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@ -49,8 +52,8 @@
<div class="bodywrapper">
<div class="body" role="main">
<div class="tex2jax_ignore mathjax_ignore section" id="datasets">
<h1>Datasets<a class="headerlink" href="#datasets" title="Permalink to this headline"></a></h1>
<section id="datasets">
<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
quantification literature, as well as an interface to allow
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
further details on how to use the artificial sampling protocol to properly
evaluate a quantification method.</p>
<div class="section" id="reviews-datasets">
<h2>Reviews Datasets<a class="headerlink" href="#reviews-datasets" title="Permalink to this headline"></a></h2>
<section id="reviews-datasets">
<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 Potters series, and
the well-known IMDb movie reviews can be fetched using a unified interface.
For example:</p>
@ -150,47 +153,47 @@ For example:</p>
</pre></div>
</div>
<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>
<tr class="row-odd"><th class="head"><p>Dataset</p></th>
<th class="text-align:center head"><p>classes</p></th>
<th class="text-align:center head"><p>train size</p></th>
<th class="text-align:center head"><p>test size</p></th>
<th class="text-align:center head"><p>train prev</p></th>
<th class="text-align:center head"><p>test prev</p></th>
<th class="head text-center"><p>classes</p></th>
<th class="head text-center"><p>train size</p></th>
<th class="head text-center"><p>test size</p></th>
<th class="head text-center"><p>train prev</p></th>
<th class="head text-center"><p>test prev</p></th>
<th class="head"><p>type</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>hp</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>9533</p></td>
<td class="text-align:center"><p>18399</p></td>
<td class="text-align: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>2</p></td>
<td class="text-center"><p>9533</p></td>
<td class="text-center"><p>18399</p></td>
<td class="text-center"><p>[0.018, 0.982]</p></td>
<td class="text-center"><p>[0.065, 0.935]</p></td>
<td><p>text</p></td>
</tr>
<tr class="row-odd"><td><p>kindle</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>3821</p></td>
<td class="text-align:center"><p>21591</p></td>
<td class="text-align: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>2</p></td>
<td class="text-center"><p>3821</p></td>
<td class="text-center"><p>21591</p></td>
<td class="text-center"><p>[0.081, 0.919]</p></td>
<td class="text-center"><p>[0.063, 0.937]</p></td>
<td><p>text</p></td>
</tr>
<tr class="row-even"><td><p>imdb</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>25000</p></td>
<td class="text-align:center"><p>25000</p></td>
<td class="text-align: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>2</p></td>
<td class="text-center"><p>25000</p></td>
<td class="text-center"><p>25000</p></td>
<td class="text-center"><p>[0.500, 0.500]</p></td>
<td class="text-center"><p>[0.500, 0.500]</p></td>
<td><p>text</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="twitter-sentiment-datasets">
<h2>Twitter Sentiment Datasets<a class="headerlink" href="#twitter-sentiment-datasets" title="Permalink to this headline"></a></h2>
</section>
<section id="twitter-sentiment-datasets">
<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.
Text is not accessible, and the documents were made available
in tf-idf format. Each dataset presents two splits: a train/val
@ -221,123 +224,123 @@ The lists of the Twitter datasets ids can be consulted in:</p>
</pre></div>
</div>
<p>Some details can be found below:</p>
<table class="colwidths-auto docutils align-default">
<table class="docutils align-default">
<thead>
<tr class="row-odd"><th class="head"><p>Dataset</p></th>
<th class="text-align:center head"><p>classes</p></th>
<th class="text-align:center head"><p>train size</p></th>
<th class="text-align:center head"><p>test size</p></th>
<th class="text-align:center head"><p>features</p></th>
<th class="text-align:center head"><p>train prev</p></th>
<th class="text-align:center head"><p>test prev</p></th>
<th class="head text-center"><p>classes</p></th>
<th class="head text-center"><p>train size</p></th>
<th class="head text-center"><p>test size</p></th>
<th class="head text-center"><p>features</p></th>
<th class="head text-center"><p>train prev</p></th>
<th class="head text-center"><p>test prev</p></th>
<th class="head"><p>type</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>gasp</p></td>
<td class="text-align:center"><p>3</p></td>
<td class="text-align:center"><p>8788</p></td>
<td class="text-align:center"><p>3765</p></td>
<td class="text-align:center"><p>694582</p></td>
<td class="text-align:center"><p>[0.421, 0.496, 0.082]</p></td>
<td class="text-align:center"><p>[0.407, 0.507, 0.086]</p></td>
<td class="text-center"><p>3</p></td>
<td class="text-center"><p>8788</p></td>
<td class="text-center"><p>3765</p></td>
<td class="text-center"><p>694582</p></td>
<td class="text-center"><p>[0.421, 0.496, 0.082]</p></td>
<td class="text-center"><p>[0.407, 0.507, 0.086]</p></td>
<td><p>sparse</p></td>
</tr>
<tr class="row-odd"><td><p>hcr</p></td>
<td class="text-align:center"><p>3</p></td>
<td class="text-align:center"><p>1594</p></td>
<td class="text-align:center"><p>798</p></td>
<td class="text-align:center"><p>222046</p></td>
<td class="text-align:center"><p>[0.546, 0.211, 0.243]</p></td>
<td class="text-align:center"><p>[0.640, 0.167, 0.193]</p></td>
<td class="text-center"><p>3</p></td>
<td class="text-center"><p>1594</p></td>
<td class="text-center"><p>798</p></td>
<td class="text-center"><p>222046</p></td>
<td class="text-center"><p>[0.546, 0.211, 0.243]</p></td>
<td class="text-center"><p>[0.640, 0.167, 0.193]</p></td>
<td><p>sparse</p></td>
</tr>
<tr class="row-even"><td><p>omd</p></td>
<td class="text-align:center"><p>3</p></td>
<td class="text-align:center"><p>1839</p></td>
<td class="text-align:center"><p>787</p></td>
<td class="text-align:center"><p>199151</p></td>
<td class="text-align:center"><p>[0.463, 0.271, 0.266]</p></td>
<td class="text-align:center"><p>[0.437, 0.283, 0.280]</p></td>
<td class="text-center"><p>3</p></td>
<td class="text-center"><p>1839</p></td>
<td class="text-center"><p>787</p></td>
<td class="text-center"><p>199151</p></td>
<td class="text-center"><p>[0.463, 0.271, 0.266]</p></td>
<td class="text-center"><p>[0.437, 0.283, 0.280]</p></td>
<td><p>sparse</p></td>
</tr>
<tr class="row-odd"><td><p>sanders</p></td>
<td class="text-align:center"><p>3</p></td>
<td class="text-align:center"><p>2155</p></td>
<td class="text-align:center"><p>923</p></td>
<td class="text-align:center"><p>229399</p></td>
<td class="text-align:center"><p>[0.161, 0.691, 0.148]</p></td>
<td class="text-align:center"><p>[0.164, 0.688, 0.148]</p></td>
<td class="text-center"><p>3</p></td>
<td class="text-center"><p>2155</p></td>
<td class="text-center"><p>923</p></td>
<td class="text-center"><p>229399</p></td>
<td class="text-center"><p>[0.161, 0.691, 0.148]</p></td>
<td class="text-center"><p>[0.164, 0.688, 0.148]</p></td>
<td><p>sparse</p></td>
</tr>
<tr class="row-even"><td><p>semeval13</p></td>
<td class="text-align:center"><p>3</p></td>
<td class="text-align:center"><p>11338</p></td>
<td class="text-align:center"><p>3813</p></td>
<td class="text-align:center"><p>1215742</p></td>
<td class="text-align:center"><p>[0.159, 0.470, 0.372]</p></td>
<td class="text-align:center"><p>[0.158, 0.430, 0.412]</p></td>
<td class="text-center"><p>3</p></td>
<td class="text-center"><p>11338</p></td>
<td class="text-center"><p>3813</p></td>
<td class="text-center"><p>1215742</p></td>
<td class="text-center"><p>[0.159, 0.470, 0.372]</p></td>
<td class="text-center"><p>[0.158, 0.430, 0.412]</p></td>
<td><p>sparse</p></td>
</tr>
<tr class="row-odd"><td><p>semeval14</p></td>
<td class="text-align:center"><p>3</p></td>
<td class="text-align:center"><p>11338</p></td>
<td class="text-align:center"><p>1853</p></td>
<td class="text-align:center"><p>1215742</p></td>
<td class="text-align:center"><p>[0.159, 0.470, 0.372]</p></td>
<td class="text-align:center"><p>[0.109, 0.361, 0.530]</p></td>
<td class="text-center"><p>3</p></td>
<td class="text-center"><p>11338</p></td>
<td class="text-center"><p>1853</p></td>
<td class="text-center"><p>1215742</p></td>
<td class="text-center"><p>[0.159, 0.470, 0.372]</p></td>
<td class="text-center"><p>[0.109, 0.361, 0.530]</p></td>
<td><p>sparse</p></td>
</tr>
<tr class="row-even"><td><p>semeval15</p></td>
<td class="text-align:center"><p>3</p></td>
<td class="text-align:center"><p>11338</p></td>
<td class="text-align:center"><p>2390</p></td>
<td class="text-align:center"><p>1215742</p></td>
<td class="text-align:center"><p>[0.159, 0.470, 0.372]</p></td>
<td class="text-align:center"><p>[0.153, 0.413, 0.434]</p></td>
<td class="text-center"><p>3</p></td>
<td class="text-center"><p>11338</p></td>
<td class="text-center"><p>2390</p></td>
<td class="text-center"><p>1215742</p></td>
<td class="text-center"><p>[0.159, 0.470, 0.372]</p></td>
<td class="text-center"><p>[0.153, 0.413, 0.434]</p></td>
<td><p>sparse</p></td>
</tr>
<tr class="row-odd"><td><p>semeval16</p></td>
<td class="text-align:center"><p>3</p></td>
<td class="text-align:center"><p>8000</p></td>
<td class="text-align:center"><p>2000</p></td>
<td class="text-align:center"><p>889504</p></td>
<td class="text-align:center"><p>[0.157, 0.351, 0.492]</p></td>
<td class="text-align:center"><p>[0.163, 0.341, 0.497]</p></td>
<td class="text-center"><p>3</p></td>
<td class="text-center"><p>8000</p></td>
<td class="text-center"><p>2000</p></td>
<td class="text-center"><p>889504</p></td>
<td class="text-center"><p>[0.157, 0.351, 0.492]</p></td>
<td class="text-center"><p>[0.163, 0.341, 0.497]</p></td>
<td><p>sparse</p></td>
</tr>
<tr class="row-even"><td><p>sst</p></td>
<td class="text-align:center"><p>3</p></td>
<td class="text-align:center"><p>2971</p></td>
<td class="text-align:center"><p>1271</p></td>
<td class="text-align:center"><p>376132</p></td>
<td class="text-align:center"><p>[0.261, 0.452, 0.288]</p></td>
<td class="text-align:center"><p>[0.207, 0.481, 0.312]</p></td>
<td class="text-center"><p>3</p></td>
<td class="text-center"><p>2971</p></td>
<td class="text-center"><p>1271</p></td>
<td class="text-center"><p>376132</p></td>
<td class="text-center"><p>[0.261, 0.452, 0.288]</p></td>
<td class="text-center"><p>[0.207, 0.481, 0.312]</p></td>
<td><p>sparse</p></td>
</tr>
<tr class="row-odd"><td><p>wa</p></td>
<td class="text-align:center"><p>3</p></td>
<td class="text-align:center"><p>2184</p></td>
<td class="text-align:center"><p>936</p></td>
<td class="text-align:center"><p>248563</p></td>
<td class="text-align:center"><p>[0.305, 0.414, 0.281]</p></td>
<td class="text-align:center"><p>[0.282, 0.446, 0.272]</p></td>
<td class="text-center"><p>3</p></td>
<td class="text-center"><p>2184</p></td>
<td class="text-center"><p>936</p></td>
<td class="text-center"><p>248563</p></td>
<td class="text-center"><p>[0.305, 0.414, 0.281]</p></td>
<td class="text-center"><p>[0.282, 0.446, 0.272]</p></td>
<td><p>sparse</p></td>
</tr>
<tr class="row-even"><td><p>wb</p></td>
<td class="text-align:center"><p>3</p></td>
<td class="text-align:center"><p>4259</p></td>
<td class="text-align:center"><p>1823</p></td>
<td class="text-align:center"><p>404333</p></td>
<td class="text-align:center"><p>[0.270, 0.392, 0.337]</p></td>
<td class="text-align:center"><p>[0.274, 0.392, 0.335]</p></td>
<td class="text-center"><p>3</p></td>
<td class="text-center"><p>4259</p></td>
<td class="text-center"><p>1823</p></td>
<td class="text-center"><p>404333</p></td>
<td class="text-center"><p>[0.270, 0.392, 0.337]</p></td>
<td class="text-center"><p>[0.274, 0.392, 0.335]</p></td>
<td><p>sparse</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="uci-machine-learning">
<h2>UCI Machine Learning<a class="headerlink" href="#uci-machine-learning" title="Permalink to this headline"></a></h2>
</section>
<section id="uci-machine-learning">
<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>
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>
@ -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>All datasets come in numerical form (dense matrices); some statistics
are summarized below.</p>
<table class="colwidths-auto docutils align-default">
<table class="docutils align-default">
<thead>
<tr class="row-odd"><th class="head"><p>Dataset</p></th>
<th class="text-align:center head"><p>classes</p></th>
<th class="text-align:center head"><p>instances</p></th>
<th class="text-align:center head"><p>features</p></th>
<th class="text-align:center head"><p>prev</p></th>
<th class="head text-center"><p>classes</p></th>
<th class="head text-center"><p>instances</p></th>
<th class="head text-center"><p>features</p></th>
<th class="head text-center"><p>prev</p></th>
<th class="head"><p>type</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>acute.a</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>120</p></td>
<td class="text-align:center"><p>6</p></td>
<td class="text-align:center"><p>[0.508, 0.492]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>120</p></td>
<td class="text-center"><p>6</p></td>
<td class="text-center"><p>[0.508, 0.492]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-odd"><td><p>acute.b</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>120</p></td>
<td class="text-align:center"><p>6</p></td>
<td class="text-align:center"><p>[0.583, 0.417]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>120</p></td>
<td class="text-center"><p>6</p></td>
<td class="text-center"><p>[0.583, 0.417]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-even"><td><p>balance.1</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>625</p></td>
<td class="text-align:center"><p>4</p></td>
<td class="text-align:center"><p>[0.539, 0.461]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>625</p></td>
<td class="text-center"><p>4</p></td>
<td class="text-center"><p>[0.539, 0.461]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-odd"><td><p>balance.2</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>625</p></td>
<td class="text-align:center"><p>4</p></td>
<td class="text-align:center"><p>[0.922, 0.078]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>625</p></td>
<td class="text-center"><p>4</p></td>
