapy.classification.neural.TorchDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.classification.neural.TorchDataset</span></code></a>)</p> instance (see <a class="reference internal" href="#quapy.classification.neural.TorchDataset" title="quapy.classification.neural.TorchDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.classification.neural.TorchDataset</span></code></a>)</p>
</dd> </dd>
<dt class="field-even">Returns</dt> <dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>a tensor of shape <cite>(n_instances, n_classes)</cite> with the decision scores <dd class="field-even"><p>a tensor of shape <cite>(n_instances, n_classes)</cite> with the decision scores
for each of the instances and classes</p> for each of the instances and classes</p>
</dd> </dd>
@ -482,10 +710,10 @@ for each of the instances and classes</p>
<dl class="py method"> <dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.get_params"> <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> <em class="property"><span class="pre">abstract</span><span class="w"> </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><p>Get hyper-parameters for this estimator</p> <dd><p>Get hyper-parameters for this estimator</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Returns</dt> <dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>a dictionary with parameter names mapped to their values</p> <dd class="field-odd"><p>a dictionary with parameter names mapped to their values</p>
</dd> </dd>
</dl> </dl>
@ -496,23 +724,28 @@ for each of the instances and classes</p>
<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> <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><p>Predicts posterior probabilities for the instances in <cite>x</cite></p> <dd><p>Predicts posterior probabilities for the instances in <cite>x</cite></p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Parameters</dt> <dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>x</strong> – a torch tensor of indexed tokens with shape <cite>(n_instances, pad_length)</cite> <dd class="field-odd"><p><strong>x</strong> – a torch tensor of indexed tokens with shape <cite>(n_instances, pad_length)</cite>
where <cite>n_instances</cite> is the number of instances in the batch, and <cite>pad_length</cite> where <cite>n_instances</cite> is the number of instances in the batch, and <cite>pad_length</cite>
is length of the pad in the batch</p> is length of the pad in the batch</p>
</dd> </dd>
<dt class="field-even">Returns</dt> <dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>array-like of shape <cite>(n_samples, n_classes)</cite> with the posterior probabilities</p> <dd class="field-even"><p>array-like of shape <cite>(n_samples, n_classes)</cite> with the posterior probabilities</p>
</dd> </dd>
</dl> </dl>
</dd></dl> </dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.training">
<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.training" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py property"> <dl class="py property">
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.vocabulary_size"> <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> <em class="property"><span class="pre">property</span><span class="w"> </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><p>Return the size of the vocabulary</p> <dd><p>Return the size of the vocabulary</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Returns</dt> <dt class="field-odd">Returns<span class="colon">:</span></dt>
<dd class="field-odd"><p>integer</p> <dd class="field-odd"><p>integer</p>
</dd> </dd>
</dl> </dl>
@ -528,11 +761,11 @@ is length of the pad in the batch</p>
<dl class="py class"> <dl class="py class">
<dt class="sig sig-object py" id="quapy.classification.neural.TorchDataset"> <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> <em class="property"><span class="pre">class</span><span class="w"> </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> <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Dataset</span></code></p>
<p>Transforms labelled instances into a Torch’s <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.utils.data.DataLoader</span></code> object</p> <p>Transforms labelled instances into a Torch’s <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.utils.data.DataLoader</span></code> object</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Parameters</dt> <dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple"> <dd class="field-odd"><ul class="simple">
<li><p><strong>instances</strong> – list of lists of indexed tokens</p></li> <li><p><strong>instances</strong> – list of lists of indexed tokens</p></li>
<li><p><strong>labels</strong> – array-like of shape <cite>(n_samples, n_classes)</cite> with the class labels</p></li> <li><p><strong>labels</strong> – array-like of shape <cite>(n_samples, n_classes)</cite> with the class labels</p></li>
@ -545,7 +778,7 @@ is length of the pad in the batch</p>
<dd><p>Converts the labelled collection into a Torch DataLoader with dynamic padding for <dd><p>Converts the labelled collection into a Torch DataLoader with dynamic padding for
the batch</p> the batch</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Parameters</dt> <dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple"> <dd class="field-odd"><ul class="simple">
<li><p><strong>batch_size</strong> – batch size</p></li> <li><p><strong>batch_size</strong> – batch size</p></li>
<li><p><strong>shuffle</strong> – whether or not to shuffle instances</p></li> <li><p><strong>shuffle</strong> – whether or not to shuffle instances</p></li>
@ -555,7 +788,7 @@ applied, meaning that if the longest document in the batch is shorter than
<li><p><strong>device</strong> – whether to allocate tensors in cpu or in cuda</p></li> <li><p><strong>device</strong> – whether to allocate tensors in cpu or in cuda</p></li>
</ul> </ul>
</dd> </dd>
<dt class="field-even">Returns</dt> <dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>a <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.utils.data.DataLoader</span></code> object</p> <dd class="field-even"><p>a <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.utils.data.DataLoader</span></code> object</p>
</dd> </dd>
</dl> </dl>
@ -565,11 +798,11 @@ applied, meaning that if the longest document in the batch is shorter than
</section> </section>
<section id="module-quapy.classification.svmperf"> <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> <span id="quapy-classification-svmperf"></span><h2>quapy.classification.svmperf<a class="headerlink" href="#module-quapy.classification.svmperf" title="Permalink to this heading"></a></h2>
<dl class="py class"> <dl class="py class">
<dt class="sig sig-object py" id="quapy.classification.svmperf.SVMperf"> <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> <em class="property"><span class="pre">class</span><span class="w"> </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> <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">BaseEstimator</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">ClassifierMixin</span></code></p>
<p>A wrapper for the <a class="reference external" href="https://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html">SVM-perf package</a> by Thorsten Joachims. <p>A wrapper for the <a class="reference external" href="https://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html">SVM-perf package</a> by Thorsten Joachims.
When using losses for quantification, the source code has to be patched. See When using losses for quantification, the source code has to be patched. See
the <a class="reference external" href="https://hlt-isti.github.io/QuaPy/build/html/Installation.html#svm-perf-with-quantification-oriented-losses">installation documentation</a> the <a class="reference external" href="https://hlt-isti.github.io/QuaPy/build/html/Installation.html#svm-perf-with-quantification-oriented-losses">installation documentation</a>
@ -582,7 +815,7 @@ for further details.</p>
</ul> </ul>
</div></blockquote> </div></blockquote>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Parameters</dt> <dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple"> <dd class="field-odd"><ul class="simple">
<li><p><strong>svmperf_base</strong> – path to directory containing the binary files <cite>svm_perf_learn</cite> and <cite>svm_perf_classify</cite></p></li> <li><p><strong>svmperf_base</strong> – path to directory containing the binary files <cite>svm_perf_learn</cite> and <cite>svm_perf_classify</cite></p></li>
<li><p><strong>C</strong> – trade-off between training error and margin (default 0.01)</p></li> <li><p><strong>C</strong> – trade-off between training error and margin (default 0.01)</p></li>
@ -596,13 +829,13 @@ for further details.</p>
<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> <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><p>Evaluate the decision function for the samples in <cite>X</cite>.</p> <dd><p>Evaluate the decision function for the samples in <cite>X</cite>.</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Parameters</dt> <dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple"> <dd class="field-odd"><ul class="simple">
<li><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> containing the instances to classify</p></li> <li><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> containing the instances to classify</p></li>
<li><p><strong>y</strong> – unused</p></li> <li><p><strong>y</strong> – unused</p></li>
</ul> </ul>
</dd> </dd>
<dt class="field-even">Returns</dt> <dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>array-like of shape <cite>(n_samples,)</cite> containing the decision scores of the instances</p> <dd class="field-even"><p>array-like of shape <cite>(n_samples,)</cite> containing the decision scores of the instances</p>
</dd> </dd>
</dl> </dl>
@ -613,13 +846,13 @@ for further details.</p>
<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> <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><p>Trains the SVM for the multivariate performance loss</p> <dd><p>Trains the SVM for the multivariate performance loss</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Parameters</dt> <dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple"> <dd class="field-odd"><ul class="simple">
<li><p><strong>X</strong> – training instances</p></li> <li><p><strong>X</strong> – training instances</p></li>
<li><p><strong>y</strong> – a binary vector of labels</p></li> <li><p><strong>y</strong> – a binary vector of labels</p></li>
</ul> </ul>
</dd> </dd>
<dt class="field-even">Returns</dt> <dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p><cite>self</cite></p> <dd class="field-even"><p><cite>self</cite></p>
</dd> </dd>
</dl> </dl>
@ -628,12 +861,16 @@ for further details.</p>
<dl class="py method"> <dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.svmperf.SVMperf.predict"> <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> <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><p>Predicts labels for the instances <cite>X</cite> <dd><p>Predicts labels for the instances <cite>X</cite></p>
:param X: array-like of shape <cite>(n_samples, n_features)</cite> instances to classify <dl class="field-list simple">
:return: a <cite>numpy</cite> array of length <cite>n</cite> containing the label predictions, where <cite>n</cite> is the number of</p> <dt class="field-odd">Parameters<span class="colon">:</span></dt>
<blockquote> <dd class="field-odd"><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> instances to classify</p>
<div><p>instances in <cite>X</cite></p> </dd>
</div></blockquote> <dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>a <cite>numpy</cite> array of length <cite>n</cite> containing the label predictions, where <cite>n</cite> is the number of
instances in <cite>X</cite></p>
</dd>
</dl>
</dd></dl> </dd></dl>
<dl class="py method"> <dl class="py method">
@ -641,7 +878,7 @@ for further details.</p>
<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> <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 hyper-parameters for svm-perf. Currently, only the <cite>C</cite> parameter is supported</p> <dd><p>Set the hyper-parameters for svm-perf. Currently, only the <cite>C</cite> parameter is supported</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Parameters</dt> <dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><p><strong>parameters</strong> – a <cite>**kwargs</cite> dictionary <cite>{‘C’: &lt;float&gt;}</cite></p> <dd class="field-odd"><p><strong>parameters</strong> – a <cite>**kwargs</cite> dictionary <cite>{‘C’: &lt;float&gt;}</cite></p>
</dd> </dd>
</dl> </dl>
@ -649,14 +886,14 @@ for further details.</p>
<dl class="py attribute"> <dl class="py attribute">
<dt class="sig sig-object py" id="quapy.classification.svmperf.SVMperf.valid_losses"> <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> <span class="sig-name descname"><span class="pre">valid_losses</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </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></dd></dl>
</dd></dl> </dd></dl>
</section> </section>
<section id="module-quapy.classification"> <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> <span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-quapy.classification" title="Permalink to this heading"></a></h2>
</section> </section>
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@ -667,24 +904,31 @@ for further details.</p>
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<p>QuaPy is an open source framework for Quantification (a.k.a. Supervised Prevalence Estimation) written in Python.</p>
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<li class="right" > <li class="right" >
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69
examples/custom_quantifier.py Normal file
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@ -0,0 +1,69 @@
import quapy as qp
from data import LabelledCollection
from method.base import BaseQuantifier, BinaryQuantifier
from model_selection import GridSearchQ
from quapy.method.aggregative import PACC, AggregativeProbabilisticQuantifier
from quapy.protocol import APP
import numpy as np
from sklearn.linear_model import LogisticRegression
# Define a custom quantifier: for this example, we will consider a new quantification algorithm that uses a
# logistic regressor for generating posterior probabilities, and then applies a custom threshold value to the
# posteriors. Since the quantifier internally uses a classifier, it is an aggregative quantifier; and since it
# relies on posterior probabilities, then it is a probabilistic aggregative quantifier. Note also it has an
# internal hyperparameter (let say, alpha) which is the decision threshold. Let's also assume the quantifier
# is binary, for simplicity.
class MyQuantifier(AggregativeProbabilisticQuantifier, BinaryQuantifier):
def __init__(self, classifier, alpha=0.5):
self.alpha = alpha
# aggregative quantifiers have an internal self.classifier attribute
self.classifier = classifier
def fit(self, data: LabelledCollection, fit_classifier=True):
assert fit_classifier, 'this quantifier needs to fit the classifier!'
self.classifier.fit(*data.Xy)
return self
# in general, we would need to implement the method quantify(self, instances) but, since this method is of
# type aggregative, we can simply implement the method aggregate, which has the following interface
def aggregate(self, classif_predictions: np.ndarray):
# the posterior probabilities have already been generated by the quantify method; we only need to
# specify what to do with them
positive_probabilities = classif_predictions[:, 1]
crisp_decisions = positive_probabilities > self.alpha
pos_prev = crisp_decisions.mean()
neg_prev = 1-pos_prev
return np.asarray([neg_prev, pos_prev])
if __name__ == '__main__':
qp.environ['SAMPLE_SIZE'] = 100
# define an instance of our custom quantifier
quantifier = MyQuantifier(LogisticRegression(), alpha=0.5)
# load the IMDb dataset
train, test = qp.datasets.fetch_reviews('imdb', tfidf=True, min_df=5).train_test
train, val = train.split_stratified(train_prop=0.75)
# model selection
# let us assume we want to explore our hyperparameter alpha along with one hyperparameter of the classifier
param_grid = {
'alpha': np.linspace(0,1,11), # quantifier-dependent hyperparameter
'classifier__C': np.logspace(-2,2,5) # classifier-dependent hyperparameter
}
quantifier = GridSearchQ(quantifier, param_grid, protocol=APP(val), n_jobs=-1, verbose=True).fit(train)
# evaluation
mae = qp.evaluation.evaluate(quantifier, protocol=APP(test), error_metric='mae')
print(f'MAE = {mae:.4f}')
# final remarks: this method is only for demonstration purposes and makes little sense in general. The method relies
# on an hyperparameter alpha for binarizing the posterior probabilities. A much better way for fulfilling this
# goal would be to calibrate the classifier (LogisticRegression is already reasonably well calibrated) and then
# simply cut at 0.5.