<td class="text-center"><p>[0.922, 0.078]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-even"><td><p>balance.3</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>625</p></td>
<td class="text-align:center"><p>4</p></td>
<td class="text-align:center"><p>[0.539, 0.461]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>625</p></td>
<td class="text-center"><p>4</p></td>
<td class="text-center"><p>[0.539, 0.461]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-odd"><td><p>breast-cancer</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>683</p></td>
<td class="text-align:center"><p>9</p></td>
<td class="text-align:center"><p>[0.350, 0.650]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>683</p></td>
<td class="text-center"><p>9</p></td>
<td class="text-center"><p>[0.350, 0.650]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-even"><td><p>cmc.1</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>1473</p></td>
<td class="text-align:center"><p>9</p></td>
<td class="text-align:center"><p>[0.573, 0.427]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>1473</p></td>
<td class="text-center"><p>9</p></td>
<td class="text-center"><p>[0.573, 0.427]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-odd"><td><p>cmc.2</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>1473</p></td>
<td class="text-align:center"><p>9</p></td>
<td class="text-align:center"><p>[0.774, 0.226]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>1473</p></td>
<td class="text-center"><p>9</p></td>
<td class="text-center"><p>[0.774, 0.226]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-even"><td><p>cmc.3</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>1473</p></td>
<td class="text-align:center"><p>9</p></td>
<td class="text-align:center"><p>[0.653, 0.347]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>1473</p></td>
<td class="text-center"><p>9</p></td>
<td class="text-center"><p>[0.653, 0.347]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-odd"><td><p>ctg.1</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>2126</p></td>
<td class="text-align:center"><p>22</p></td>
<td class="text-align:center"><p>[0.222, 0.778]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>2126</p></td>
<td class="text-center"><p>22</p></td>
<td class="text-center"><p>[0.222, 0.778]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-even"><td><p>ctg.2</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>2126</p></td>
<td class="text-align:center"><p>22</p></td>
<td class="text-align:center"><p>[0.861, 0.139]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>2126</p></td>
<td class="text-center"><p>22</p></td>
<td class="text-center"><p>[0.861, 0.139]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-odd"><td><p>ctg.3</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>2126</p></td>
<td class="text-align:center"><p>22</p></td>
<td class="text-align:center"><p>[0.917, 0.083]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>2126</p></td>
<td class="text-center"><p>22</p></td>
<td class="text-center"><p>[0.917, 0.083]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-even"><td><p>german</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>1000</p></td>
<td class="text-align:center"><p>24</p></td>
<td class="text-align:center"><p>[0.300, 0.700]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>1000</p></td>
<td class="text-center"><p>24</p></td>
<td class="text-center"><p>[0.300, 0.700]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-odd"><td><p>haberman</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>306</p></td>
<td class="text-align:center"><p>3</p></td>
<td class="text-align:center"><p>[0.735, 0.265]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>306</p></td>
<td class="text-center"><p>3</p></td>
<td class="text-center"><p>[0.735, 0.265]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-even"><td><p>ionosphere</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>351</p></td>
<td class="text-align:center"><p>34</p></td>
<td class="text-align:center"><p>[0.641, 0.359]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>351</p></td>
<td class="text-center"><p>34</p></td>
<td class="text-center"><p>[0.641, 0.359]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-odd"><td><p>iris.1</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>150</p></td>
<td class="text-align:center"><p>4</p></td>
<td class="text-align:center"><p>[0.667, 0.333]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>150</p></td>
<td class="text-center"><p>4</p></td>
<td class="text-center"><p>[0.667, 0.333]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-even"><td><p>iris.2</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>150</p></td>
<td class="text-align:center"><p>4</p></td>
<td class="text-align:center"><p>[0.667, 0.333]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>150</p></td>
<td class="text-center"><p>4</p></td>
<td class="text-center"><p>[0.667, 0.333]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-odd"><td><p>iris.3</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>150</p></td>
<td class="text-align:center"><p>4</p></td>
<td class="text-align:center"><p>[0.667, 0.333]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>150</p></td>
<td class="text-center"><p>4</p></td>
<td class="text-center"><p>[0.667, 0.333]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-even"><td><p>mammographic</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>830</p></td>
<td class="text-align:center"><p>5</p></td>
<td class="text-align:center"><p>[0.514, 0.486]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>830</p></td>
<td class="text-center"><p>5</p></td>
<td class="text-center"><p>[0.514, 0.486]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-odd"><td><p>pageblocks.5</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>5473</p></td>
<td class="text-align:center"><p>10</p></td>
<td class="text-align:center"><p>[0.979, 0.021]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>5473</p></td>
<td class="text-center"><p>10</p></td>
<td class="text-center"><p>[0.979, 0.021]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-even"><td><p>semeion</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>1593</p></td>
<td class="text-align:center"><p>256</p></td>
<td class="text-align:center"><p>[0.901, 0.099]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>1593</p></td>
<td class="text-center"><p>256</p></td>
<td class="text-center"><p>[0.901, 0.099]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-odd"><td><p>sonar</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>208</p></td>
<td class="text-align:center"><p>60</p></td>
<td class="text-align:center"><p>[0.534, 0.466]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>208</p></td>
<td class="text-center"><p>60</p></td>
<td class="text-center"><p>[0.534, 0.466]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-even"><td><p>spambase</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>4601</p></td>
<td class="text-align:center"><p>57</p></td>
<td class="text-align:center"><p>[0.606, 0.394]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>4601</p></td>
<td class="text-center"><p>57</p></td>
<td class="text-center"><p>[0.606, 0.394]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-odd"><td><p>spectf</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>267</p></td>
<td class="text-align:center"><p>44</p></td>
<td class="text-align:center"><p>[0.794, 0.206]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>267</p></td>
<td class="text-center"><p>44</p></td>
<td class="text-center"><p>[0.794, 0.206]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-even"><td><p>tictactoe</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>958</p></td>
<td class="text-align:center"><p>9</p></td>
<td class="text-align:center"><p>[0.653, 0.347]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>958</p></td>
<td class="text-center"><p>9</p></td>
<td class="text-center"><p>[0.653, 0.347]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-odd"><td><p>transfusion</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>748</p></td>
<td class="text-align:center"><p>4</p></td>
<td class="text-align:center"><p>[0.762, 0.238]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>748</p></td>
<td class="text-center"><p>4</p></td>
<td class="text-center"><p>[0.762, 0.238]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-even"><td><p>wdbc</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>569</p></td>
<td class="text-align:center"><p>30</p></td>
<td class="text-align:center"><p>[0.627, 0.373]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>569</p></td>
<td class="text-center"><p>30</p></td>
<td class="text-center"><p>[0.627, 0.373]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-odd"><td><p>wine.1</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>178</p></td>
<td class="text-align:center"><p>13</p></td>
<td class="text-align:center"><p>[0.669, 0.331]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>178</p></td>
<td class="text-center"><p>13</p></td>
<td class="text-center"><p>[0.669, 0.331]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-even"><td><p>wine.2</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>178</p></td>
<td class="text-align:center"><p>13</p></td>
<td class="text-align:center"><p>[0.601, 0.399]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>178</p></td>
<td class="text-center"><p>13</p></td>
<td class="text-center"><p>[0.601, 0.399]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-odd"><td><p>wine.3</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>178</p></td>
<td class="text-align:center"><p>13</p></td>
<td class="text-align:center"><p>[0.730, 0.270]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>178</p></td>
<td class="text-center"><p>13</p></td>
<td class="text-center"><p>[0.730, 0.270]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-even"><td><p>wine-q-red</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>1599</p></td>
<td class="text-align:center"><p>11</p></td>
<td class="text-align:center"><p>[0.465, 0.535]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>1599</p></td>
<td class="text-center"><p>11</p></td>
<td class="text-center"><p>[0.465, 0.535]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-odd"><td><p>wine-q-white</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>4898</p></td>
<td class="text-align:center"><p>11</p></td>
<td class="text-align:center"><p>[0.335, 0.665]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>4898</p></td>
<td class="text-center"><p>11</p></td>
<td class="text-center"><p>[0.335, 0.665]</p></td>
<td><p>dense</p></td>
</tr>
<tr class="row-even"><td><p>yeast</p></td>
<td class="text-align:center"><p>2</p></td>
<td class="text-align:center"><p>1484</p></td>
<td class="text-align:center"><p>8</p></td>
<td class="text-align:center"><p>[0.711, 0.289]</p></td>
<td class="text-center"><p>2</p></td>
<td class="text-center"><p>1484</p></td>
<td class="text-center"><p>8</p></td>
<td class="text-center"><p>[0.711, 0.289]</p></td>
<td><p>dense</p></td>
</tr>
</tbody>
</table>
<div class="section" id="issues">
<h3>Issues:<a class="headerlink" href="#issues" title="Permalink to this headline"></a></h3>
<section id="issues">
<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
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
@ -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
time the dataset is invoked.</p></li>
</ul>
</div>
</div>
<div class="section" id="adding-custom-datasets">
<h2>Adding Custom Datasets<a class="headerlink" href="#adding-custom-datasets" title="Permalink to this headline"></a></h2>
</section>
</section>
<section id="adding-custom-datasets">
<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
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>
@ -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>
</pre></div>
</div>
<div class="section" id="data-processing">
<h3>Data Processing<a class="headerlink" href="#data-processing" title="Permalink to this headline"></a></h3>
<section id="data-processing">
<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>
<ul class="simple">
<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>
<li><p><em>index</em>: transforms textual tokens into lists of numeric ids)</p></li>
</ul>
</div>
</div>
</div>
</section>
</section>
</section>
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@ -694,8 +697,9 @@ that the column values have zero mean and unit variance).</p></li>
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<li><a class="reference internal" href="#reviews-datasets">Reviews Datasets</a></li>
<li><a class="reference internal" href="#twitter-sentiment-datasets">Twitter Sentiment Datasets</a></li>
@ -711,12 +715,17 @@ that the column values have zero mean and unit variance).</p></li>
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@ -49,8 +52,8 @@
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<div class="tex2jax_ignore mathjax_ignore section" id="evaluation">
<h1>Evaluation<a class="headerlink" href="#evaluation" title="Permalink to this headline"></a></h1>
<section id="evaluation">
<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,
and particularly in scenarios of prior-probability shift.
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).
In brief, quantification requires dedicated evaluation protocols,
which are implemented in QuaPy and explained here.</p>
<div class="section" id="error-measures">
<h2>Error Measures<a class="headerlink" href="#error-measures" title="Permalink to this headline"></a></h2>
<section id="error-measures">
<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>
<ul class="simple">
<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>
</pre></div>
</div>
</div>
<div class="section" id="evaluation-protocols">
<h2>Evaluation Protocols<a class="headerlink" href="#evaluation-protocols" title="Permalink to this headline"></a></h2>
</section>
<section id="evaluation-protocols">
<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”,
according to which a test set is used to generate samplings at
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>
</ul>
<p>See the documentation for further details.</p>
</div>
</div>
</section>
</section>
<div class="clearer"></div>
@ -264,8 +267,9 @@ true prevalences and the estimated prevalences.</p></li>