78
quapy/CHANGE_LOG.txt
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@ -1,11 +1,19 @@
# main changes in 0.1.7 Change Log 0.1.7
---------------------
- Protocols are now abstracted as AbstractProtocol. There is a new class extending AbstractProtocol called - Protocols are now abstracted as instances of AbstractProtocol. There is a new class extending AbstractProtocol called
AbstractStochasticSeededProtocol, which implements a seeding policy to allow replicate the series of samplings. AbstractStochasticSeededProtocol, which implements a seeding policy to allow replicate the series of samplings.
There are some examples of protocols, APP, NPP, USimplexPP, CovariateShiftPP (experimental). There are some examples of protocols, APP, NPP, USimplexPP, DomainMixer (experimental).
The idea is to start the sampling by simply calling the __call__ method. The idea is to start the sampling by simply calling the __call__ method.
This change has a great impact in the framework, since many functions in qp.evaluation, qp.model_selection, This change has a great impact in the framework, since many functions in qp.evaluation, qp.model_selection,
and sampling functions in LabelledCollection make use of the old functions. and sampling functions in LabelledCollection relied of the old functions. E.g., the functionality of
qp.evaluation.artificial_prevalence_report or qp.evaluation.natural_prevalence_report is now obtained by means of
qp.evaluation.report which takes a protocol as an argument. I have not maintained compatibility with the old
interfaces because I did not really like them. Check the wiki guide and the examples for more details.
check guides
check examples
- ACC, PACC, Forman's threshold variants have been parallelized. - ACC, PACC, Forman's threshold variants have been parallelized.
@ -51,47 +59,31 @@
multiclass quantification. That is to say, one could get a multiclass variant of the (originally binary) HDy multiclass quantification. That is to say, one could get a multiclass variant of the (originally binary) HDy
method aligned with the Firat's formulation. method aligned with the Firat's formulation.
- internal method properties "binary", "aggregative", and "probabilistic" have been removed; these conditions are
checked via isinstance
- quantifiers (i.e., classes that inherit from BaseQuantifier) are not forced to implement classes_ or n_classes;
these can be used anyway internally, but the framework will not suppose (nor impose) that a quantifier implements
them
- qp.evaluation.prediction has been optimized so that, if a quantifier is of type aggregative, and if the evaluation
protocol is of type OnLabelledCollection, then the computation is faster. In this specific case, the predictions
are issued only once and for all, and not for each sample. An exception to this (which is implement also), is
when the number of instances across all samples is anyway smaller than the number of instances in the original
labelled collection; in this case the heuristic is of no help, and is therefore not applied.
- the distinction between "classify" and "posterior_probabilities" has been removed in Aggregative quantifiers,
so that probabilistic classifiers return posterior probabilities, while non-probabilistic quantifiers
return crisp decisions.
Things to fix: Things to fix:
- calibration with recalibration methods has to be fixed for exact_train_prev in EMQ (conflicts with clone, deepcopy, etc.) --------------
- clean functions like binary, aggregative, probabilistic, etc; those should be resolved via isinstance(): - OneVsAll is duplicated (in aggregative and in general), and is not well documented. It is not working either.
this is not working; I don't know how to make the isinstance work. Looks like there is some problem with the Check method def __parallel(self, func, *args, **kwargs) in aggregative.OneVsAll
path of the imported class wrt the path of the class that arrives from another module...
- clean classes_ and n_classes from methods (maybe not from aggregative ones, but those have to be used only
internally and not imposed in any abstract class)
- optimize "qp.evaluation.prediction" for aggregative methods (pre-classification)
- update unit tests - update unit tests
- Policies should be able to set their output to "labelled_collection" or "instances_prevalence" or something similar. - update Wikis...
- Policies should implement the "gen()" one, taking a reader function as an input, and a folder path maybe
- Review all documentation, redo the Sphinx doc, update Wikis...
- Resolve the OneVsAll thing (it is in base.py and in aggregative.py) - Resolve the OneVsAll thing (it is in base.py and in aggregative.py)
- Better handle the environment (e.g., with n_jobs)
- test cross_generate_predictions and cancel cross_generate_predictions_depr
- Add a proper log? - Add a proper log?
- test LoadSamplesFromDirectory (in protocols.py) - improve plots
- improve plots? - documentation of protocols is incomplete
- I have removed the distinction between "classify" and "posterior_probabilities" in the Aggregative quantifiers,
so that probabilistic classifiers actually return posterior probabilities, while non-probabilistic quantifiers
return instead crisp decisions. The idea was to unify the quantification function (i.e., now it is always
classify & aggregate, irrespective of the class). However, this has caused a problem with OneVsAll. This has to
be checked, since it is now innecessarily complicated (it also has old references to .probabilistic, and all this
stuff).
- Check method def __parallel(self, func, *args, **kwargs) in aggregative.OneVsAll
New features:
- Add LeQua2022 to datasets (everything automatic, and with proper protocols "gen")
- Add an "experimental room", with scripts to quickly test new ideas and see results.
# 0.1.7
# change the LabelledCollection API (removing protocol-related samplings)
# need to change the two references to the above in the wiki / doc, and code examples...
# removed artificial_prevalence_sampling from functional
# also: some parameters in the init could be used to indicate that the method should return a tuple with
# unlabelled instances and the vector of prevalence values (and not a LabelledCollection).
# Or: this can be done in a different function; i.e., we use one function (now __call__) to return
# LabelledCollections, and another new one for returning the other output, which is more general for
# evaluation purposes.
# the so-called "gen" function has to be implemented as a protocol. The problem here is that this function
# should be able to return only unlabelled instances plus a vector of prevalences (and not LabelledCollections).
# This was coded as different functions in 0.1.6

21
quapy/__init__.py
View File

@ -23,9 +23,28 @@ environ = {
} }
def get_njobs(n_jobs): def _get_njobs(n_jobs):
"""
If `n_jobs` is None, then it returns `environ['N_JOBS']`; if otherwise, returns `n_jobs`.
:param n_jobs: the number of `n_jobs` or None if not specified
:return: int
"""
return environ['N_JOBS'] if n_jobs is None else n_jobs return environ['N_JOBS'] if n_jobs is None else n_jobs
def _get_sample_size(sample_size):
"""
If `sample_size` is None, then it returns `environ['SAMPLE_SIZE']`; if otherwise, returns `sample_size`.
If none of these are set, then a ValueError exception is raised.
:param sample_size: integer or None
:return: int
"""
sample_size = environ['SAMPLE_SIZE'] if sample_size is None else sample_size
if sample_size is None:
raise ValueError('neither sample_size nor qp.environ["SAMPLE_SIZE"] have been specified')
return sample_size

77
quapy/classification/calibration.py
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@ -12,12 +12,18 @@ import numpy as np
class RecalibratedProbabilisticClassifier: class RecalibratedProbabilisticClassifier:
"""
Abstract class for (re)calibration method from `abstention.calibration`, as defined in
`Alexandari, A., Kundaje, A., & Shrikumar, A. (2020, November). Maximum likelihood with bias-corrected calibration
is hard-to-beat at label shift adaptation. In International Conference on Machine Learning (pp. 222-232). PMLR.
<http://proceedings.mlr.press/v119/alexandari20a.html>`_:
"""
pass pass
class RecalibratedProbabilisticClassifierBase(BaseEstimator, RecalibratedProbabilisticClassifier): class RecalibratedProbabilisticClassifierBase(BaseEstimator, RecalibratedProbabilisticClassifier):
""" """
Applies a (re)calibration method from abstention.calibration, as defined in Applies a (re)calibration method from `abstention.calibration`, as defined in
`Alexandari et al. paper <http://proceedings.mlr.press/v119/alexandari20a.html>`_: `Alexandari et al. paper <http://proceedings.mlr.press/v119/alexandari20a.html>`_:
:param classifier: a scikit-learn probabilistic classifier :param classifier: a scikit-learn probabilistic classifier
@ -25,7 +31,7 @@ class RecalibratedProbabilisticClassifierBase(BaseEstimator, RecalibratedProbabi
:param val_split: indicate an integer k for performing kFCV to obtain the posterior probabilities, or a float p :param val_split: indicate an integer k for performing kFCV to obtain the posterior probabilities, or a float p
in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the
training instances (the rest is used for training). In any case, the classifier is retrained in the whole training instances (the rest is used for training). In any case, the classifier is retrained in the whole
training set afterwards. training set afterwards. Default value is 5.
:param n_jobs: indicate the number of parallel workers (only when val_split is an integer); default=None :param n_jobs: indicate the number of parallel workers (only when val_split is an integer); default=None
:param verbose: whether or not to display information in the standard output :param verbose: whether or not to display information in the standard output
""" """
@ -38,6 +44,13 @@ class RecalibratedProbabilisticClassifierBase(BaseEstimator, RecalibratedProbabi
self.verbose = verbose self.verbose = verbose
def fit(self, X, y): def fit(self, X, y):
"""
Fits the calibration for the probabilistic classifier.
:param X: array-like of shape `(n_samples, n_features)` with the data instances
:param y: array-like of shape `(n_samples,)` with the class labels
:return: self
"""
k = self.val_split k = self.val_split
if isinstance(k, int): if isinstance(k, int):
if k < 2: if k < 2:
@ -49,6 +62,15 @@ class RecalibratedProbabilisticClassifierBase(BaseEstimator, RecalibratedProbabi
return self.fit_cv(X, y) return self.fit_cv(X, y)
def fit_cv(self, X, y): def fit_cv(self, X, y):
"""
Fits the calibration in a cross-validation manner, i.e., it generates posterior probabilities for all
training instances via cross-validation, and then retrains the classifier on all training instances.
The posterior probabilities thus generated are used for calibrating the outpus of the classifier.
:param X: array-like of shape `(n_samples, n_features)` with the data instances
:param y: array-like of shape `(n_samples,)` with the class labels
:return: self
"""
posteriors = cross_val_predict( posteriors = cross_val_predict(
self.classifier, X, y, cv=self.val_split, n_jobs=self.n_jobs, verbose=self.verbose, method='predict_proba' self.classifier, X, y, cv=self.val_split, n_jobs=self.n_jobs, verbose=self.verbose, method='predict_proba'
) )
@ -58,6 +80,16 @@ class RecalibratedProbabilisticClassifierBase(BaseEstimator, RecalibratedProbabi
return self return self
def fit_tr_val(self, X, y): def fit_tr_val(self, X, y):
"""
Fits the calibration in a train/val-split manner, i.e.t, it partitions the training instances into a
training and a validation set, and then uses the training samples to learn classifier which is then used
to generate posterior probabilities for the held-out validation data. These posteriors are used to calibrate
the classifier. The classifier is not retrained on the whole dataset.
:param X: array-like of shape `(n_samples, n_features)` with the data instances
:param y: array-like of shape `(n_samples,)` with the class labels
:return: self
"""
Xtr, Xva, ytr, yva = train_test_split(X, y, test_size=self.val_split, stratify=y) Xtr, Xva, ytr, yva = train_test_split(X, y, test_size=self.val_split, stratify=y)
self.classifier.fit(Xtr, ytr) self.classifier.fit(Xtr, ytr)
posteriors = self.classifier.predict_proba(Xva) posteriors = self.classifier.predict_proba(Xva)
@ -66,32 +98,49 @@ class RecalibratedProbabilisticClassifierBase(BaseEstimator, RecalibratedProbabi
return self return self
def predict(self, X): def predict(self, X):
"""
Predicts class labels for the data instances in `X`
:param X: array-like of shape `(n_samples, n_features)` with the data instances
:return: array-like of shape `(n_samples,)` with the class label predictions
"""
return self.classifier.predict(X) return self.classifier.predict(X)
def predict_proba(self, X): def predict_proba(self, X):
"""
Generates posterior probabilities for the data instances in `X`
:param X: array-like of shape `(n_samples, n_features)` with the data instances
:return: array-like of shape `(n_samples, n_classes)` with posterior probabilities
"""
posteriors = self.classifier.predict_proba(X) posteriors = self.classifier.predict_proba(X)
return self.calibration_function(posteriors) return self.calibration_function(posteriors)
@property @property
def classes_(self): def classes_(self):
"""
Returns the classes on which the classifier has been trained on
:return: array-like of shape `(n_classes)`
"""
return self.classifier.classes_ return self.classifier.classes_
class NBVSCalibration(RecalibratedProbabilisticClassifierBase): class NBVSCalibration(RecalibratedProbabilisticClassifierBase):
""" """
Applies the No-Bias Vector Scaling (NBVS) calibration method from abstention.calibration, as defined in Applies the No-Bias Vector Scaling (NBVS) calibration method from `abstention.calibration`, as defined in
`Alexandari et al. paper <http://proceedings.mlr.press/v119/alexandari20a.html>`_: `Alexandari et al. paper <http://proceedings.mlr.press/v119/alexandari20a.html>`_:
:param classifier: a scikit-learn probabilistic classifier :param classifier: a scikit-learn probabilistic classifier
:param val_split: indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p :param val_split: indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p
in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the
training instances (the rest is used for training). In any case, the classifier is retrained in the whole training instances (the rest is used for training). In any case, the classifier is retrained in the whole
training set afterwards. training set afterwards. Default value is 5.
:param n_jobs: indicate the number of parallel workers (only when val_split is an integer) :param n_jobs: indicate the number of parallel workers (only when val_split is an integer)
:param verbose: whether or not to display information in the standard output :param verbose: whether or not to display information in the standard output
""" """
def __init__(self, classifier, val_split=5, n_jobs=1, verbose=False): def __init__(self, classifier, val_split=5, n_jobs=None, verbose=False):
self.classifier = classifier self.classifier = classifier
self.calibrator = NoBiasVectorScaling(verbose=verbose) self.calibrator = NoBiasVectorScaling(verbose=verbose)
self.val_split = val_split self.val_split = val_split
@ -101,19 +150,19 @@ class NBVSCalibration(RecalibratedProbabilisticClassifierBase):
class BCTSCalibration(RecalibratedProbabilisticClassifierBase): class BCTSCalibration(RecalibratedProbabilisticClassifierBase):
""" """
Applies the Bias-Corrected Temperature Scaling (BCTS) calibration method from abstention.calibration, as defined in Applies the Bias-Corrected Temperature Scaling (BCTS) calibration method from `abstention.calibration`, as defined in
`Alexandari et al. paper <http://proceedings.mlr.press/v119/alexandari20a.html>`_: `Alexandari et al. paper <http://proceedings.mlr.press/v119/alexandari20a.html>`_:
:param classifier: a scikit-learn probabilistic classifier :param classifier: a scikit-learn probabilistic classifier
:param val_split: indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p :param val_split: indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p
in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the
training instances (the rest is used for training). In any case, the classifier is retrained in the whole training instances (the rest is used for training). In any case, the classifier is retrained in the whole
training set afterwards. training set afterwards. Default value is 5.
:param n_jobs: indicate the number of parallel workers (only when val_split is an integer) :param n_jobs: indicate the number of parallel workers (only when val_split is an integer)
:param verbose: whether or not to display information in the standard output :param verbose: whether or not to display information in the standard output
""" """
def __init__(self, classifier, val_split=5, n_jobs=1, verbose=False): def __init__(self, classifier, val_split=5, n_jobs=None, verbose=False):
self.classifier = classifier self.classifier = classifier
self.calibrator = TempScaling(verbose=verbose, bias_positions='all') self.calibrator = TempScaling(verbose=verbose, bias_positions='all')
self.val_split = val_split self.val_split = val_split
@ -123,19 +172,19 @@ class BCTSCalibration(RecalibratedProbabilisticClassifierBase):
class TSCalibration(RecalibratedProbabilisticClassifierBase): class TSCalibration(RecalibratedProbabilisticClassifierBase):
""" """
Applies the Temperature Scaling (TS) calibration method from abstention.calibration, as defined in Applies the Temperature Scaling (TS) calibration method from `abstention.calibration`, as defined in
`Alexandari et al. paper <http://proceedings.mlr.press/v119/alexandari20a.html>`_: `Alexandari et al. paper <http://proceedings.mlr.press/v119/alexandari20a.html>`_:
:param classifier: a scikit-learn probabilistic classifier :param classifier: a scikit-learn probabilistic classifier
:param val_split: indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p :param val_split: indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p
in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the
training instances (the rest is used for training). In any case, the classifier is retrained in the whole training instances (the rest is used for training). In any case, the classifier is retrained in the whole
training set afterwards. training set afterwards. Default value is 5.
:param n_jobs: indicate the number of parallel workers (only when val_split is an integer) :param n_jobs: indicate the number of parallel workers (only when val_split is an integer)
:param verbose: whether or not to display information in the standard output :param verbose: whether or not to display information in the standard output
""" """
def __init__(self, classifier, val_split=5, n_jobs=1, verbose=False): def __init__(self, classifier, val_split=5, n_jobs=None, verbose=False):
self.classifier = classifier self.classifier = classifier
self.calibrator = TempScaling(verbose=verbose) self.calibrator = TempScaling(verbose=verbose)
self.val_split = val_split self.val_split = val_split
@ -145,19 +194,19 @@ class TSCalibration(RecalibratedProbabilisticClassifierBase):
class VSCalibration(RecalibratedProbabilisticClassifierBase): class VSCalibration(RecalibratedProbabilisticClassifierBase):
""" """
Applies the Vector Scaling (VS) calibration method from abstention.calibration, as defined in Applies the Vector Scaling (VS) calibration method from `abstention.calibration`, as defined in
`Alexandari et al. paper <http://proceedings.mlr.press/v119/alexandari20a.html>`_: `Alexandari et al. paper <http://proceedings.mlr.press/v119/alexandari20a.html>`_:
:param classifier: a scikit-learn probabilistic classifier :param classifier: a scikit-learn probabilistic classifier
:param val_split: indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p :param val_split: indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p
in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the
training instances (the rest is used for training). In any case, the classifier is retrained in the whole training instances (the rest is used for training). In any case, the classifier is retrained in the whole
training set afterwards. training set afterwards. Default value is 5.
:param n_jobs: indicate the number of parallel workers (only when val_split is an integer) :param n_jobs: indicate the number of parallel workers (only when val_split is an integer)
:param verbose: whether or not to display information in the standard output :param verbose: whether or not to display information in the standard output
""" """
def __init__(self, classifier, val_split=5, n_jobs=1, verbose=False): def __init__(self, classifier, val_split=5, n_jobs=None, verbose=False):
self.classifier = classifier self.classifier = classifier
self.calibrator = VectorScaling(verbose=verbose) self.calibrator = VectorScaling(verbose=verbose)
self.val_split = val_split self.val_split = val_split

1
quapy/classification/svmperf.py
View File

@ -94,6 +94,7 @@ class SVMperf(BaseEstimator, ClassifierMixin):
def predict(self, X): def predict(self, X):
""" """
Predicts labels for the instances `X` Predicts labels for the instances `X`
:param X: array-like of shape `(n_samples, n_features)` instances to classify :param X: array-like of shape `(n_samples, n_features)` instances to classify
:return: a `numpy` array of length `n` containing the label predictions, where `n` is the number of :return: a `numpy` array of length `n` containing the label predictions, where `n` is the number of
instances in `X` instances in `X`

24
quapy/data/datasets.py
View File

@ -554,7 +554,31 @@ def _df_replace(df, col, repl={'yes': 1, 'no':0}, astype=float):
def fetch_lequa2022(task, data_home=None): def fetch_lequa2022(task, data_home=None):
""" """
Loads the official datasets provided for the `LeQua <https://lequa2022.github.io/index>`_ competition.
In brief, there are 4 tasks (T1A, T1B, T2A, T2B) having to do with text quantification
problems. Tasks T1A and T1B provide documents in vector form, while T2A and T2B provide raw documents instead.
Tasks T1A and T2A are binary sentiment quantification problems, while T2A and T2B are multiclass quantification
problems consisting of estimating the class prevalence values of 28 different merchandise products.
We refer to the `Esuli, A., Moreo, A., Sebastiani, F., & Sperduti, G. (2022).
A Detailed Overview of LeQua@ CLEF 2022: Learning to Quantify.
<https://ceur-ws.org/Vol-3180/paper-146.pdf>`_ for a detailed description
on the tasks and datasets.
The datasets are downloaded only once, and stored for fast reuse.
See `lequa2022_experiments.py` provided in the example folder, that can serve as a guide on how to use these
datasets.
:param task: a string representing the task name; valid ones are T1A, T1B, T2A, and T2B
:param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default
~/quay_data/ directory)
:return: a tuple `(train, val_gen, test_gen)` where `train` is an instance of
:class:`quapy.data.base.LabelledCollection`, `val_gen` and `test_gen` are instances of
:class:`quapy.protocol.SamplesFromDir`, i.e., are sampling protocols that return a series of samples
labelled by prevalence.
""" """
from quapy.data._lequa2022 import load_raw_documents, load_vector_documents, SamplesFromDir from quapy.data._lequa2022 import load_raw_documents, load_vector_documents, SamplesFromDir
assert task in LEQUA2022_TASKS, \ assert task in LEQUA2022_TASKS, \

7
quapy/data/preprocessing.py
View File

@ -88,7 +88,7 @@ def standardize(dataset: Dataset, inplace=False):
:param dataset: a :class:`quapy.data.base.Dataset` object :param dataset: a :class:`quapy.data.base.Dataset` object
:param inplace: set to True if the transformation is to be applied inplace, or to False (default) if a new :param inplace: set to True if the transformation is to be applied inplace, or to False (default) if a new
:class:`quapy.data.base.Dataset` is to be returned :class:`quapy.data.base.Dataset` is to be returned
:return: :return: an instance of :class:`quapy.data.base.Dataset`
""" """
s = StandardScaler(copy=not inplace) s = StandardScaler(copy=not inplace)
training = s.fit_transform(dataset.training.instances) training = s.fit_transform(dataset.training.instances)
@ -147,7 +147,8 @@ class IndexTransformer:
contains, and that would be generated by sklearn's contains, and that would be generated by sklearn's
`CountVectorizer <https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html>`_ `CountVectorizer <https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html>`_
:param kwargs: keyworded arguments from `CountVectorizer <https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html>`_ :param kwargs: keyworded arguments from
`CountVectorizer <https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html>`_
""" """
def __init__(self, **kwargs): def __init__(self, **kwargs):
@ -179,7 +180,7 @@ class IndexTransformer:
""" """
# given the number of tasks and the number of jobs, generates the slices for the parallel processes # given the number of tasks and the number of jobs, generates the slices for the parallel processes
assert self.unk != -1, 'transform called before fit' assert self.unk != -1, 'transform called before fit'
n_jobs = qp.get_njobs(n_jobs) n_jobs = qp._get_njobs(n_jobs)
indexed = map_parallel(func=self._index, args=X, n_jobs=n_jobs) indexed = map_parallel(func=self._index, args=X, n_jobs=n_jobs)
return np.asarray(indexed) return np.asarray(indexed)