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<li><a class="reference internal" href="#">Evaluation</a><ul>
<li><a class="reference internal" href="#error-measures">Error Measures</a></li>
<li><a class="reference internal" href="#evaluation-protocols">Evaluation Protocols</a></li>
@ -273,12 +277,17 @@ true prevalences and the estimated prevalences.</p></li>
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<section id="installation">
<h1>Installation<a class="headerlink" href="#installation" title="Permalink to this heading"></a></h1>
<p>QuaPy can be easily installed via <cite>pip</cite></p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">quapy</span>
</pre></div>
</div>
<p>See <a class="reference external" href="https://pypi.org/project/QuaPy/">pip page</a> for older versions.</p>
<div class="section" id="requirements">
<h2>Requirements<a class="headerlink" href="#requirements" title="Permalink to this headline"></a></h2>
<section id="requirements">
<h2>Requirements<a class="headerlink" href="#requirements" title="Permalink to this heading"></a></h2>
<ul class="simple">
<li><p>scikit-learn, numpy, scipy</p></li>
<li><p>pytorch (for QuaNet)</p></li>
@ -67,9 +70,9 @@
<li><p>pandas, xlrd</p></li>
<li><p>matplotlib</p></li>
</ul>
</div>
<div class="section" id="svm-perf-with-quantification-oriented-losses">
<h2>SVM-perf with quantification-oriented losses<a class="headerlink" href="#svm-perf-with-quantification-oriented-losses" title="Permalink to this headline"></a></h2>
</section>
<section id="svm-perf-with-quantification-oriented-losses">
<h2>SVM-perf with quantification-oriented losses<a class="headerlink" href="#svm-perf-with-quantification-oriented-losses" title="Permalink to this heading"></a></h2>
<p>In order to run experiments involving SVM(Q), SVM(KLD), SVM(NKLD),
SVM(AE), or SVM(RAE), you have to first download the
<a class="reference external" href="http://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html">svmperf</a>
@ -96,8 +99,8 @@ and for the <cite>KLD</cite> and <cite>NKLD</cite> as proposed by
for quantification.
This patch extends the former by also allowing SVMperf to optimize for
<cite>AE</cite> and <cite>RAE</cite>.</p>
</div>
</div>
</section>
</section>
<div class="clearer"></div>
@ -106,8 +109,9 @@ This patch extends the former by also allowing SVMperf to optimize for
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<li><a class="reference internal" href="#requirements">Requirements</a></li>
<li><a class="reference internal" href="#svm-perf-with-quantification-oriented-losses">SVM-perf with quantification-oriented losses</a></li>
@ -115,12 +119,17 @@ This patch extends the former by also allowing SVMperf to optimize for
</li>
</ul>
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@ -34,12 +37,12 @@
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@ -49,8 +52,8 @@
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<div class="tex2jax_ignore mathjax_ignore section" id="quantification-methods">
<h1>Quantification Methods<a class="headerlink" href="#quantification-methods" title="Permalink to this headline"></a></h1>
<section id="quantification-methods">
<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
<em>aggregative</em> and <em>non-aggregative</em> groups.
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
<a class="reference external" href="https://github.com/HLT-ISTI/QuaPy/wiki/Model-Selection">model selector</a>
to automate the process of hyperparameter search.</p>
<div class="section" id="aggregative-methods">
<h2>Aggregative Methods<a class="headerlink" href="#aggregative-methods" title="Permalink to this headline"></a></h2>
<section id="aggregative-methods">
<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
<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>.
@ -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
quantification over samples, as is customarily done in model selection or
in evaluation.</p>
<div class="section" id="the-classify-count-variants">
<h3>The Classify &amp; Count variants<a class="headerlink" href="#the-classify-count-variants" title="Permalink to this headline"></a></h3>
<section id="the-classify-count-variants">
<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>
<ul class="simple">
<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>
<p>Lastly, everything we said aboud ACC and PCC
applies to PACC as well.</p>
</div>
<div class="section" id="expectation-maximization-emq">
<h3>Expectation Maximization (EMQ)<a class="headerlink" href="#expectation-maximization-emq" title="Permalink to this headline"></a></h3>
</section>
<section id="expectation-maximization-emq">
<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
the SLD, is available at <em>qp.method.aggregative.EMQ</em> or via the
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>
</pre></div>
</div>
</div>
<div class="section" id="hellinger-distance-y-hdy">
<h3>Hellinger Distance y (HDy)<a class="headerlink" href="#hellinger-distance-y-hdy" title="Permalink to this headline"></a></h3>
</section>
<section id="hellinger-distance-y-hdy">
<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><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
@ -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>
</pre></div>
</div>
</div>
<div class="section" id="explicit-loss-minimization">
<h3>Explicit Loss Minimization<a class="headerlink" href="#explicit-loss-minimization" title="Permalink to this headline"></a></h3>
</section>
<section id="explicit-loss-minimization">
<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
based on structured output learning, i.e., quantifiers relying on
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>
</pre></div>
</div>
</div>
</div>
<div class="section" id="meta-models">
<h2>Meta Models<a class="headerlink" href="#meta-models" title="Permalink to this headline"></a></h2>
</section>
</section>
<section id="meta-models">
<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
quantification methods, and that thus do not squarely belong to the aggregative nor
the non-aggregative group (indeed, <em>meta</em> models could use quantifiers from any of those
groups).
<em>Meta</em> models are implemented in the <em>qp.method.meta</em> module.</p>
<div class="section" id="ensembles">
<h3>Ensembles<a class="headerlink" href="#ensembles" title="Permalink to this headline"></a></h3>
<section id="ensembles">
<h3>Ensembles<a class="headerlink" href="#ensembles" title="Permalink to this heading"></a></h3>
<p>QuaPy implements (some of) the variants proposed in:</p>
<ul class="simple">
<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>
<p>Please, check the <a class="reference external" href="https://github.com/HLT-ISTI/QuaPy/wiki/Model-Selection">model selection</a>
wiki if you want to optimize the hyperparameters of ensemble for classification or quantification.</p>
</div>
<div class="section" id="the-quanet-neural-network">
<h3>The QuaNet neural network<a class="headerlink" href="#the-quanet-neural-network" title="Permalink to this headline"></a></h3>
</section>
<section id="the-quanet-neural-network">
<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><em>Esuli, A., Moreo, A., &amp; Sebastiani, F. (2018, October).
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>
</pre></div>
</div>
</div>
</div>
</div>
</section>
</section>
</section>
<div class="clearer"></div>
@ -436,8 +439,9 @@ In the following example, we show an instantiation of QuaNet that instead uses C
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<li><a class="reference internal" href="#">Quantification Methods</a><ul>
<li><a class="reference internal" href="#aggregative-methods">Aggregative Methods</a><ul>
<li><a class="reference internal" href="#the-classify-count-variants">The Classify &amp; Count variants</a></li>
@ -455,12 +459,17 @@ In the following example, we show an instantiation of QuaNet that instead uses C
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@ -492,18 +501,18 @@ In the following example, we show an instantiation of QuaNet that instead uses C
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@ -2,21 +2,26 @@
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@ -31,7 +36,13 @@
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@ -41,8 +52,8 @@
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<div class="tex2jax_ignore mathjax_ignore section" id="model-selection">
<h1>Model Selection<a class="headerlink" href="#model-selection" title="Permalink to this headline"></a></h1>
<section id="model-selection">
<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
can strongly depend on a good choice of model hyper-parameters.
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
best in a held-out validation set in terms of any given
evaluation measure.</p>
<div class="section" id="targeting-a-quantification-oriented-loss">
<h2>Targeting a Quantification-oriented loss<a class="headerlink" href="#targeting-a-quantification-oriented-loss" title="Permalink to this headline"></a></h2>
<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 heading"></a></h2>
<p>The task being optimized determines the evaluation protocol,
i.e., the criteria according to which the performance of
any given method for solving is to be assessed.
@ -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
on the specific data split to be used across different model selection
experiments.</p>
</div>
<div class="section" id="targeting-a-classification-oriented-loss">
<h2>Targeting a Classification-oriented loss<a class="headerlink" href="#targeting-a-classification-oriented-loss" title="Permalink to this headline"></a></h2>
</section>
<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 heading"></a></h2>
<p>Optimizing a model for quantification could rather be
computationally costly.
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
quantifier than when optimizing for quantification.
Nonetheless, this is theoretically unlikely to happen.</p>
</div>
</div>
</section>
</section>
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@ -195,8 +206,9 @@ Nonetheless, this is theoretically unlikely to happen.</p>
</div>
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<ul>
<li><a class="reference internal" href="#">Model Selection</a><ul>
<li><a class="reference internal" href="#targeting-a-quantification-oriented-loss">Targeting a Quantification-oriented loss</a></li>
<li><a class="reference internal" href="#targeting-a-classification-oriented-loss">Targeting a Classification-oriented loss</a></li>
@ -204,6 +216,17 @@ Nonetheless, this is theoretically unlikely to happen.</p>
</li>
</ul>
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@ -220,7 +243,7 @@ Nonetheless, this is theoretically unlikely to happen.</p>
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@ -234,13 +257,19 @@ Nonetheless, this is theoretically unlikely to happen.</p>
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@ -2,23 +2,26 @@
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@ -49,8 +52,8 @@
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<div class="tex2jax_ignore mathjax_ignore section" id="plotting">
<h1>Plotting<a class="headerlink" href="#plotting" title="Permalink to this headline"></a></h1>
<section id="plotting">
<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
that can help analyse the performance of a quantification method.</p>
<p>All plotting functions receive as inputs the outcomes of
@ -137,8 +140,8 @@ generating 100 random samples at each prevalence).</p>
</pre></div>
</div>
<p>the plots that can be generated are explained below.</p>
<div class="section" id="diagonal-plot">
<h2>Diagonal Plot<a class="headerlink" href="#diagonal-plot" title="Permalink to this headline"></a></h2>
<section id="diagonal-plot">
<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
quantifiers performance. It plots the predicted class
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”
variants CC and PCC, are strongly biased towards the
prevalence seen during training.</p>
</div>
<div class="section" id="quantification-bias">
<h2>Quantification bias<a class="headerlink" href="#quantification-bias" title="Permalink to this headline"></a></h2>
</section>
<section id="quantification-bias">
<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
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>.
@ -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="p">)</span>
<span class="c1"># method names can contain Latex syntax</span>
<span class="n">method_name</span> <span class="o">=</span> <span class="s1">&#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="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>showing pretty clearly the dependency of CC on the prior probabilities
of the labeled set it was trained on.</p>
</div>
<div class="section" id="error-by-drift">
<h2>Error by Drift<a class="headerlink" href="#error-by-drift" title="Permalink to this headline"></a></h2>
</section>
<section id="error-by-drift">
<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
the performance of different quantification methods, but are
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.
We recommend to set <em>show_std=False</em> in those cases
in order to hide the color bands.</p>
</div>
</div>
</section>
</section>
<div class="clearer"></div>
@ -280,8 +283,9 @@ in order to hide the color bands.</p>
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<li><a class="reference internal" href="#">Plotting</a><ul>
<li><a class="reference internal" href="#diagonal-plot">Diagonal Plot</a></li>
<li><a class="reference internal" href="#quantification-bias">Quantification bias</a></li>
@ -290,12 +294,17 @@ in order to hide the color bands.</p>
</li>
</ul>
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title="next chapter">quapy</a></p>
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@ -312,7 +321,7 @@ in order to hide the color bands.</p>
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@ -330,15 +339,15 @@ in order to hide the color bands.</p>
<a href="modules.html" title="quapy"
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&#169; Copyright 2021, Alejandro Moreo.
Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.2.0.
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@ -76,7 +76,7 @@ Features
Datasets
Evaluation
Methods
Model Selection
Model-Selection
Plotting
API Developers documentation<modules>