439
quapy/depr_evaluation.py
View File

@ -1,439 +0,0 @@
from typing import Union, Callable, Iterable
import numpy as np
from tqdm import tqdm
import inspect
import quapy as qp
from quapy.data import LabelledCollection
from quapy.method.base import BaseQuantifier
from quapy.util import temp_seed
import quapy.functional as F
import pandas as pd
def artificial_prevalence_prediction(
model: BaseQuantifier,
test: LabelledCollection,
sample_size,
n_prevpoints=101,
repeats=1,
eval_budget: int = None,
n_jobs=1,
random_seed=42,
verbose=False):
"""
Performs the predictions for all samples generated according to the Artificial Prevalence Protocol (APP).
The APP consists of exploring a grid of prevalence values containing `n_prevalences` points (e.g.,
[0, 0.05, 0.1, 0.15, ..., 1], if `n_prevalences=21`), and generating all valid combinations of
prevalence values for all classes (e.g., for 3 classes, samples with [0, 0, 1], [0, 0.05, 0.95], ...,
[1, 0, 0] prevalence values of size `sample_size` will be considered). The number of samples for each valid
combination of prevalence values is indicated by `repeats`.
:param model: the model in charge of generating the class prevalence estimations
:param test: the test set on which to perform APP
:param sample_size: integer, the size of the samples
:param n_prevpoints: integer, the number of different prevalences to sample (or set to None if eval_budget
is specified; default 101, i.e., steps of 1%)
:param repeats: integer, the number of repetitions for each prevalence (default 1)
:param eval_budget: integer, if specified, sets a ceil on the number of evaluations to perform. For example, if
there are 3 classes, `repeats=1`, and `eval_budget=20`, then `n_prevpoints` will be set to 5, since this
will generate 15 different prevalence vectors ([0, 0, 1], [0, 0.25, 0.75], [0, 0.5, 0.5] ... [1, 0, 0]) and
since setting `n_prevpoints=6` would produce more than 20 evaluations.
:param n_jobs: integer, number of jobs to be run in parallel (default 1)
:param random_seed: integer, allows to replicate the samplings. The seed is local to the method and does not affect
any other random process (default 42)
:param verbose: if True, shows a progress bar
:return: a tuple containing two `np.ndarrays` of shape `(m,n,)` with `m` the number of samples
`(n_prevpoints*repeats)` and `n` the number of classes. The first one contains the true prevalence values
for the samples generated while the second one contains the prevalence estimations
"""
n_prevpoints, _ = qp.evaluation._check_num_evals(test.n_classes, n_prevpoints, eval_budget, repeats, verbose)
with temp_seed(random_seed):
indexes = list(test.artificial_sampling_index_generator(sample_size, n_prevpoints, repeats))
return _predict_from_indexes(indexes, model, test, n_jobs, verbose)
def natural_prevalence_prediction(
model: BaseQuantifier,
test: LabelledCollection,
sample_size,
repeats,
n_jobs=1,
random_seed=42,
verbose=False):
"""
Performs the predictions for all samples generated according to the Natural Prevalence Protocol (NPP).
The NPP consists of drawing samples uniformly at random, therefore approximately preserving the natural
prevalence of the collection.
:param model: the model in charge of generating the class prevalence estimations
:param test: the test set on which to perform NPP
:param sample_size: integer, the size of the samples
:param repeats: integer, the number of samples to generate
:param n_jobs: integer, number of jobs to be run in parallel (default 1)
:param random_seed: allows to replicate the samplings. The seed is local to the method and does not affect
any other random process (default 42)
:param verbose: if True, shows a progress bar
:return: a tuple containing two `np.ndarrays` of shape `(m,n,)` with `m` the number of samples
`(repeats)` and `n` the number of classes. The first one contains the true prevalence values
for the samples generated while the second one contains the prevalence estimations
"""
with temp_seed(random_seed):
indexes = list(test.natural_sampling_index_generator(sample_size, repeats))
return _predict_from_indexes(indexes, model, test, n_jobs, verbose)
def gen_prevalence_prediction(model: BaseQuantifier, gen_fn: Callable, eval_budget=None):
"""
Generates prevalence predictions for a custom protocol defined as a generator function that yields
samples at each iteration. The sequence of samples is processed exhaustively if `eval_budget=None`
or up to the `eval_budget` iterations if specified.
:param model: the model in charge of generating the class prevalence estimations
:param gen_fn: a generator function yielding one sample at each iteration
:param eval_budget: a maximum number of evaluations to run. Set to None (default) for exploring the
entire sequence
:return: a tuple containing two `np.ndarrays` of shape `(m,n,)` with `m` the number of samples
generated and `n` the number of classes. The first one contains the true prevalence values
for the samples generated while the second one contains the prevalence estimations
"""
if not inspect.isgenerator(gen_fn()):
raise ValueError('param "gen_fun" is not a callable returning a generator')
if not isinstance(eval_budget, int):
eval_budget = -1
true_prevalences, estim_prevalences = [], []
for sample_instances, true_prev in gen_fn():
true_prevalences.append(true_prev)
estim_prevalences.append(model.quantify(sample_instances))
eval_budget -= 1
if eval_budget == 0:
break
true_prevalences = np.asarray(true_prevalences)
estim_prevalences = np.asarray(estim_prevalences)
return true_prevalences, estim_prevalences
def _predict_from_indexes(
indexes,
model: BaseQuantifier,
test: LabelledCollection,
n_jobs=1,
verbose=False):
if model.aggregative: #isinstance(model, qp.method.aggregative.AggregativeQuantifier):
# print('\tinstance of aggregative-quantifier')
quantification_func = model.aggregate
if model.probabilistic: # isinstance(model, qp.method.aggregative.AggregativeProbabilisticQuantifier):
# print('\t\tinstance of probabilitstic-aggregative-quantifier')
preclassified_instances = model.posterior_probabilities(test.instances)
else:
# print('\t\tinstance of hard-aggregative-quantifier')
preclassified_instances = model.classify(test.instances)
test = LabelledCollection(preclassified_instances, test.labels)
else:
# print('\t\tinstance of base-quantifier')
quantification_func = model.quantify
def _predict_prevalences(index):
sample = test.sampling_from_index(index)
true_prevalence = sample.prevalence()
estim_prevalence = quantification_func(sample.instances)
return true_prevalence, estim_prevalence
pbar = tqdm(indexes, desc='[artificial sampling protocol] generating predictions') if verbose else indexes
results = qp.util.parallel(_predict_prevalences, pbar, n_jobs=n_jobs)
true_prevalences, estim_prevalences = zip(*results)
true_prevalences = np.asarray(true_prevalences)
estim_prevalences = np.asarray(estim_prevalences)
return true_prevalences, estim_prevalences
def artificial_prevalence_report(
model: BaseQuantifier,
test: LabelledCollection,
sample_size,
n_prevpoints=101,
repeats=1,
eval_budget: int = None,
n_jobs=1,
random_seed=42,
error_metrics:Iterable[Union[str,Callable]]='mae',
verbose=False):
"""
Generates an evaluation report for all samples generated according to the Artificial Prevalence Protocol (APP).
The APP consists of exploring a grid of prevalence values containing `n_prevalences` points (e.g.,
[0, 0.05, 0.1, 0.15, ..., 1], if `n_prevalences=21`), and generating all valid combinations of
prevalence values for all classes (e.g., for 3 classes, samples with [0, 0, 1], [0, 0.05, 0.95], ...,
[1, 0, 0] prevalence values of size `sample_size` will be considered). The number of samples for each valid
combination of prevalence values is indicated by `repeats`.
Te report takes the form of a
pandas' `dataframe <https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html>`_
in which the rows correspond to different samples, and the columns inform of the true prevalence values,
the estimated prevalence values, and the score obtained by each of the evaluation measures indicated.
:param model: the model in charge of generating the class prevalence estimations
:param test: the test set on which to perform APP
:param sample_size: integer, the size of the samples
:param n_prevpoints: integer, the number of different prevalences to sample (or set to None if eval_budget
is specified; default 101, i.e., steps of 1%)
:param repeats: integer, the number of repetitions for each prevalence (default 1)
:param eval_budget: integer, if specified, sets a ceil on the number of evaluations to perform. For example, if
there are 3 classes, `repeats=1`, and `eval_budget=20`, then `n_prevpoints` will be set to 5, since this
will generate 15 different prevalence vectors ([0, 0, 1], [0, 0.25, 0.75], [0, 0.5, 0.5] ... [1, 0, 0]) and
since setting `n_prevpoints=6` would produce more than 20 evaluations.
:param n_jobs: integer, number of jobs to be run in parallel (default 1)
:param random_seed: integer, allows to replicate the samplings. The seed is local to the method and does not affect
any other random process (default 42)
:param error_metrics: a string indicating the name of the error (as defined in :mod:`quapy.error`) or a
callable error function; optionally, a list of strings or callables can be indicated, if the results
are to be evaluated with more than one error metric. Default is "mae"
:param verbose: if True, shows a progress bar
:return: pandas' dataframe with rows corresponding to different samples, and with columns informing of the
true prevalence values, the estimated prevalence values, and the score obtained by each of the evaluation
measures indicated.
"""
true_prevs, estim_prevs = artificial_prevalence_prediction(
model, test, sample_size, n_prevpoints, repeats, eval_budget, n_jobs, random_seed, verbose
)
return _prevalence_report(true_prevs, estim_prevs, error_metrics)
def natural_prevalence_report(
model: BaseQuantifier,
test: LabelledCollection,
sample_size,
repeats=1,
n_jobs=1,
random_seed=42,
error_metrics:Iterable[Union[str,Callable]]='mae',
verbose=False):
"""
Generates an evaluation report for all samples generated according to the Natural Prevalence Protocol (NPP).
The NPP consists of drawing samples uniformly at random, therefore approximately preserving the natural
prevalence of the collection.
Te report takes the form of a
pandas' `dataframe <https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html>`_
in which the rows correspond to different samples, and the columns inform of the true prevalence values,
the estimated prevalence values, and the score obtained by each of the evaluation measures indicated.
:param model: the model in charge of generating the class prevalence estimations
:param test: the test set on which to perform NPP
:param sample_size: integer, the size of the samples
:param repeats: integer, the number of samples to generate
:param n_jobs: integer, number of jobs to be run in parallel (default 1)
:param random_seed: allows to replicate the samplings. The seed is local to the method and does not affect
any other random process (default 42)
:param error_metrics: a string indicating the name of the error (as defined in :mod:`quapy.error`) or a
callable error function; optionally, a list of strings or callables can be indicated, if the results
are to be evaluated with more than one error metric. Default is "mae"
:param verbose: if True, shows a progress bar
:return: a tuple containing two `np.ndarrays` of shape `(m,n,)` with `m` the number of samples
`(repeats)` and `n` the number of classes. The first one contains the true prevalence values
for the samples generated while the second one contains the prevalence estimations
"""
true_prevs, estim_prevs = natural_prevalence_prediction(
model, test, sample_size, repeats, n_jobs, random_seed, verbose
)
return _prevalence_report(true_prevs, estim_prevs, error_metrics)
def gen_prevalence_report(model: BaseQuantifier, gen_fn: Callable, eval_budget=None,
error_metrics:Iterable[Union[str,Callable]]='mae'):
"""
GGenerates an evaluation report for a custom protocol defined as a generator function that yields
samples at each iteration. The sequence of samples is processed exhaustively if `eval_budget=None`
or up to the `eval_budget` iterations if specified.
Te report takes the form of a
pandas' `dataframe <https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html>`_
in which the rows correspond to different samples, and the columns inform of the true prevalence values,
the estimated prevalence values, and the score obtained by each of the evaluation measures indicated.
:param model: the model in charge of generating the class prevalence estimations
:param gen_fn: a generator function yielding one sample at each iteration
:param eval_budget: a maximum number of evaluations to run. Set to None (default) for exploring the
entire sequence
:return: a tuple containing two `np.ndarrays` of shape `(m,n,)` with `m` the number of samples
generated. The first one contains the true prevalence values
for the samples generated while the second one contains the prevalence estimations
"""
true_prevs, estim_prevs = gen_prevalence_prediction(model, gen_fn, eval_budget)
return _prevalence_report(true_prevs, estim_prevs, error_metrics)
def _prevalence_report(
true_prevs,
estim_prevs,
error_metrics: Iterable[Union[str, Callable]] = 'mae'):
if isinstance(error_metrics, str):
error_metrics = [error_metrics]
error_names = [e if isinstance(e, str) else e.__name__ for e in error_metrics]
error_funcs = [qp.error.from_name(e) if isinstance(e, str) else e for e in error_metrics]
assert all(hasattr(e, '__call__') for e in error_funcs), 'invalid error functions'
df = pd.DataFrame(columns=['true-prev', 'estim-prev'] + error_names)
for true_prev, estim_prev in zip(true_prevs, estim_prevs):
series = {'true-prev': true_prev, 'estim-prev': estim_prev}
for error_name, error_metric in zip(error_names, error_funcs):
score = error_metric(true_prev, estim_prev)
series[error_name] = score
df = df.append(series, ignore_index=True)
return df
def artificial_prevalence_protocol(
model: BaseQuantifier,
test: LabelledCollection,
sample_size,
n_prevpoints=101,
repeats=1,
eval_budget: int = None,
n_jobs=1,
random_seed=42,
error_metric:Union[str,Callable]='mae',
verbose=False):
"""
Generates samples according to the Artificial Prevalence Protocol (APP).
The APP consists of exploring a grid of prevalence values containing `n_prevalences` points (e.g.,
[0, 0.05, 0.1, 0.15, ..., 1], if `n_prevalences=21`), and generating all valid combinations of
prevalence values for all classes (e.g., for 3 classes, samples with [0, 0, 1], [0, 0.05, 0.95], ...,
[1, 0, 0] prevalence values of size `sample_size` will be considered). The number of samples for each valid
combination of prevalence values is indicated by `repeats`.
:param model: the model in charge of generating the class prevalence estimations
:param test: the test set on which to perform APP
:param sample_size: integer, the size of the samples
:param n_prevpoints: integer, the number of different prevalences to sample (or set to None if eval_budget
is specified; default 101, i.e., steps of 1%)
:param repeats: integer, the number of repetitions for each prevalence (default 1)
:param eval_budget: integer, if specified, sets a ceil on the number of evaluations to perform. For example, if
there are 3 classes, `repeats=1`, and `eval_budget=20`, then `n_prevpoints` will be set to 5, since this
will generate 15 different prevalence vectors ([0, 0, 1], [0, 0.25, 0.75], [0, 0.5, 0.5] ... [1, 0, 0]) and
since setting `n_prevpoints=6` would produce more than 20 evaluations.
:param n_jobs: integer, number of jobs to be run in parallel (default 1)
:param random_seed: integer, allows to replicate the samplings. The seed is local to the method and does not affect
any other random process (default 42)
:param error_metric: a string indicating the name of the error (as defined in :mod:`quapy.error`) or a
callable error function
:param verbose: set to True (default False) for displaying some information on standard output
:return: yields one sample at a time
"""
if isinstance(error_metric, str):
error_metric = qp.error.from_name(error_metric)
assert hasattr(error_metric, '__call__'), 'invalid error function'
true_prevs, estim_prevs = artificial_prevalence_prediction(
model, test, sample_size, n_prevpoints, repeats, eval_budget, n_jobs, random_seed, verbose
)
return error_metric(true_prevs, estim_prevs)
def natural_prevalence_protocol(
model: BaseQuantifier,
test: LabelledCollection,
sample_size,
repeats=1,
n_jobs=1,
random_seed=42,
error_metric:Union[str,Callable]='mae',
verbose=False):
"""
Generates samples according to the Natural Prevalence Protocol (NPP).
The NPP consists of drawing samples uniformly at random, therefore approximately preserving the natural
prevalence of the collection.
:param model: the model in charge of generating the class prevalence estimations
:param test: the test set on which to perform NPP
:param sample_size: integer, the size of the samples
:param repeats: integer, the number of samples to generate
:param n_jobs: integer, number of jobs to be run in parallel (default 1)
:param random_seed: allows to replicate the samplings. The seed is local to the method and does not affect
any other random process (default 42)
:param error_metric: a string indicating the name of the error (as defined in :mod:`quapy.error`) or a
callable error function
:param verbose: if True, shows a progress bar
:return: yields one sample at a time
"""
if isinstance(error_metric, str):
error_metric = qp.error.from_name(error_metric)
assert hasattr(error_metric, '__call__'), 'invalid error function'
true_prevs, estim_prevs = natural_prevalence_prediction(
model, test, sample_size, repeats, n_jobs, random_seed, verbose
)
return error_metric(true_prevs, estim_prevs)
def evaluate(model: BaseQuantifier, test_samples:Iterable[LabelledCollection], error_metric:Union[str, Callable], n_jobs:int=-1):
"""
Evaluates a model on a sequence of test samples in terms of a given error metric.
:param model: the model in charge of generating the class prevalence estimations
:param test_samples: an iterable yielding one sample at a time
:param error_metric: a string indicating the name of the error (as defined in :mod:`quapy.error`) or a
callable error function
:param n_jobs: integer, number of jobs to be run in parallel (default 1)
:return: the score obtained using `error_metric`
"""
if isinstance(error_metric, str):
error_metric = qp.error.from_name(error_metric)
scores = qp.util.parallel(_delayed_eval, ((model, Ti, error_metric) for Ti in test_samples), n_jobs=n_jobs)
return np.mean(scores)
def _delayed_eval(args):
model, test, error = args
prev_estim = model.quantify(test.instances)
prev_true = test.prevalence()
return error(prev_true, prev_estim)
def _check_num_evals(n_classes, n_prevpoints=None, eval_budget=None, repeats=1, verbose=False):
if n_prevpoints is None and eval_budget is None:
raise ValueError('either n_prevpoints or eval_budget has to be specified')
elif n_prevpoints is None:
assert eval_budget > 0, 'eval_budget must be a positive integer'
n_prevpoints = F.get_nprevpoints_approximation(eval_budget, n_classes, repeats)
eval_computations = F.num_prevalence_combinations(n_prevpoints, n_classes, repeats)
if verbose:
print(f'setting n_prevpoints={n_prevpoints} so that the number of '
f'evaluations ({eval_computations}) does not exceed the evaluation '
f'budget ({eval_budget})')
elif eval_budget is None:
eval_computations = F.num_prevalence_combinations(n_prevpoints, n_classes, repeats)
if verbose:
print(f'{eval_computations} evaluations will be performed for each '
f'combination of hyper-parameters')
else:
eval_computations = F.num_prevalence_combinations(n_prevpoints, n_classes, repeats)
if eval_computations > eval_budget:
n_prevpoints = F.get_nprevpoints_approximation(eval_budget, n_classes, repeats)
new_eval_computations = F.num_prevalence_combinations(n_prevpoints, n_classes, repeats)
if verbose:
print(f'the budget of evaluations would be exceeded with '
f'n_prevpoints={n_prevpoints}. Chaning to n_prevpoints={n_prevpoints}. This will produce '
f'{new_eval_computations} evaluation computations for each hyper-parameter combination.')
return n_prevpoints, eval_computations

5
quapy/error.py
View File

@ -11,11 +11,6 @@ def from_name(err_name):
""" """
assert err_name in ERROR_NAMES, f'unknown error {err_name}' assert err_name in ERROR_NAMES, f'unknown error {err_name}'
callable_error = globals()[err_name] callable_error = globals()[err_name]
# if err_name in QUANTIFICATION_ERROR_SMOOTH_NAMES:
# eps = __check_eps()
# def bound_callable_error(y_true, y_pred):
# return callable_error(y_true, y_pred, eps)
# return bound_callable_error
return callable_error return callable_error

23
quapy/functional.py
View File

@ -70,7 +70,7 @@ def HellingerDistance(P, Q):
The HD for two discrete distributions of `k` bins is defined as: The HD for two discrete distributions of `k` bins is defined as:
.. math:: .. math::
HD(P,Q) = \\frac{ 1 }{ \\sqrt{ 2 } } \\sqrt{ \sum_{i=1}^k ( \\sqrt{p_i} - \\sqrt{q_i} )^2 } HD(P,Q) = \\frac{ 1 }{ \\sqrt{ 2 } } \\sqrt{ \\sum_{i=1}^k ( \\sqrt{p_i} - \\sqrt{q_i} )^2 }
:param P: real-valued array-like of shape `(k,)` representing a discrete distribution :param P: real-valued array-like of shape `(k,)` representing a discrete distribution
:param Q: real-valued array-like of shape `(k,)` representing a discrete distribution :param Q: real-valued array-like of shape `(k,)` representing a discrete distribution
@ -78,11 +78,21 @@ def HellingerDistance(P, Q):
""" """
return np.sqrt(np.sum((np.sqrt(P) - np.sqrt(Q))**2)) return np.sqrt(np.sum((np.sqrt(P) - np.sqrt(Q))**2))
def TopsoeDistance(P, Q, epsilon=1e-20): def TopsoeDistance(P, Q, epsilon=1e-20):
""" Topsoe
""" """
return np.sum(P*np.log((2*P+epsilon)/(P+Q+epsilon)) + Topsoe distance between two (discretized) distributions `P` and `Q`.
Q*np.log((2*Q+epsilon)/(P+Q+epsilon))) The Topsoe distance for two discrete distributions of `k` bins is defined as:
.. math::
Topsoe(P,Q) = \\sum_{i=1}^k \\left( p_i \\log\\left(\\frac{ 2 p_i + \\epsilon }{ p_i+q_i+\\epsilon }\\right) +
q_i \\log\\left(\\frac{ 2 q_i + \\epsilon }{ p_i+q_i+\\epsilon }\\right) \\right)
:param P: real-valued array-like of shape `(k,)` representing a discrete distribution
:param Q: real-valued array-like of shape `(k,)` representing a discrete distribution
:return: float
"""
return np.sum(P*np.log((2*P+epsilon)/(P+Q+epsilon)) + Q*np.log((2*Q+epsilon)/(P+Q+epsilon)))
def uniform_prevalence_sampling(n_classes, size=1): def uniform_prevalence_sampling(n_classes, size=1):
@ -136,7 +146,6 @@ def adjusted_quantification(prevalence_estim, tpr, fpr, clip=True):
.. math:: .. math::
ACC(p) = \\frac{ p - fpr }{ tpr - fpr } ACC(p) = \\frac{ p - fpr }{ tpr - fpr }
:param prevalence_estim: float, the estimated value for the positive class :param prevalence_estim: float, the estimated value for the positive class
:param tpr: float, the true positive rate of the classifier :param tpr: float, the true positive rate of the classifier
:param fpr: float, the false positive rate of the classifier :param fpr: float, the false positive rate of the classifier
@ -230,7 +239,7 @@ def get_nprevpoints_approximation(combinations_budget:int, n_classes:int, n_repe
that the number of valid prevalence values generated as combinations of prevalence points (points in a that the number of valid prevalence values generated as combinations of prevalence points (points in a
`n_classes`-dimensional simplex) do not exceed combinations_budget. `n_classes`-dimensional simplex) do not exceed combinations_budget.
:param combinations_budget: integer, maximum number of combinatios allowed :param combinations_budget: integer, maximum number of combinations allowed
:param n_classes: integer, number of classes :param n_classes: integer, number of classes
:param n_repeats: integer, number of repetitions for each prevalence combination :param n_repeats: integer, number of repetitions for each prevalence combination
:return: the largest number of prevalence points that generate less than combinations_budget valid prevalences :return: the largest number of prevalence points that generate less than combinations_budget valid prevalences
@ -248,6 +257,7 @@ def get_nprevpoints_approximation(combinations_budget:int, n_classes:int, n_repe
def check_prevalence_vector(p, raise_exception=False, toleranze=1e-08): def check_prevalence_vector(p, raise_exception=False, toleranze=1e-08):
""" """
Checks that p is a valid prevalence vector, i.e., that it contains values in [0,1] and that the values sum up to 1. Checks that p is a valid prevalence vector, i.e., that it contains values in [0,1] and that the values sum up to 1.
:param p: the prevalence vector to check :param p: the prevalence vector to check
:return: True if `p` is valid, False otherwise :return: True if `p` is valid, False otherwise
""" """
@ -265,3 +275,4 @@ def check_prevalence_vector(p, raise_exception=False, toleranze=1e-08):
raise ValueError('the prevalence vector does not sum up to 1') raise ValueError('the prevalence vector does not sum up to 1')
return False return False
return True return True