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@ -1,27 +1,38 @@
:tocdepth: 2
quapy.classification package
============================
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
:members:
:undoc-members:
:show-inheritance:
quapy.classification.neural module
----------------------------------
quapy.classification.neural
---------------------------
.. automodule:: quapy.classification.neural
:members:
:undoc-members:
:show-inheritance:
quapy.classification.svmperf module
-----------------------------------
quapy.classification.svmperf
----------------------------
.. automodule:: quapy.classification.svmperf
:members:

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

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@ -1,43 +1,45 @@
:tocdepth: 2
quapy.method package
====================
Submodules
----------
quapy.method.aggregative module
-------------------------------
quapy.method.aggregative
------------------------
.. automodule:: quapy.method.aggregative
:members:
:undoc-members:
:show-inheritance:
quapy.method.base module
------------------------
quapy.method.base
-----------------
.. automodule:: quapy.method.base
:members:
:undoc-members:
:show-inheritance:
quapy.method.meta module
------------------------
quapy.method.meta
-----------------
.. automodule:: quapy.method.meta
:members:
:undoc-members:
:show-inheritance:
quapy.method.neural module
--------------------------
quapy.method.neural
-------------------
.. automodule:: quapy.method.neural
:members:
:undoc-members:
:show-inheritance:
quapy.method.non\_aggregative module
------------------------------------
quapy.method.non\_aggregative
-----------------------------
.. automodule:: quapy.method.non_aggregative
:members:

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

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@ -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:

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@ -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

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

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@ -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;
}
}

View File

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

View File

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

View File

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

View File

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

View File

@ -2,322 +2,155 @@
* 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.
*
*/
"use strict";
/**
* select a different prefix for underscore
*/
$u = _.noConflict();
const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
"TEXTAREA",
"INPUT",
"SELECT",
"BUTTON",
]);
/**
* make the code below compatible with browsers without
* an installed firebug like debugger
if (!window.console || !console.firebug) {
var names = ["log", "debug", "info", "warn", "error", "assert", "dir",
"dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace",
"profile", "profileEnd"];
window.console = {};
for (var i = 0; i < names.length; ++i)
window.console[names[i]] = function() {};
}
*/
/**
* small helper function to urldecode strings
*
* See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
*/
jQuery.urldecode = function(x) {
if (!x) {
return x
const _ready = (callback) => {
if (document.readyState !== "loading") {
callback();
} else {
document.addEventListener("DOMContentLoaded", callback);
}
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 {
span = document.createElement("span");
span.className = className;
}
span.appendChild(document.createTextNode(val.substr(pos, text.length)));
node.parentNode.insertBefore(span, node.parentNode.insertBefore(
document.createTextNode(val.substr(pos + text.length)),
node.nextSibling));
node.nodeValue = val.substr(0, pos);
if (isInSVG) {
var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
var bbox = node.parentElement.getBBox();
rect.x.baseVal.value = bbox.x;
rect.y.baseVal.value = bbox.y;
rect.width.baseVal.value = bbox.width;
rect.height.baseVal.value = bbox.height;
rect.setAttribute('class', className);
addItems.push({
"parent": node.parentNode,
"target": rect});
}
}
}
else if (!jQuery(node).is("button, select, textarea")) {
jQuery.each(node.childNodes, function() {
highlight(this, addItems);
});
}
}
var addItems = [];
var result = this.each(function() {
highlight(this, addItems);
});
for (var i = 0; i < addItems.length; ++i) {
jQuery(addItems[i].parent).before(addItems[i].target);
}
return result;
};
/*
* backward compatibility for jQuery.browser
* This will be supported until firefox bug is fixed.
*/
if (!jQuery.browser) {
jQuery.uaMatch = function(ua) {
ua = ua.toLowerCase();
var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
/(webkit)[ \/]([\w.]+)/.exec(ua) ||
/(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
/(msie) ([\w.]+)/.exec(ua) ||
ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
[];
return {
browser: match[ 1 ] || "",
version: match[ 2 ] || "0"
};
};
jQuery.browser = {};
jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
}
/**
* Small JavaScript module for the documentation.
*/
var Documentation = {
init : function() {
this.fixFirefoxAnchorBug();
this.highlightSearchWords();
this.initIndexTable();
if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) {
this.initOnKeyListeners();
}
const Documentation = {
init: () => {
Documentation.initDomainIndexTable();
Documentation.initOnKeyListeners();
},
/**
* i18n support
*/
TRANSLATIONS : {},
PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; },
LOCALE : 'unknown',
TRANSLATIONS: {},
PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
LOCALE: "unknown",
// gettext and ngettext don't access this so that the functions
// can safely bound to a different name (_ = Documentation.gettext)
gettext : function(string) {
var translated = Documentation.TRANSLATIONS[string];
if (typeof translated === 'undefined')
return string;
return (typeof translated === 'string') ? translated : translated[0];
gettext: (string) => {
const translated = Documentation.TRANSLATIONS[string];
switch (typeof translated) {
case "undefined":
return string; // no translation
case "string":
return translated; // translation exists
default:
return translated[0]; // (singular, plural) translation tuple exists
}
},
ngettext : function(singular, plural, n) {
var translated = Documentation.TRANSLATIONS[singular];
if (typeof translated === 'undefined')
return (n == 1) ? singular : plural;
return translated[Documentation.PLURALEXPR(n)];
ngettext: (singular, plural, n) => {
const translated = Documentation.TRANSLATIONS[singular];
if (typeof translated !== "undefined")
return translated[Documentation.PLURAL_EXPR(n)];
return n === 1 ? singular : plural;
},
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;
addTranslations: (catalog) => {
Object.assign(Documentation.TRANSLATIONS, catalog.messages);
Documentation.PLURAL_EXPR = new Function(
"n",
`return (${catalog.plural_expr})`
);
Documentation.LOCALE = catalog.locale;
},
/**
* add context elements like header anchor links
* helper function to focus on search bar
*/
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);
});
focusSearchBar: () => {
document.querySelectorAll("input[name=q]")[0]?.focus();
},
/**
* workaround a firefox stupidity
* see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075
* Initialise the domain index toggle buttons
*/
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 = $('body');
initDomainIndexTable: () => {
const toggler = (el) => {
const idNumber = el.id.substr(7);
const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
if (el.src.substr(-9) === "minus.png") {
el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
toggledRows.forEach((el) => (el.style.display = "none"));
} else {
el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
toggledRows.forEach((el) => (el.style.display = ""));
}
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'));
}
};
const togglerElements = document.querySelectorAll("img.toggler");
togglerElements.forEach((el) =>
el.addEventListener("click", (event) => toggler(event.currentTarget))
);
togglerElements.forEach((el) => (el.style.display = ""));
if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
},
/**
* init the domain index toggle buttons
*/
initIndexTable : function() {
var togglers = $('img.toggler').click(function() {
var src = $(this).attr('src');
var idnum = $(this).attr('id').substr(7);
$('tr.cg-' + idnum).toggle();
if (src.substr(-9) === 'minus.png')
$(this).attr('src', src.substr(0, src.length-9) + 'plus.png');
else
$(this).attr('src', src.substr(0, src.length-8) + 'minus.png');
}).css('display', '');
if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) {
togglers.click();
}
},
initOnKeyListeners: () => {
// only install a listener if it is really needed
if (
!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
)
return;
/**
* helper function to hide the search marks again
*/
hideSearchWords : function() {
$('#searchbox .highlight-link').fadeOut(300);
$('span.highlighted').removeClass('highlighted');
},
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;
/**
* make the url absolute
*/
makeURL : function(relativeURL) {
return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL;
},
if (!event.shiftKey) {
switch (event.key) {
case "ArrowLeft":
if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
/**
* get the current relative url
*/
getCurrentURL : function() {
var path = document.location.pathname;
var parts = path.split(/\//);
$.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() {
if (this === '..')
parts.pop();
});
var url = parts.join('/');
return path.substring(url.lastIndexOf('/') + 1, path.length - 1);
},
initOnKeyListeners: function() {
$(document).keydown(function(event) {
var activeElementType = document.activeElement.tagName;
// don't navigate when in search box, textarea, dropdown or button
if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'
&& activeElementType !== 'BUTTON' && !event.altKey && !event.ctrlKey && !event.metaKey
&& !event.shiftKey) {
switch (event.keyCode) {
case 37: // left
var prevHref = $('link[rel="prev"]').prop('href');
if (prevHref) {
window.location.href = prevHref;
return false;
const prevLink = document.querySelector('link[rel="prev"]');
if (prevLink && prevLink.href) {
window.location.href = prevLink.href;
event.preventDefault();
}
break;
case 39: // right
var nextHref = $('link[rel="next"]').prop('href');
if (nextHref) {
window.location.href = nextHref;
return false;
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
_ = Documentation.gettext;
const _ = Documentation.gettext;
$(document).ready(function() {
Documentation.init();
});
_ready(Documentation.init);

View File

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

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File diff suppressed because one or more lines are too long

View File

@ -5,12 +5,12 @@
* This script contains the language-specific data used by searchtools.js,
* namely the list of stopwords, stemmer, scorer and splitter.
*
* :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
* :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
* :license: BSD, see LICENSE for details.
*
*/
var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"];
var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
/* Non-minified version is copied as a separate JS file, is available */
@ -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;
}