58
quapy/method/aggregative.py
View File

@ -76,7 +76,7 @@ class AggregativeQuantifier(BaseQuantifier):
by the classifier. by the classifier.
:param instances: array-like :param instances: array-like
:return: `np.ndarray` of shape `(self.n_classes_,)` with class prevalence estimates. :return: `np.ndarray` of shape `(n_classes)` with class prevalence estimates.
""" """
classif_predictions = self.classify(instances) classif_predictions = self.classify(instances)
return self.aggregate(classif_predictions) return self.aggregate(classif_predictions)
@ -87,7 +87,7 @@ class AggregativeQuantifier(BaseQuantifier):
Implements the aggregation of label predictions. Implements the aggregation of label predictions.
:param classif_predictions: `np.ndarray` of label predictions :param classif_predictions: `np.ndarray` of label predictions
:return: `np.ndarray` of shape `(self.n_classes_,)` with class prevalence estimates. :return: `np.ndarray` of shape `(n_classes,)` with class prevalence estimates.
""" """
... ...
@ -113,19 +113,6 @@ class AggregativeProbabilisticQuantifier(AggregativeQuantifier):
def classify(self, instances): def classify(self, instances):
return self.classifier.predict_proba(instances) return self.classifier.predict_proba(instances)
# def set_params(self, **parameters):
# if isinstance(self.classifier, CalibratedClassifierCV):
# if self.classifier.get_params().get('base_estimator') == 'deprecated':
# key_prefix = 'estimator__' # this has changed in the newer versions of sklearn
# else:
# key_prefix = 'base_estimator__'
# parameters = {key_prefix + k: v for k, v in parameters.items()}
# elif isinstance(self.classifier, RecalibratedClassifier):
# parameters = {'estimator__' + k: v for k, v in parameters.items()}
#
# self.classifier.set_params(**parameters)
# return self
# Helper # Helper
# ------------------------------------ # ------------------------------------
@ -198,7 +185,7 @@ def cross_generate_predictions(
n_jobs n_jobs
): ):
n_jobs = qp.get_njobs(n_jobs) n_jobs = qp._get_njobs(n_jobs)
if isinstance(val_split, int): if isinstance(val_split, int):
assert fit_classifier == True, \ assert fit_classifier == True, \
@ -305,7 +292,7 @@ class CC(AggregativeQuantifier):
Computes class prevalence estimates by counting the prevalence of each of the predicted labels. Computes class prevalence estimates by counting the prevalence of each of the predicted labels.
:param classif_predictions: array-like with label predictions :param classif_predictions: array-like with label predictions
:return: `np.ndarray` of shape `(self.n_classes_,)` with class prevalence estimates. :return: `np.ndarray` of shape `(n_classes,)` with class prevalence estimates.
""" """
return F.prevalence_from_labels(classif_predictions, self.classes_) return F.prevalence_from_labels(classif_predictions, self.classes_)
@ -328,7 +315,7 @@ class ACC(AggregativeQuantifier):
def __init__(self, classifier: BaseEstimator, val_split=0.4, n_jobs=None): def __init__(self, classifier: BaseEstimator, val_split=0.4, n_jobs=None):
self.classifier = classifier self.classifier = classifier
self.val_split = val_split self.val_split = val_split
self.n_jobs = qp.get_njobs(n_jobs) self.n_jobs = qp._get_njobs(n_jobs)
def fit(self, data: LabelledCollection, fit_classifier=True, val_split: Union[float, int, LabelledCollection] = None): def fit(self, data: LabelledCollection, fit_classifier=True, val_split: Union[float, int, LabelledCollection] = None):
""" """
@ -435,7 +422,7 @@ class PACC(AggregativeProbabilisticQuantifier):
def __init__(self, classifier: BaseEstimator, val_split=0.4, n_jobs=None): def __init__(self, classifier: BaseEstimator, val_split=0.4, n_jobs=None):
self.classifier = classifier self.classifier = classifier
self.val_split = val_split self.val_split = val_split
self.n_jobs = qp.get_njobs(n_jobs) self.n_jobs = qp._get_njobs(n_jobs)
def fit(self, data: LabelledCollection, fit_classifier=True, val_split: Union[float, int, LabelledCollection] = None): def fit(self, data: LabelledCollection, fit_classifier=True, val_split: Union[float, int, LabelledCollection] = None):
""" """
@ -660,6 +647,20 @@ class HDy(AggregativeProbabilisticQuantifier, BinaryQuantifier):
return np.asarray([1 - class1_prev, class1_prev]) return np.asarray([1 - class1_prev, class1_prev])
def _get_divergence(divergence: Union[str, Callable]):
if isinstance(divergence, str):
if divergence=='HD':
return F.HellingerDistance
elif divergence=='topsoe':
return F.TopsoeDistance
else:
raise ValueError(f'unknown divergence {divergence}')
elif callable(divergence):
return divergence
else:
raise ValueError(f'argument "divergence" not understood; use a str or a callable function')
class DyS(AggregativeProbabilisticQuantifier, BinaryQuantifier): class DyS(AggregativeProbabilisticQuantifier, BinaryQuantifier):
""" """
`DyS framework <https://ojs.aaai.org/index.php/AAAI/article/view/4376>`_ (DyS). `DyS framework <https://ojs.aaai.org/index.php/AAAI/article/view/4376>`_ (DyS).
@ -765,25 +766,13 @@ class SMM(AggregativeProbabilisticQuantifier, BinaryQuantifier):
return np.asarray([1 - class1_prev, class1_prev]) return np.asarray([1 - class1_prev, class1_prev])
def _get_divergence(divergence: Union[str, Callable]):
if isinstance(divergence, str):
if divergence=='HD':
return F.HellingerDistance
elif divergence=='topsoe':
return F.TopsoeDistance
else:
raise ValueError(f'unknown divergence {divergence}')
elif callable(divergence):
return divergence
else:
raise ValueError(f'argument "divergence" not understood; use a str or a callable function')
class DistributionMatching(AggregativeProbabilisticQuantifier): class DistributionMatching(AggregativeProbabilisticQuantifier):
""" """
Generic Distribution Matching quantifier for binary or multiclass quantification. Generic Distribution Matching quantifier for binary or multiclass quantification.
This implementation takes the number of bins, the divergence, and the possibility to work on CDF as hyperparameters. This implementation takes the number of bins, the divergence, and the possibility to work on CDF as hyperparameters.
:param classifier: a sklearn's Estimator that generates a probabilistic classifier :param classifier: a `sklearn`'s Estimator that generates a probabilistic classifier
:param val_split: indicates the proportion of data to be used as a stratified held-out validation set to model the :param val_split: indicates the proportion of data to be used as a stratified held-out validation set to model the
validation distribution. validation distribution.
This parameter can be indicated as a real value (between 0 and 1, default 0.4), representing a proportion of This parameter can be indicated as a real value (between 0 and 1, default 0.4), representing a proportion of
@ -799,7 +788,6 @@ class DistributionMatching(AggregativeProbabilisticQuantifier):
""" """
def __init__(self, classifier, val_split=0.4, nbins=8, divergence: Union[str, Callable]='HD', cdf=False, n_jobs=None): def __init__(self, classifier, val_split=0.4, nbins=8, divergence: Union[str, Callable]='HD', cdf=False, n_jobs=None):
self.classifier = classifier self.classifier = classifier
self.val_split = val_split self.val_split = val_split
self.nbins = nbins self.nbins = nbins
@ -1020,7 +1008,7 @@ class ThresholdOptimization(AggregativeQuantifier, BinaryQuantifier):
def __init__(self, classifier: BaseEstimator, val_split=0.4, n_jobs=None): def __init__(self, classifier: BaseEstimator, val_split=0.4, n_jobs=None):
self.classifier = classifier self.classifier = classifier
self.val_split = val_split self.val_split = val_split
self.n_jobs = qp.get_njobs(n_jobs) self.n_jobs = qp._get_njobs(n_jobs)
def fit(self, data: LabelledCollection, fit_classifier=True, val_split: Union[float, int, LabelledCollection] = None): def fit(self, data: LabelledCollection, fit_classifier=True, val_split: Union[float, int, LabelledCollection] = None):
self._check_binary(data, "Threshold Optimization") self._check_binary(data, "Threshold Optimization")
@ -1277,7 +1265,7 @@ class OneVsAll(AggregativeQuantifier):
assert isinstance(self.binary_quantifier, AggregativeQuantifier), \ assert isinstance(self.binary_quantifier, AggregativeQuantifier), \
f'{self.binary_quantifier} does not seem to be of type Aggregative' f'{self.binary_quantifier} does not seem to be of type Aggregative'
self.binary_quantifier = binary_quantifier self.binary_quantifier = binary_quantifier
self.n_jobs = qp.get_njobs(n_jobs) self.n_jobs = qp._get_njobs(n_jobs)
def fit(self, data: LabelledCollection, fit_classifier=True): def fit(self, data: LabelledCollection, fit_classifier=True):
assert not data.binary, \ assert not data.binary, \

23
quapy/method/base.py
View File

@ -32,29 +32,10 @@ class BaseQuantifier(BaseEstimator):
Generate class prevalence estimates for the sample's instances Generate class prevalence estimates for the sample's instances
:param instances: array-like :param instances: array-like
:return: `np.ndarray` of shape `(self.n_classes_,)` with class prevalence estimates. :return: `np.ndarray` of shape `(n_classes,)` with class prevalence estimates.
""" """
... ...
# @abstractmethod
# def set_params(self, **parameters):
# """
# Set the parameters of the quantifier.
#
# :param parameters: dictionary of param-value pairs
# """
# ...
#
# @abstractmethod
# def get_params(self, deep=True):
# """
# Return the current parameters of the quantifier.
#
# :param deep: for compatibility with sklearn
# :return: a dictionary of param-value pairs
# """
# ...
class BinaryQuantifier(BaseQuantifier): class BinaryQuantifier(BaseQuantifier):
""" """
@ -77,7 +58,7 @@ class OneVsAllGeneric:
assert isinstance(binary_quantifier, BaseQuantifier), \ assert isinstance(binary_quantifier, BaseQuantifier), \
f'{binary_quantifier} does not seem to be a Quantifier' f'{binary_quantifier} does not seem to be a Quantifier'
self.binary_quantifier = binary_quantifier self.binary_quantifier = binary_quantifier
self.n_jobs = qp.get_njobs(n_jobs) self.n_jobs = qp._get_njobs(n_jobs)
def fit(self, data: LabelledCollection, **kwargs): def fit(self, data: LabelledCollection, **kwargs):
assert not data.binary, \ assert not data.binary, \

4
quapy/method/meta.py
View File

@ -84,7 +84,7 @@ class Ensemble(BaseQuantifier):
self.red_size = red_size self.red_size = red_size
self.policy = policy self.policy = policy
self.val_split = val_split self.val_split = val_split
self.n_jobs = qp.get_njobs(n_jobs) self.n_jobs = qp._get_njobs(n_jobs)
self.post_proba_fn = None self.post_proba_fn = None
self.verbose = verbose self.verbose = verbose
self.max_sample_size = max_sample_size self.max_sample_size = max_sample_size
@ -165,8 +165,10 @@ class Ensemble(BaseQuantifier):
`Ensemble(Q(GridSearchCV(l)))` with `Q` a quantifier class that has a classifier `l` optimized for `Ensemble(Q(GridSearchCV(l)))` with `Q` a quantifier class that has a classifier `l` optimized for
classification (not recommended). classification (not recommended).
:param deep: for compatibility with scikit-learn
:return: raises an Exception :return: raises an Exception
""" """
raise NotImplementedError() raise NotImplementedError()
def _accuracy_policy(self, error_name): def _accuracy_policy(self, error_name):

27
quapy/method/non_aggregative.py
View File

@ -21,7 +21,6 @@ class MaximumLikelihoodPrevalenceEstimation(BaseQuantifier):
:param data: the training sample :param data: the training sample
:return: self :return: self
""" """
self._classes_ = data.classes_
self.estimated_prevalence = data.prevalence() self.estimated_prevalence = data.prevalence()
return self return self
@ -34,29 +33,3 @@ class MaximumLikelihoodPrevalenceEstimation(BaseQuantifier):
""" """
return self.estimated_prevalence return self.estimated_prevalence
@property
def classes_(self):
"""
Number of classes
:return: integer
"""
return self._classes_
def get_params(self, deep=True):
"""
Does nothing, since this learner has no parameters.
:param deep: for compatibility with sklearn
:return: `None`
"""
return None
def set_params(self, **parameters):
"""
Does nothing, since this learner has no parameters.
:param parameters: dictionary of param-value pairs (ignored)
"""
pass

2
quapy/model_selection.py
View File

@ -49,7 +49,7 @@ class GridSearchQ(BaseQuantifier):
self.protocol = protocol self.protocol = protocol
self.refit = refit self.refit = refit
self.timeout = timeout self.timeout = timeout
self.n_jobs = qp.get_njobs(n_jobs) self.n_jobs = qp._get_njobs(n_jobs)
self.verbose = verbose self.verbose = verbose
self.__check_error(error) self.__check_error(error)
assert isinstance(protocol, AbstractProtocol), 'unknown protocol' assert isinstance(protocol, AbstractProtocol), 'unknown protocol'