View File

@ -4,22 +4,24 @@
*
* 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.
*
*/
"use strict";
if (!Scorer) {
/**
* Simple result scoring code.
*/
/**
* Simple result scoring code.
*/
if (typeof Scorer === "undefined") {
var Scorer = {
// Implement the following function to further tweak the score for each result
// The function takes a result array [filename, title, anchor, descr, score]
// The function takes a result array [docname, title, anchor, descr, score, filename]
// and returns the new score.
/*
score: function(result) {
return result[4];
score: result => {
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
objPartialMatch: 6,
// Additive scores depending on the priority of the object
objPrio: {0: 15, // used to be importantResults
1: 5, // used to be objectResults
2: -5}, // used to be unimportantResults
objPrio: {
0: 15, // used to be importantResults
1: 5, // used to be objectResults
2: -5, // used to be unimportantResults
},
// Used when the priority is not in the mapping.
objPrioDefault: 0,
@ -39,455 +43,495 @@ if (!Scorer) {
partialTitle: 7,
// query found in terms
term: 5,
partialTerm: 2
partialTerm: 2,
};
}
if (!splitQuery) {
function splitQuery(query) {
return query.split(/\s+/);
const _removeChildren = (element) => {
while (element && element.lastChild) element.removeChild(element.lastChild);
};
/**
* 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
*/
var Search = {
const Search = {
_index: null,
_queued_query: null,
_pulse_status: -1,
_index : null,
_queued_query : null,
_pulse_status : -1,
htmlToText : function(htmlString) {
var virtualDocument = document.implementation.createHTMLDocument('virtual');
var htmlElement = $(htmlString, virtualDocument);
htmlElement.find('.headerlink').remove();
docContent = htmlElement.find('[role=main]')[0];
if(docContent === undefined) {
console.warn("Content block not found. Sphinx search tries to obtain it " +
"via '[role=main]'. Could you check your theme or template.");
return "";
}
return docContent.textContent || docContent.innerText;
htmlToText: (htmlString) => {
const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() });
const docContent = htmlElement.querySelector('[role="main"]');
if (docContent !== undefined) return docContent.textContent;
console.warn(
"Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template."
);
return "";
},
init : function() {
var params = $.getQueryParameters();
if (params.q) {
var query = params.q[0];
$('input[name="q"]')[0].value = query;
this.performSearch(query);
}
init: () => {
const query = new URLSearchParams(window.location.search).get("q");
document
.querySelectorAll('input[name="q"]')
.forEach((el) => (el.value = query));
if (query) Search.performSearch(query);
},
loadIndex : function(url) {
$.ajax({type: "GET", url: url, data: null,
dataType: "script", cache: true,
complete: function(jqxhr, textstatus) {
if (textstatus != "success") {
document.getElementById("searchindexloader").src = url;
}
}});
},
loadIndex: (url) =>
(document.body.appendChild(document.createElement("script")).src = url),
setIndex : function(index) {
var q;
this._index = index;
if ((q = this._queued_query) !== null) {
this._queued_query = null;
Search.query(q);
setIndex: (index) => {
Search._index = index;
if (Search._queued_query !== null) {
const query = Search._queued_query;
Search._queued_query = null;
Search.query(query);
}
},
hasIndex : function() {
return this._index !== null;
},
hasIndex: () => Search._index !== null,
deferQuery : function(query) {
this._queued_query = query;
},
deferQuery: (query) => (Search._queued_query = query),
stopPulse : function() {
this._pulse_status = 0;
},
stopPulse: () => (Search._pulse_status = -1),
startPulse : function() {
if (this._pulse_status >= 0)
return;
function pulse() {
var i;
startPulse: () => {
if (Search._pulse_status >= 0) return;
const pulse = () => {
Search._pulse_status = (Search._pulse_status + 1) % 4;
var dotString = '';
for (i = 0; i < Search._pulse_status; i++)
dotString += '.';
Search.dots.text(dotString);
if (Search._pulse_status > -1)
window.setTimeout(pulse, 500);
}
Search.dots.innerText = ".".repeat(Search._pulse_status);
if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
};
pulse();
},
/**
* perform a search for something (or wait until index is loaded)
*/
performSearch : function(query) {
performSearch: (query) => {
// create the required interface elements
this.out = $('#search-results');
this.title = $('<h2>' + _('Searching') + '</h2>').appendTo(this.out);
this.dots = $('<span></span>').appendTo(this.title);
this.status = $('<p class="search-summary">&nbsp;</p>').appendTo(this.out);
this.output = $('<ul class="search"/>').appendTo(this.out);
const searchText = document.createElement("h2");
searchText.textContent = _("Searching");
const searchSummary = document.createElement("p");
searchSummary.classList.add("search-summary");
searchSummary.innerText = "";
const searchList = document.createElement("ul");
searchList.classList.add("search");
$('#search-progress').text(_('Preparing search...'));
this.startPulse();
const out = document.getElementById("search-results");
Search.title = out.appendChild(searchText);
Search.dots = Search.title.appendChild(document.createElement("span"));
Search.status = out.appendChild(searchSummary);
Search.output = out.appendChild(searchList);
const searchProgress = document.getElementById("search-progress");
// Some themes don't use the search progress node
if (searchProgress) {
searchProgress.innerText = _("Preparing search...");
}
Search.startPulse();
// index already loaded, the browser was quick!
if (this.hasIndex())
this.query(query);
else
this.deferQuery(query);
if (Search.hasIndex()) Search.query(query);
else Search.deferQuery(query);
},
/**
* execute search (requires search index to be loaded)
*/
query : function(query) {
var i;
query: (query) => {
const filenames = Search._index.filenames;
const docNames = Search._index.docnames;
const titles = Search._index.titles;
const allTitles = Search._index.alltitles;
const indexEntries = Search._index.indexentries;
// stem the searchterms and add them to the correct list
var stemmer = new Stemmer();
var searchterms = [];
var excluded = [];
var hlterms = [];
var tmp = splitQuery(query);
var objectterms = [];
for (i = 0; i < tmp.length; i++) {
if (tmp[i] !== "") {
objectterms.push(tmp[i].toLowerCase());
}
// stem the search terms and add them to the correct list
const stemmer = new Stemmer();
const searchTerms = new Set();
const excludedTerms = new Set();
const highlightTerms = new Set();
const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
splitQuery(query.trim()).forEach((queryTerm) => {
const queryTermLower = queryTerm.toLowerCase();
// maybe skip this "word"
// stopwords array is from language_data.js
if (
stopwords.indexOf(queryTermLower) !== -1 ||
queryTerm.match(/^\d+$/)
)
return;
if ($u.indexOf(stopwords, tmp[i].toLowerCase()) != -1 || tmp[i] === "") {
// skip this "word"
continue;
}
// stem the word
var word = stemmer.stemWord(tmp[i].toLowerCase());
// prevent stemmer from cutting word smaller than two chars
if(word.length < 3 && tmp[i].length >= 3) {
word = tmp[i];
}
var toAppend;
let word = stemmer.stemWord(queryTermLower);
// select the correct list
if (word[0] == '-') {
toAppend = excluded;
word = word.substr(1);
}
if (word[0] === "-") excludedTerms.add(word.substr(1));
else {
toAppend = searchterms;
hlterms.push(tmp[i].toLowerCase());
searchTerms.add(word);
highlightTerms.add(queryTermLower);
}
// only add if not already in the list
if (!$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.info('required: ', searchterms);
// console.info('excluded: ', excluded);
// console.debug("SEARCH: searching for:");
// console.info("required: ", [...searchTerms]);
// console.info("excluded: ", [...excludedTerms]);
// prepare search
var terms = this._index.terms;
var titleterms = this._index.titleterms;
// array of [docname, title, anchor, descr, score, filename]
let results = [];
_removeChildren(document.getElementById("search-progress"));
// array of [filename, title, anchor, descr, score]
var results = [];
$('#search-progress').empty();
const queryLower = query.toLowerCase();
for (const [title, foundTitles] of Object.entries(allTitles)) {
if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) {
for (const [file, id] of foundTitles) {
let score = Math.round(100 * queryLower.length / title.length)
results.push([
docNames[file],
titles[file] !== title ? `${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
for (i = 0; i < objectterms.length; i++) {
var others = [].concat(objectterms.slice(0, i),
objectterms.slice(i+1, objectterms.length));
results = results.concat(this.performObjectSearch(objectterms[i], others));
}
objectTerms.forEach((term) =>
results.push(...Search.performObjectSearch(term, objectTerms))
);
// 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
if (Scorer.score) {
for (i = 0; i < results.length; i++)
results[i][4] = Scorer.score(results[i]);
}
if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item)));
// now sort the results by score (in opposite order of appearance, since the
// display function below uses pop() to retrieve items) and then
// alphabetically
results.sort(function(a, b) {
var left = a[4];
var right = b[4];
if (left > right) {
return 1;
} else if (left < right) {
return -1;
} else {
results.sort((a, b) => {
const leftScore = a[4];
const rightScore = b[4];
if (leftScore === rightScore) {
// same score: sort alphabetically
left = a[1].toLowerCase();
right = b[1].toLowerCase();
return (left > right) ? -1 : ((left < right) ? 1 : 0);
const leftTitle = a[1].toLowerCase();
const rightTitle = b[1].toLowerCase();
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
//Search.lastresults = results.slice(); // a copy
//console.info('search results:', Search.lastresults);
// console.info("search results:", Search.lastresults);
// print the results
var resultCount = results.length;
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();
_displayNextItem(results, results.length, searchTerms);
},
/**
* search for object names
*/
performObjectSearch : function(object, otherterms) {
var filenames = this._index.filenames;
var docnames = this._index.docnames;
var objects = this._index.objects;
var objnames = this._index.objnames;
var titles = this._index.titles;
performObjectSearch: (object, objectTerms) => {
const filenames = Search._index.filenames;
const docNames = Search._index.docnames;
const objects = Search._index.objects;
const objNames = Search._index.objnames;
const titles = Search._index.titles;
var i;
var results = [];
const results = [];
for (var prefix in objects) {
for (var name in objects[prefix]) {
var fullname = (prefix ? prefix + '.' : '') + name;
var fullnameLower = fullname.toLowerCase()
if (fullnameLower.indexOf(object) > -1) {
var score = 0;
var parts = fullnameLower.split('.');
// check for different match types: exact matches of full name or
// "last name" (i.e. last dotted part)
if (fullnameLower == object || parts[parts.length - 1] == object) {
score += Scorer.objNameMatch;
// matches in last name
} else if (parts[parts.length - 1].indexOf(object) > -1) {
score += Scorer.objPartialMatch;
}
var match = objects[prefix][name];
var objname = objnames[match[1]][2];
var title = titles[match[0]];
// If more than one term searched for, we require other words to be
// found in the name/title/description
if (otherterms.length > 0) {
var haystack = (prefix + ' ' + name + ' ' +
objname + ' ' + title).toLowerCase();
var allfound = true;
for (i = 0; i < otherterms.length; i++) {
if (haystack.indexOf(otherterms[i]) == -1) {
allfound = false;
break;
}
}
if (!allfound) {
continue;
}
}
var descr = objname + _(', in ') + title;
const objectSearchCallback = (prefix, match) => {
const name = match[4]
const fullname = (prefix ? prefix + "." : "") + name;
const fullnameLower = fullname.toLowerCase();
if (fullnameLower.indexOf(object) < 0) return;
var anchor = match[3];
if (anchor === '')
anchor = fullname;
else if (anchor == '-')
anchor = objnames[match[1]][1] + '-' + fullname;
// add custom score for some objects according to scorer
if (Scorer.objPrio.hasOwnProperty(match[2])) {
score += Scorer.objPrio[match[2]];
} else {
score += Scorer.objPrioDefault;
}
results.push([docnames[match[0]], fullname, '#'+anchor, descr, score, filenames[match[0]]]);
}
let score = 0;
const parts = fullnameLower.split(".");
// check for different match types: exact matches of full name or
// "last name" (i.e. last dotted part)
if (fullnameLower === object || parts.slice(-1)[0] === object)
score += Scorer.objNameMatch;
else if (parts.slice(-1)[0].indexOf(object) > -1)
score += Scorer.objPartialMatch; // matches in last name
const objName = objNames[match[1]][2];
const title = titles[match[0]];
// If more than one term searched for, we require other words to be
// found in the name/title/description
const otherTerms = new Set(objectTerms);
otherTerms.delete(object);
if (otherTerms.size > 0) {
const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
if (
[...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
)
return;
}
}
let anchor = match[3];
if (anchor === "") anchor = fullname;
else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
const descr = objName + _(", in ") + title;
// add custom score for some objects according to scorer
if (Scorer.objPrio.hasOwnProperty(match[2]))
score += Scorer.objPrio[match[2]];
else score += Scorer.objPrioDefault;
results.push([
docNames[match[0]],
fullname,
"#" + anchor,
descr,
score,
filenames[match[0]],
]);
};
Object.keys(objects).forEach((prefix) =>
objects[prefix].forEach((array) =>
objectSearchCallback(prefix, array)
)
);
return results;
},
/**
* See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions
*/
escapeRegExp : function(string) {
return string.replace(/[.*+\-?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
},
/**
* search for full-text terms in the index
*/
performTermsSearch : function(searchterms, excluded, terms, titleterms) {
var docnames = this._index.docnames;
var filenames = this._index.filenames;
var titles = this._index.titles;
performTermsSearch: (searchTerms, excludedTerms) => {
// prepare search
const terms = Search._index.terms;
const titleTerms = Search._index.titleterms;
const filenames = Search._index.filenames;
const docNames = Search._index.docnames;
const titles = Search._index.titles;
var i, j, file;
var fileMap = {};
var scoreMap = {};
var results = [];
const scoreMap = new Map();
const fileMap = new Map();
// perform the search on the required terms
for (i = 0; i < searchterms.length; i++) {
var word = searchterms[i];
var files = [];
var _o = [
{files: terms[word], score: Scorer.term},
{files: titleterms[word], score: Scorer.title}
searchTerms.forEach((word) => {
const files = [];
const arr = [
{ files: terms[word], score: Scorer.term },
{ files: titleTerms[word], score: Scorer.title },
];
// add support for partial matches
if (word.length > 2) {
var word_regex = this.escapeRegExp(word);
for (var w in terms) {
if (w.match(word_regex) && !terms[word]) {
_o.push({files: terms[w], score: Scorer.partialTerm})
}
}
for (var w in titleterms) {
if (w.match(word_regex) && !titleterms[word]) {
_o.push({files: titleterms[w], score: Scorer.partialTitle})
}
}
const escapedWord = _escapeRegExp(word);
Object.keys(terms).forEach((term) => {
if (term.match(escapedWord) && !terms[word])
arr.push({ files: terms[term], score: Scorer.partialTerm });
});
Object.keys(titleTerms).forEach((term) => {
if (term.match(escapedWord) && !titleTerms[word])
arr.push({ files: titleTerms[word], score: Scorer.partialTitle });
});
}
// no match but word was a required one
if ($u.every(_o, function(o){return o.files === undefined;})) {
break;
}
if (arr.every((record) => record.files === undefined)) return;
// found search word in contents
$u.each(_o, function(o) {
var _files = o.files;
if (_files === undefined)
return
arr.forEach((record) => {
if (record.files === undefined) return;
if (_files.length === undefined)
_files = [_files];
files = files.concat(_files);
let recordFiles = record.files;
if (recordFiles.length === undefined) recordFiles = [recordFiles];
files.push(...recordFiles);
// set score for the word in each file to Scorer.term
for (j = 0; j < _files.length; j++) {
file = _files[j];
if (!(file in scoreMap))
scoreMap[file] = {};
scoreMap[file][word] = o.score;
}
// set score for the word in each file
recordFiles.forEach((file) => {
if (!scoreMap.has(file)) scoreMap.set(file, {});
scoreMap.get(file)[word] = record.score;
});
});
// create the mapping
for (j = 0; j < files.length; j++) {
file = files[j];
if (file in fileMap && fileMap[file].indexOf(word) === -1)
fileMap[file].push(word);
else
fileMap[file] = [word];
}
}
files.forEach((file) => {
if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1)
fileMap.get(file).push(word);
else fileMap.set(file, [word]);
});
});
// now check if the files don't contain excluded terms
for (file in fileMap) {
var valid = true;
const results = [];
for (const [file, wordList] of fileMap) {
// 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 (
fileMap[file].length != searchterms.length &&
fileMap[file].length != filteredTermCount
) continue;
wordList.length !== searchTerms.size &&
wordList.length !== filteredTermCount
)
continue;
// ensure that none of the excluded terms is in the search result
for (i = 0; i < excluded.length; i++) {
if (terms[excluded[i]] == file ||
titleterms[excluded[i]] == file ||
$u.contains(terms[excluded[i]] || [], file) ||
$u.contains(titleterms[excluded[i]] || [], file)) {
valid = false;
break;
}
}
if (
[...excludedTerms].some(
(term) =>
terms[term] === file ||
titleTerms[term] === file ||
(terms[term] || []).includes(file) ||
(titleTerms[term] || []).includes(file)
)
)
break;
// if we have still a valid result we can add it to the result list
if (valid) {
// select one (max) score for the file.
// for better ranking, we should calculate ranking by using words statistics like basic tf-idf...
var score = $u.max($u.map(fileMap[file], function(w){return scoreMap[file][w]}));
results.push([docnames[file], titles[file], '', null, score, filenames[file]]);
}
// select one (max) score for the file.
const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
// add result to the result list
results.push([
docNames[file],
titles[file],
"",
null,
score,
filenames[file],
]);
}
return results;
},
@ -495,34 +539,28 @@ var Search = {
/**
* helper function to return a node containing the
* search summary for a given text. keywords is a list
* of stemmed words, hlwords is the list of normal, unstemmed
* words. the first one is used to find the occurrence, the
* latter for highlighting it.
* of stemmed words.
*/
makeSearchSummary : function(htmlText, keywords, hlwords) {
var text = Search.htmlToText(htmlText);
if (text == "") {
return null;
}
var textLower = text.toLowerCase();
var start = 0;
$.each(keywords, function() {
var i = textLower.indexOf(this.toLowerCase());
if (i > -1)
start = i;
});
start = Math.max(start - 120, 0);
var excerpt = ((start > 0) ? '...' : '') +
$.trim(text.substr(start, 240)) +
((start + 240 - text.length) ? '...' : '');
var rv = $('<p class="context"></p>').text(excerpt);
$.each(hlwords, function() {
rv = rv.highlightText(this, 'highlighted');
});
return rv;
}
makeSearchSummary: (htmlText, keywords) => {
const text = Search.htmlToText(htmlText);
if (text === "") return null;
const textLower = text.toLowerCase();
const actualStartPosition = [...keywords]
.map((k) => textLower.indexOf(k.toLowerCase()))
.filter((i) => i > -1)
.slice(-1)[0];
const startWithContext = Math.max(actualStartPosition - 120, 0);
const top = startWithContext === 0 ? "" : "...";
const tail = startWithContext + 240 < text.length ? "..." : "";
let summary = document.createElement("p");
summary.classList.add("context");
summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
return summary;
},
};
$(document).ready(function() {
Search.init();
});
_ready(Search.init);