69
quapy/protocol.py
View File

@ -11,13 +11,17 @@ from glob import glob
class AbstractProtocol(metaclass=ABCMeta): class AbstractProtocol(metaclass=ABCMeta):
"""
Abstract parent class for sample generation protocols.
"""
@abstractmethod @abstractmethod
def __call__(self): def __call__(self):
""" """
Implements the protocol. Yields one sample at a time Implements the protocol. Yields one sample at a time along with its prevalence
:return: yields one sample at a time :return: yields a tuple `(sample, prev) at a time, where `sample` is a set of instances
and in which `prev` is an `nd.array` with the class prevalence values
""" """
... ...
@ -32,9 +36,10 @@ class AbstractProtocol(metaclass=ABCMeta):
class AbstractStochasticSeededProtocol(AbstractProtocol): class AbstractStochasticSeededProtocol(AbstractProtocol):
""" """
An AbstractStochasticSeededProtocol is a protocol that generates, via any random procedure (e.g., An `AbstractStochasticSeededProtocol` is a protocol that generates, via any random procedure (e.g.,
via random sapling), sequences of `LabelledCollection` samples. The protocol abstraction enforces via random sampling), sequences of :class:`quapy.data.base.LabelledCollection` samples.
the object to be instantiated using a seed, so that the sequence can be completely replicated. The protocol abstraction enforces
the object to be instantiated using a seed, so that the sequence can be fully replicated.
In order to make this functionality possible, the classes extending this abstraction need to In order to make this functionality possible, the classes extending this abstraction need to
implement only two functions, :meth:`samples_parameters` which generates all the parameters implement only two functions, :meth:`samples_parameters` which generates all the parameters
needed for extracting the samples, and :meth:`sample` that, given some parameters as input, needed for extracting the samples, and :meth:`sample` that, given some parameters as input,
@ -128,7 +133,8 @@ class APP(AbstractStochasticSeededProtocol, OnLabelledCollectionProtocol):
combination of prevalence values is indicated by `repeats`. combination of prevalence values is indicated by `repeats`.
:param data: a `LabelledCollection` from which the samples will be drawn :param data: a `LabelledCollection` from which the samples will be drawn
:param sample_size: integer, number of instances in each sample :param sample_size: integer, number of instances in each sample; if None (default) then it is taken from
qp.environ["SAMPLE_SIZE"]. If this is not set, a ValueError exception is raised.
:param n_prevalences: the number of equidistant prevalence points to extract from the [0,1] interval for the :param n_prevalences: the number of equidistant prevalence points to extract from the [0,1] interval for the
grid (default is 21) grid (default is 21)
:param repeats: number of copies for each valid prevalence vector (default is 10) :param repeats: number of copies for each valid prevalence vector (default is 10)
@ -138,10 +144,11 @@ class APP(AbstractStochasticSeededProtocol, OnLabelledCollectionProtocol):
to "labelled_collection" to get instead instances of LabelledCollection to "labelled_collection" to get instead instances of LabelledCollection
""" """
def __init__(self, data:LabelledCollection, sample_size, n_prevalences=21, repeats=10, smooth_limits_epsilon=0, random_state=None, return_type='sample_prev'): def __init__(self, data:LabelledCollection, sample_size=None, n_prevalences=21, repeats=10,
smooth_limits_epsilon=0, random_state=None, return_type='sample_prev'):
super(APP, self).__init__(random_state) super(APP, self).__init__(random_state)
self.data = data self.data = data
self.sample_size = sample_size self.sample_size = qp._get_sample_size(sample_size)
self.n_prevalences = n_prevalences self.n_prevalences = n_prevalences
self.repeats = repeats self.repeats = repeats
self.smooth_limits_epsilon = smooth_limits_epsilon self.smooth_limits_epsilon = smooth_limits_epsilon
@ -191,17 +198,18 @@ class NPP(AbstractStochasticSeededProtocol, OnLabelledCollectionProtocol):
samples uniformly at random, therefore approximately preserving the natural prevalence of the collection. samples uniformly at random, therefore approximately preserving the natural prevalence of the collection.
:param data: a `LabelledCollection` from which the samples will be drawn :param data: a `LabelledCollection` from which the samples will be drawn
:param sample_size: integer, the number of instances in each sample :param sample_size: integer, the number of instances in each sample; if None (default) then it is taken from
qp.environ["SAMPLE_SIZE"]. If this is not set, a ValueError exception is raised.
:param repeats: the number of samples to generate. Default is 100. :param repeats: the number of samples to generate. Default is 100.
:param random_state: allows replicating samples across runs (default None) :param random_state: allows replicating samples across runs (default None)
:param return_type: set to "sample_prev" (default) to get the pairs of (sample, prevalence) at each iteration, or :param return_type: set to "sample_prev" (default) to get the pairs of (sample, prevalence) at each iteration, or
to "labelled_collection" to get instead instances of LabelledCollection to "labelled_collection" to get instead instances of LabelledCollection
""" """
def __init__(self, data:LabelledCollection, sample_size, repeats=100, random_state=None, return_type='sample_prev'): def __init__(self, data:LabelledCollection, sample_size=None, repeats=100, random_state=None, return_type='sample_prev'):
super(NPP, self).__init__(random_state) super(NPP, self).__init__(random_state)
self.data = data self.data = data
self.sample_size = sample_size self.sample_size = qp._get_sample_size(sample_size)
self.repeats = repeats self.repeats = repeats
self.random_state = random_state self.random_state = random_state
self.collator = OnLabelledCollectionProtocol.get_collator(return_type) self.collator = OnLabelledCollectionProtocol.get_collator(return_type)
@ -230,17 +238,19 @@ class USimplexPP(AbstractStochasticSeededProtocol, OnLabelledCollectionProtocol)
combinations of the grid values of APP makes this endeavour intractable. combinations of the grid values of APP makes this endeavour intractable.
:param data: a `LabelledCollection` from which the samples will be drawn :param data: a `LabelledCollection` from which the samples will be drawn
:param sample_size: integer, the number of instances in each sample :param sample_size: integer, the number of instances in each sample; if None (default) then it is taken from
qp.environ["SAMPLE_SIZE"]. If this is not set, a ValueError exception is raised.
:param repeats: the number of samples to generate. Default is 100. :param repeats: the number of samples to generate. Default is 100.
:param random_state: allows replicating samples across runs (default None) :param random_state: allows replicating samples across runs (default None)
:param return_type: set to "sample_prev" (default) to get the pairs of (sample, prevalence) at each iteration, or :param return_type: set to "sample_prev" (default) to get the pairs of (sample, prevalence) at each iteration, or
to "labelled_collection" to get instead instances of LabelledCollection to "labelled_collection" to get instead instances of LabelledCollection
""" """
def __init__(self, data: LabelledCollection, sample_size, repeats=100, random_state=None, return_type='sample_prev'): def __init__(self, data: LabelledCollection, sample_size=None, repeats=100, random_state=None,
return_type='sample_prev'):
super(USimplexPP, self).__init__(random_state) super(USimplexPP, self).__init__(random_state)
self.data = data self.data = data
self.sample_size = sample_size self.sample_size = qp._get_sample_size(sample_size)
self.repeats = repeats self.repeats = repeats
self.random_state = random_state self.random_state = random_state
self.collator = OnLabelledCollectionProtocol.get_collator(return_type) self.collator = OnLabelledCollectionProtocol.get_collator(return_type)
@ -259,32 +269,7 @@ class USimplexPP(AbstractStochasticSeededProtocol, OnLabelledCollectionProtocol)
return self.repeats return self.repeats
# class LoadSamplesFromDirectory(AbstractProtocol): class DomainMixer(AbstractStochasticSeededProtocol):
#
# def __init__(self, folder_path, loader_fn, classes=None, **loader_kwargs):
# assert exists(folder_path), f'folder {folder_path} does not exist'
# assert callable(loader_fn), f'the passed load_fn does not seem to be callable'
# self.folder_path = folder_path
# self.loader_fn = loader_fn
# self.classes = classes
# self.loader_kwargs = loader_kwargs
# self._list_files = None
#
# def __call__(self):
# for file in self.list_files:
# yield LabelledCollection.load(file, loader_func=self.loader_fn, classes=self.classes, **self.loader_kwargs)
#
# @property
# def list_files(self):
# if self._list_files is None:
# self._list_files = sorted(glob(self.folder_path, '*'))
# return self._list_files
#
# def total(self):
# return len(self.list_files)
class CovariateShiftPP(AbstractStochasticSeededProtocol):
""" """
Generates mixtures of two domains (A and B) at controlled rates, but preserving the original class prevalence. Generates mixtures of two domains (A and B) at controlled rates, but preserving the original class prevalence.
@ -311,10 +296,10 @@ class CovariateShiftPP(AbstractStochasticSeededProtocol):
mixture_points=11, mixture_points=11,
random_state=None, random_state=None,
return_type='sample_prev'): return_type='sample_prev'):
super(CovariateShiftPP, self).__init__(random_state) super(DomainMixer, self).__init__(random_state)
self.A = domainA self.A = domainA
self.B = domainB self.B = domainB
self.sample_size = sample_size self.sample_size = qp._get_sample_size(sample_size)
self.repeats = repeats self.repeats = repeats
if prevalence is None: if prevalence is None:
self.prevalence = domainA.prevalence() self.prevalence = domainA.prevalence()

17
quapy/tests/test_methods.py
View File

@ -4,6 +4,7 @@ from sklearn.linear_model import LogisticRegression
from sklearn.svm import LinearSVC from sklearn.svm import LinearSVC
import quapy as qp import quapy as qp
from quapy.method.base import BinaryQuantifier
from quapy.data import Dataset, LabelledCollection from quapy.data import Dataset, LabelledCollection
from quapy.method import AGGREGATIVE_METHODS, NON_AGGREGATIVE_METHODS, EXPLICIT_LOSS_MINIMIZATION_METHODS from quapy.method import AGGREGATIVE_METHODS, NON_AGGREGATIVE_METHODS, EXPLICIT_LOSS_MINIMIZATION_METHODS
from quapy.method.aggregative import ACC, PACC, HDy from quapy.method.aggregative import ACC, PACC, HDy
@ -21,7 +22,7 @@ learners = [LogisticRegression, LinearSVC]
def test_aggregative_methods(dataset: Dataset, aggregative_method, learner): def test_aggregative_methods(dataset: Dataset, aggregative_method, learner):
model = aggregative_method(learner()) model = aggregative_method(learner())
if model.binary and not dataset.binary: if isinstance(model, BinaryQuantifier) and not dataset.binary:
print(f'skipping the test of binary model {type(model)} on non-binary dataset {dataset}') print(f'skipping the test of binary model {type(model)} on non-binary dataset {dataset}')
return return
@ -45,7 +46,7 @@ def test_elm_methods(dataset: Dataset, elm_method):
print('Missing SVMperf binary program, skipping test') print('Missing SVMperf binary program, skipping test')
return return
if model.binary and not dataset.binary: if isinstance(model, BinaryQuantifier) and not dataset.binary:
print(f'skipping the test of binary model {model} on non-binary dataset {dataset}') print(f'skipping the test of binary model {model} on non-binary dataset {dataset}')
return return
@ -64,7 +65,7 @@ def test_elm_methods(dataset: Dataset, elm_method):
def test_non_aggregative_methods(dataset: Dataset, non_aggregative_method): def test_non_aggregative_methods(dataset: Dataset, non_aggregative_method):
model = non_aggregative_method() model = non_aggregative_method()
if model.binary and not dataset.binary: if isinstance(model, BinaryQuantifier) and not dataset.binary:
print(f'skipping the test of binary model {model} on non-binary dataset {dataset}') print(f'skipping the test of binary model {model} on non-binary dataset {dataset}')
return return
@ -85,7 +86,7 @@ def test_non_aggregative_methods(dataset: Dataset, non_aggregative_method):
def test_ensemble_method(base_method, learner, dataset: Dataset, policy): def test_ensemble_method(base_method, learner, dataset: Dataset, policy):
qp.environ['SAMPLE_SIZE'] = len(dataset.training) qp.environ['SAMPLE_SIZE'] = len(dataset.training)
model = Ensemble(quantifier=base_method(learner()), size=5, policy=policy, n_jobs=-1) model = Ensemble(quantifier=base_method(learner()), size=5, policy=policy, n_jobs=-1)
if model.binary and not dataset.binary: if isinstance(model, BinaryQuantifier) and not dataset.binary:
print(f'skipping the test of binary model {model} on non-binary dataset {dataset}') print(f'skipping the test of binary model {model} on non-binary dataset {dataset}')
return return
@ -120,7 +121,7 @@ def test_quanet_method():
from quapy.method.meta import QuaNet from quapy.method.meta import QuaNet
model = QuaNet(learner, sample_size=len(dataset.training), device='cuda') model = QuaNet(learner, sample_size=len(dataset.training), device='cuda')
if model.binary and not dataset.binary: if isinstance(model, BinaryQuantifier) and not dataset.binary:
print(f'skipping the test of binary model {model} on non-binary dataset {dataset}') print(f'skipping the test of binary model {model} on non-binary dataset {dataset}')
return return
@ -138,7 +139,7 @@ def models_to_test_for_str_label_names():
models = list() models = list()
learner = LogisticRegression learner = LogisticRegression
for method in AGGREGATIVE_METHODS.difference(EXPLICIT_LOSS_MINIMIZATION_METHODS): for method in AGGREGATIVE_METHODS.difference(EXPLICIT_LOSS_MINIMIZATION_METHODS):
models.append(method(learner())) models.append(method(learner(random_state=0)))
for method in NON_AGGREGATIVE_METHODS: for method in NON_AGGREGATIVE_METHODS:
models.append(method()) models.append(method())
return models return models
@ -156,6 +157,7 @@ def test_str_label_names(model):
dataset.test.sampling(1000, *dataset.test.prevalence())) dataset.test.sampling(1000, *dataset.test.prevalence()))
qp.data.preprocessing.text2tfidf(dataset, min_df=5, inplace=True) qp.data.preprocessing.text2tfidf(dataset, min_df=5, inplace=True)
numpy.random.seed(0)
model.fit(dataset.training) model.fit(dataset.training)
int_estim_prevalences = model.quantify(dataset.test.instances) int_estim_prevalences = model.quantify(dataset.test.instances)
@ -168,7 +170,8 @@ def test_str_label_names(model):
['one' if label == 1 else 'zero' for label in dataset.training.labels]), ['one' if label == 1 else 'zero' for label in dataset.training.labels]),
LabelledCollection(dataset.test.instances, LabelledCollection(dataset.test.instances,
['one' if label == 1 else 'zero' for label in dataset.test.labels])) ['one' if label == 1 else 'zero' for label in dataset.test.labels]))
assert all(dataset_str.training.classes_ == dataset_str.test.classes_), 'wrong indexation'
numpy.random.seed(0)
model.fit(dataset_str.training) model.fit(dataset_str.training)
str_estim_prevalences = model.quantify(dataset_str.test.instances) str_estim_prevalences = model.quantify(dataset_str.test.instances)

22
quapy/tests/test_modsel.py
View File

@ -5,9 +5,9 @@ from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC from sklearn.svm import SVC
import quapy as qp import quapy as qp
from method.aggregative import PACC from quapy.method.aggregative import PACC
from model_selection import GridSearchQ from quapy.model_selection import GridSearchQ
from protocol import APP from quapy.protocol import APP
import time import time
@ -20,7 +20,7 @@ class ModselTestCase(unittest.TestCase):
data = qp.datasets.fetch_reviews('imdb', tfidf=True, min_df=10) data = qp.datasets.fetch_reviews('imdb', tfidf=True, min_df=10)
training, validation = data.training.split_stratified(0.7, random_state=1) training, validation = data.training.split_stratified(0.7, random_state=1)
param_grid = {'C': np.logspace(-3,3,7)} param_grid = {'classifier__C': np.logspace(-3,3,7)}
app = APP(validation, sample_size=100, random_state=1) app = APP(validation, sample_size=100, random_state=1)
q = GridSearchQ( q = GridSearchQ(
q, param_grid, protocol=app, error='mae', refit=True, timeout=-1, verbose=True q, param_grid, protocol=app, error='mae', refit=True, timeout=-1, verbose=True
@ -28,8 +28,8 @@ class ModselTestCase(unittest.TestCase):
print('best params', q.best_params_) print('best params', q.best_params_)
print('best score', q.best_score_) print('best score', q.best_score_)
self.assertEqual(q.best_params_['C'], 10.0) self.assertEqual(q.best_params_['classifier__C'], 10.0)
self.assertEqual(q.best_model().get_params()['C'], 10.0) self.assertEqual(q.best_model().get_params()['classifier__C'], 10.0)
def test_modsel_parallel(self): def test_modsel_parallel(self):
@ -39,7 +39,7 @@ class ModselTestCase(unittest.TestCase):
training, validation = data.training.split_stratified(0.7, random_state=1) training, validation = data.training.split_stratified(0.7, random_state=1)
# test = data.test # test = data.test
param_grid = {'C': np.logspace(-3,3,7)} param_grid = {'classifier__C': np.logspace(-3,3,7)}
app = APP(validation, sample_size=100, random_state=1) app = APP(validation, sample_size=100, random_state=1)
q = GridSearchQ( q = GridSearchQ(
q, param_grid, protocol=app, error='mae', refit=True, timeout=-1, n_jobs=-1, verbose=True q, param_grid, protocol=app, error='mae', refit=True, timeout=-1, n_jobs=-1, verbose=True
@ -47,8 +47,8 @@ class ModselTestCase(unittest.TestCase):
print('best params', q.best_params_) print('best params', q.best_params_)
print('best score', q.best_score_) print('best score', q.best_score_)
self.assertEqual(q.best_params_['C'], 10.0) self.assertEqual(q.best_params_['classifier__C'], 10.0)
self.assertEqual(q.best_model().get_params()['C'], 10.0) self.assertEqual(q.best_model().get_params()['classifier__C'], 10.0)
def test_modsel_parallel_speedup(self): def test_modsel_parallel_speedup(self):
class SlowLR(LogisticRegression): class SlowLR(LogisticRegression):
@ -61,7 +61,7 @@ class ModselTestCase(unittest.TestCase):
data = qp.datasets.fetch_reviews('imdb', tfidf=True, min_df=10) data = qp.datasets.fetch_reviews('imdb', tfidf=True, min_df=10)
training, validation = data.training.split_stratified(0.7, random_state=1) training, validation = data.training.split_stratified(0.7, random_state=1)
param_grid = {'C': np.logspace(-3, 3, 7)} param_grid = {'classifier__C': np.logspace(-3, 3, 7)}
app = APP(validation, sample_size=100, random_state=1) app = APP(validation, sample_size=100, random_state=1)
tinit = time.time() tinit = time.time()
@ -95,7 +95,7 @@ class ModselTestCase(unittest.TestCase):
training, validation = data.training.split_stratified(0.7, random_state=1) training, validation = data.training.split_stratified(0.7, random_state=1)
# test = data.test # test = data.test
param_grid = {'C': np.logspace(-3,3,7)} param_grid = {'classifier__C': np.logspace(-3,3,7)}
app = APP(validation, sample_size=100, random_state=1) app = APP(validation, sample_size=100, random_state=1)
q = GridSearchQ( q = GridSearchQ(
q, param_grid, protocol=app, error='mae', refit=True, timeout=3, n_jobs=-1, verbose=True q, param_grid, protocol=app, error='mae', refit=True, timeout=3, n_jobs=-1, verbose=True

6
quapy/tests/test_protocols.py
View File

@ -1,7 +1,7 @@
import unittest import unittest
import numpy as np import numpy as np
from quapy.data import LabelledCollection from quapy.data import LabelledCollection
from quapy.protocol import APP, NPP, USimplexPP, CovariateShiftPP, AbstractStochasticSeededProtocol from quapy.protocol import APP, NPP, USimplexPP, DomainMixer, AbstractStochasticSeededProtocol
def mock_labelled_collection(prefix=''): def mock_labelled_collection(prefix=''):
@ -94,7 +94,7 @@ class TestProtocols(unittest.TestCase):
def test_covariate_shift_replicate(self): def test_covariate_shift_replicate(self):
dataA = mock_labelled_collection('domA') dataA = mock_labelled_collection('domA')
dataB = mock_labelled_collection('domB') dataB = mock_labelled_collection('domB')
p = CovariateShiftPP(dataA, dataB, sample_size=10, mixture_points=11, random_state=1) p = DomainMixer(dataA, dataB, sample_size=10, mixture_points=11, random_state=1)
samples1 = samples_to_str(p) samples1 = samples_to_str(p)
samples2 = samples_to_str(p) samples2 = samples_to_str(p)
@ -104,7 +104,7 @@ class TestProtocols(unittest.TestCase):
def test_covariate_shift_not_replicate(self): def test_covariate_shift_not_replicate(self):
dataA = mock_labelled_collection('domA') dataA = mock_labelled_collection('domA')
dataB = mock_labelled_collection('domB') dataB = mock_labelled_collection('domB')
p = CovariateShiftPP(dataA, dataB, sample_size=10, mixture_points=11) p = DomainMixer(dataA, dataB, sample_size=10, mixture_points=11)
samples1 = samples_to_str(p) samples1 = samples_to_str(p)
samples2 = samples_to_str(p) samples2 = samples_to_str(p)

16
quapy/util.py
View File

@ -22,7 +22,7 @@ def _get_parallel_slices(n_tasks, n_jobs):
def map_parallel(func, args, n_jobs): def map_parallel(func, args, n_jobs):
""" """
Applies func to n_jobs slices of args. E.g., if args is an array of 99 items and n_jobs=2, then 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]. func is a function func is applied in two parallel processes to args[0:50] and to args[50:99]. func is a function
that already works with a list of arguments. that already works with a list of arguments.
@ -128,6 +128,7 @@ def create_if_not_exist(path):
def get_quapy_home(): def get_quapy_home():
""" """
Gets the home directory of QuaPy, i.e., the directory where QuaPy saves permanent data, such as dowloaded datasets. Gets the home directory of QuaPy, i.e., the directory where QuaPy saves permanent data, such as dowloaded datasets.
This directory is `~/quapy_data`
:return: a string representing the path :return: a string representing the path
""" """
@ -162,7 +163,7 @@ def save_text_file(path, text):
def pickled_resource(pickle_path:str, generation_func:callable, *args): def pickled_resource(pickle_path:str, generation_func:callable, *args):
""" """
Allows for fast reuse of resources that are generated only once by calling generation_func(*args). The next times Allows for fast reuse of resources that are generated only once by calling generation_func(\\*args). The next times
this function is invoked, it loads the pickled resource. Example: this function is invoked, it loads the pickled resource. Example:
>>> def some_array(n): # a mock resource created with one parameter (`n`) >>> def some_array(n): # a mock resource created with one parameter (`n`)
@ -191,10 +192,6 @@ class EarlyStop:
""" """
A class implementing the early-stopping condition typically used for training neural networks. A class implementing the early-stopping condition typically used for training neural networks.
:param patience: the number of (consecutive) times that a monitored evaluation metric (typically obtaind in a
held-out validation split) can be found to be worse than the best one obtained so far, before flagging the
stopping condition. An instance of this class is `callable`, and is to be used as follows:
>>> earlystop = EarlyStop(patience=2, lower_is_better=True) >>> earlystop = EarlyStop(patience=2, lower_is_better=True)
>>> earlystop(0.9, epoch=0) >>> earlystop(0.9, epoch=0)
>>> earlystop(0.7, epoch=1) >>> earlystop(0.7, epoch=1)
@ -206,14 +203,14 @@ class EarlyStop:
>>> earlystop.best_epoch # is 1 >>> earlystop.best_epoch # is 1
>>> earlystop.best_score # is 0.7 >>> earlystop.best_score # is 0.7
:param patience: the number of (consecutive) times that a monitored evaluation metric (typically obtaind in a
held-out validation split) can be found to be worse than the best one obtained so far, before flagging the
stopping condition. An instance of this class is `callable`, and is to be used as follows:
:param lower_is_better: if True (default) the metric is to be minimized. :param lower_is_better: if True (default) the metric is to be minimized.
:ivar best_score: keeps track of the best value seen so far :ivar best_score: keeps track of the best value seen so far
:ivar best_epoch: keeps track of the epoch in which the best score was set :ivar best_epoch: keeps track of the epoch in which the best score was set
:ivar STOP: flag (boolean) indicating the stopping condition :ivar STOP: flag (boolean) indicating the stopping condition
:ivar IMPROVED: flag (boolean) indicating whether there was an improvement in the last call :ivar IMPROVED: flag (boolean) indicating whether there was an improvement in the last call
""" """
def __init__(self, patience, lower_is_better=True): def __init__(self, patience, lower_is_better=True):
@ -244,3 +241,4 @@ class EarlyStop:
self.patience -= 1 self.patience -= 1
if self.patience <= 0: if self.patience <= 0:
self.STOP = True self.STOP = True