View File

@ -2,18 +2,20 @@
<!doctype html>
<html>
<html lang="en">
<head>
<meta charset="utf-8" />
<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/bizstyle.css" />
<script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
<script src="_static/jquery.js"></script>
<script src="_static/underscore.js"></script>
<script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
<script src="_static/doctools.js"></script>
<script src="_static/sphinx_highlight.js"></script>
<script src="_static/bizstyle.js"></script>
<link rel="index" title="Index" href="#" />
<link rel="search" title="Search" href="search.html" />
@ -31,7 +33,7 @@
<li class="right" >
<a href="py-modindex.html" title="Python Module Index"
>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>
</ul>
</div>
@ -68,12 +70,17 @@
| <a href="#V"><strong>V</strong></a>
| <a href="#W"><strong>W</strong></a>
| <a href="#X"><strong>X</strong></a>
| <a href="#Y"><strong>Y</strong></a>
</div>
<h2 id="A">A</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<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>
<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><a href="quapy.method.html#quapy.method.aggregative.ACC">ACC (class in quapy.method.aggregative)</a>
</li>
@ -95,6 +102,10 @@
<li><a href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier.aggregate">(quapy.method.aggregative.AggregativeQuantifier method)</a>
</li>
<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><a href="quapy.method.html#quapy.method.aggregative.ELM.aggregate">(quapy.method.aggregative.ELM method)</a>
</li>
@ -107,35 +118,21 @@
<li><a href="quapy.method.html#quapy.method.aggregative.PACC.aggregate">(quapy.method.aggregative.PACC method)</a>
</li>
<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><a href="quapy.method.html#quapy.method.aggregative.ThresholdOptimization.aggregate">(quapy.method.aggregative.ThresholdOptimization method)</a>
</li>
</ul></li>
</ul></td>
<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>
<ul>
<li><a href="quapy.method.html#quapy.method.base.BaseQuantifier.aggregative">(quapy.method.base.BaseQuantifier property)</a>
<li><a href="quapy.method.html#quapy.method.meta.Ensemble.aggregative">aggregative (quapy.method.meta.Ensemble property)</a>
</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>
<li><a href="quapy.method.html#quapy.method.aggregative.AggregativeQuantifier">AggregativeQuantifier (class in quapy.method.aggregative)</a>
</li>
<li><a href="quapy.html#quapy.evaluation.artificial_prevalence_prediction">artificial_prevalence_prediction() (in module quapy.evaluation)</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><a href="quapy.html#quapy.protocol.APP">APP (class in quapy.protocol)</a>
</li>
<li><a href="quapy.classification.html#quapy.classification.neural.TorchDataset.asDataloader">asDataloader() (quapy.classification.neural.TorchDataset method)</a>
</li>
@ -146,6 +143,8 @@
<table style="width: 100%" class="indextable genindextable"><tr>
<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>
<li><a href="quapy.classification.html#quapy.classification.calibration.BCTSCalibration">BCTSCalibration (class in quapy.classification.calibration)</a>
</li>
<li><a href="quapy.html#quapy.model_selection.GridSearchQ.best_model">best_model() (quapy.model_selection.GridSearchQ method)</a>
</li>
@ -155,14 +154,6 @@
<ul>
<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>
</ul></li>
</ul></td>
@ -185,27 +176,29 @@
<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>
<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>
<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>
<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><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>
</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>
<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>
<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>
<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><a href="quapy.data.html#quapy.data.base.LabelledCollection.counts">counts() (quapy.data.base.LabelledCollection method)</a>
</li>
<li><a href="quapy.html#quapy.util.create_if_not_exist">create_if_not_exist() (in module quapy.util)</a>
</li>
<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>
</ul></td>
</tr></table>
@ -248,6 +249,8 @@
</li>
</ul></li>
<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>
</ul></td>
<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>
</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>
<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>
</ul></td>
</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>
<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><a href="quapy.method.html#quapy.method.aggregative.ExpectationMaximizationQuantifier">ExpectationMaximizationQuantifier (in module quapy.method.aggregative)</a>
</li>
@ -312,6 +321,8 @@
<li><a href="quapy.html#quapy.error.f1_error">f1_error() (in module quapy.error)</a>
</li>
<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><a href="quapy.data.html#quapy.data.datasets.fetch_reviews">fetch_reviews() (in module quapy.data.datasets)</a>
</li>
@ -321,9 +332,11 @@
</li>
<li><a href="quapy.data.html#quapy.data.datasets.fetch_UCILabelledCollection">fetch_UCILabelledCollection() (in module quapy.data.datasets)</a>
</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>
<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>
<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>
<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><a href="quapy.method.html#quapy.method.aggregative.ELM.fit">(quapy.method.aggregative.ELM method)</a>
</li>
@ -347,10 +364,14 @@
<li><a href="quapy.method.html#quapy.method.aggregative.PACC.fit">(quapy.method.aggregative.PACC method)</a>
</li>
<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><a href="quapy.method.html#quapy.method.aggregative.ThresholdOptimization.fit">(quapy.method.aggregative.ThresholdOptimization method)</a>
</li>
<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><a href="quapy.method.html#quapy.method.meta.Ensemble.fit">(quapy.method.meta.Ensemble method)</a>
</li>
@ -363,6 +384,10 @@
</ul></li>
</ul></td>
<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>
<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>
<table style="width: 100%" class="indextable genindextable"><tr>
<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><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><a href="quapy.html#quapy.functional.get_nprevpoints_approximation">get_nprevpoints_approximation() (in module quapy.functional)</a>
</li>
@ -401,18 +426,14 @@
<li><a href="quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.get_params">(quapy.classification.neural.NeuralClassifierTrainer method)</a>
</li>
<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><a href="quapy.method.html#quapy.method.aggregative.OneVsAll.get_params">(quapy.method.aggregative.OneVsAll method)</a>
</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><a href="quapy.method.html#quapy.method.meta.Ensemble.get_params">(quapy.method.meta.Ensemble method)</a>
</li>
<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><a href="quapy.html#quapy.model_selection.GridSearchQ.get_params">(quapy.model_selection.GridSearchQ method)</a>
</li>
@ -423,6 +444,12 @@
</li>
<li><a href="quapy.html#quapy.util.get_quapy_home">get_quapy_home() (in module quapy.util)</a>
</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>
</ul></td>
@ -446,22 +473,10 @@
<table style="width: 100%" class="indextable genindextable"><tr>
<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>
<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>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.html#quapy.isbinary">isbinary() (in module quapy)</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><a href="quapy.data.html#quapy.data.preprocessing.IndexTransformer">IndexTransformer (class in quapy.data.preprocessing)</a>
</li>
</ul></td>
</tr></table>
@ -486,8 +501,6 @@
<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">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><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>
<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><a href="quapy.classification.html#module-quapy.classification.methods">quapy.classification.methods</a>
</li>
@ -576,6 +591,8 @@
<li><a href="quapy.html#module-quapy.model_selection">quapy.model_selection</a>
</li>
<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><a href="quapy.html#module-quapy.util">quapy.util</a>
</li>
@ -600,27 +617,19 @@
<ul>
<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>
</ul></li>
<li><a href="quapy.html#quapy.evaluation.natural_prevalence_prediction">natural_prevalence_prediction() (in module quapy.evaluation)</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><a href="quapy.classification.html#quapy.classification.calibration.NBVSCalibration">NBVSCalibration (class in quapy.classification.calibration)</a>
</li>
<li><a href="quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer">NeuralClassifierTrainer (class in quapy.classification.neural)</a>
</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>
<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><a href="quapy.html#quapy.functional.num_prevalence_combinations">num_prevalence_combinations() (in module quapy.functional)</a>
</li>
@ -630,7 +639,15 @@
<h2 id="O">O</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<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>
</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>
</ul></td>
</tr></table>
@ -638,6 +655,8 @@
<h2 id="P">P</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.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>
<li><a href="quapy.html#quapy.util.parallel">parallel() (in module quapy.util)</a>
@ -646,52 +665,44 @@
</li>
<li><a href="quapy.html#quapy.util.pickled_resource">pickled_resource() (in module quapy.util)</a>
</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>
<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>
</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>
<li><a href="quapy.classification.html#quapy.classification.svmperf.SVMperf.predict">(quapy.classification.svmperf.SVMperf method)</a>
</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>
<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>
<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><a href="quapy.method.html#quapy.method.aggregative.EMQ.predict_proba">(quapy.method.aggregative.EMQ method)</a>
</li>
</ul></li>
</ul></td>
<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>
<li><a href="quapy.html#quapy.functional.prevalence_from_labels">prevalence_from_labels() (in module quapy.functional)</a>
</li>
<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><a href="quapy.html#quapy.functional.prevalence_linspace">prevalence_linspace() (in module quapy.functional)</a>
</li>
<li><a href="quapy.method.html#quapy.method.aggregative.AggregativeProbabilisticQuantifier.probabilistic">probabilistic (quapy.method.aggregative.AggregativeProbabilisticQuantifier property)</a>
<ul>
<li><a href="quapy.method.html#quapy.method.aggregative.OneVsAll.probabilistic">(quapy.method.aggregative.OneVsAll property)</a>
<li><a href="quapy.method.html#quapy.method.meta.Ensemble.probabilistic">probabilistic (quapy.method.meta.Ensemble property)</a>
</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>
<li><a href="quapy.method.html#quapy.method.aggregative.ProbabilisticClassifyAndCount">ProbabilisticClassifyAndCount (in module quapy.method.aggregative)</a>
@ -706,14 +717,12 @@
</li>
<li><a href="quapy.method.html#quapy.method.neural.QuaNetTrainer">QuaNetTrainer (class in quapy.method.neural)</a>
</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>
<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>
<li><a href="quapy.method.html#quapy.method.base.OneVsAllGeneric.quantify">(quapy.method.base.OneVsAllGeneric method)</a>
</li>
<li><a href="quapy.method.html#quapy.method.meta.Ensemble.quantify">(quapy.method.meta.Ensemble method)</a>
</li>
@ -736,6 +745,13 @@
<ul>
<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>
</ul></li>
<li>
@ -871,6 +887,13 @@
<ul>
<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>
</ul></li>
<li>
@ -888,15 +911,23 @@
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.html#quapy.error.rae">rae() (in module quapy.error)</a>
</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>
</ul></td>
<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>
<li><a href="quapy.html#quapy.error.relative_absolute_error">relative_absolute_error() (in module quapy.error)</a>
</li>
<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>
</ul></td>
</tr></table>
@ -904,6 +935,30 @@
<h2 id="S">S</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<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>
<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>
<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><a href="quapy.method.html#quapy.method.aggregative.OneVsAll.set_params">(quapy.method.aggregative.OneVsAll method)</a>
</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><a href="quapy.method.html#quapy.method.meta.Ensemble.set_params">(quapy.method.meta.Ensemble method)</a>
</li>
<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><a href="quapy.html#quapy.model_selection.GridSearchQ.set_params">(quapy.model_selection.GridSearchQ method)</a>
</li>
@ -941,10 +990,14 @@
</ul></td>
<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>
<li><a href="quapy.method.html#quapy.method.aggregative.SMM">SMM (class in quapy.method.aggregative)</a>
</li>
<li><a href="quapy.html#quapy.error.smooth">smooth() (in module quapy.error)</a>
</li>
<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><a href="quapy.data.html#quapy.data.base.LabelledCollection.split_stratified">split_stratified() (quapy.data.base.LabelledCollection method)</a>
</li>
@ -986,12 +1039,38 @@
</li>
<li><a href="quapy.classification.html#quapy.classification.neural.TextClassifierNet">TextClassifierNet (class in quapy.classification.neural)</a>
</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>
<li><a href="quapy.html#quapy.functional.TopsoeDistance">TopsoeDistance() (in module quapy.functional)</a>
</li>
<li><a href="quapy.classification.html#quapy.classification.neural.TorchDataset">TorchDataset (class in quapy.classification.neural)</a>
</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>
<ul>
@ -1000,6 +1079,8 @@
<li><a href="quapy.data.html#quapy.data.preprocessing.IndexTransformer.transform">(quapy.data.preprocessing.IndexTransformer method)</a>
</li>
</ul></li>
<li><a href="quapy.classification.html#quapy.classification.calibration.TSCalibration">TSCalibration (class in quapy.classification.calibration)</a>
</li>
</ul></td>
</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>
<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>
</ul></td>
</tr></table>
@ -1039,6 +1122,8 @@
</ul></td>
<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>
<li><a href="quapy.classification.html#quapy.classification.calibration.VSCalibration">VSCalibration (class in quapy.classification.calibration)</a>
</li>
</ul></td>
</tr></table>
@ -1055,16 +1140,30 @@
<table style="width: 100%" class="indextable genindextable"><tr>
<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>
<ul>
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection.X">(quapy.data.base.LabelledCollection property)</a>
</li>
</ul></li>
</ul></td>
<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>
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection.Xp">Xp (quapy.data.base.LabelledCollection property)</a>
</li>
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection.Xy">Xy (quapy.data.base.LabelledCollection property)</a>
</li>
</ul></td>
</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>
@ -1082,7 +1181,7 @@
</form>
</div>
</div>
<script>$('#searchbox').show(0);</script>
<script>document.getElementById('searchbox').style.display = "block"</script>
</div>
</div>
<div class="clearer"></div>
@ -1096,13 +1195,13 @@
<li class="right" >
<a href="py-modindex.html" title="Python Module Index"
>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>
</ul>
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&#169; Copyright 2021, Alejandro Moreo.
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@ -2,19 +2,21 @@
<!doctype html>
<html>
<html lang="en">
<head>
<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 QuaPys documentation! &#8212; QuaPy 0.1.6 documentation</title>
<title>Welcome to QuaPys 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/bizstyle.css" />
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<link rel="search" title="Search" href="search.html" />
@ -36,7 +38,7 @@
<li class="right" >
<a href="Installation.html" title="Installation"
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 QuaPys documentation!</a></li>
</ul>
</div>
@ -47,11 +49,11 @@
<div class="body" role="main">
<section id="welcome-to-quapy-s-documentation">
<h1>Welcome to QuaPys documentation!<a class="headerlink" href="#welcome-to-quapy-s-documentation" title="Permalink to this headline"></a></h1>
<h1>Welcome to QuaPys 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)
written in Python.</p>
<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
in quantification literature, such as the most important quantification baselines, many advanced
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
interpretation of results.</p>
<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
<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
@ -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>
</section>
<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">
<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>
@ -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>
</ul>
</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-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>
@ -149,7 +156,7 @@ See the <a class="reference internal" href="Evaluation.html"><span class="doc">E
</section>
</section>
<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">
<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>
@ -164,8 +171,9 @@ See the <a class="reference internal" href="Evaluation.html"><span class="doc">E
</div>
<div class="sphinxsidebar" role="navigation" aria-label="main navigation">
<div class="sphinxsidebarwrapper">
<h3><a href="#">Table of Contents</a></h3>
<ul>
<div>
<h3><a href="#">Table of Contents</a></h3>
<ul>
<li><a class="reference internal" href="#">Welcome to QuaPys documentation!</a><ul>
<li><a class="reference internal" href="#introduction">Introduction</a><ul>
<li><a class="reference internal" href="#a-quick-example">A quick example:</a></li>
@ -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>
</ul>
<h4>Next topic</h4>
<p class="topless"><a href="Installation.html"
title="next chapter">Installation</a></p>
</div>
<div>
<h4>Next topic</h4>
<p class="topless"><a href="Installation.html"
title="next chapter">Installation</a></p>
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<li class="nav-item nav-item-0"><a href="#">QuaPy 0.1.6 documentation</a> &#187;</li>
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<li class="nav-item nav-item-this"><a href="">Welcome to QuaPys documentation!</a></li>
</ul>
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<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>
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<li class="toctree-l2"><a class="reference internal" href="quapy.html#module-quapy.model_selection">quapy.model_selection module</a></li>
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<li class="toctree-l2"><a class="reference internal" href="quapy.html#module-quapy.model_selection">quapy.model_selection</a></li>
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@ -106,12 +80,16 @@
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@ -161,6 +168,11 @@
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@ -53,16 +55,232 @@
<div class="body" role="main">
<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">
<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 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">
<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>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.base.BaseEstimator</span></code></p>
<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">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>)
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
@ -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>
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">
<dt class="field-odd">Parameters</dt>
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<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>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
fitting <cite>TruncatedSVD</cite> and then <cite>LogisticRegression</cite> on the low-rank representation.</p>
<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">
<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>
</ul>
</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>
</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>
<dd><p>Get hyper-parameters for this estimator.</p>
<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>
</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>
<dd><p>Predicts labels for the instances <cite>X</cite> embedded into the low-rank space.</p>
<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>
<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
instances in <cite>X</cite></p>
</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>
<dd><p>Predicts posterior probabilities for the instances <cite>X</cite> embedded into the low-rank space.</p>
<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>
<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>
</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>
<dd><p>Set the parameters of this estimator.</p>
<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
<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>
@ -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
<cite>n_components</cite> &gt;= <cite>X.shape[1]</cite>.</p>
<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>
<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>
</dl>
@ -168,15 +386,15 @@ and eventually also <cite>n_components</cite> for <cite>TruncatedSVD</cite></p>
</section>
<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">
<dt class="sig sig-object py" id="quapy.classification.neural.CNNnet">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.classification.neural.</span></span><span class="sig-name descname"><span class="pre">CNNnet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">vocabulary_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">256</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repr_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_heights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[3,</span> <span class="pre">5,</span> <span class="pre">7]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.CNNnet" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.classification.neural.TextClassifierNet" title="quapy.classification.neural.TextClassifierNet"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.classification.neural.TextClassifierNet</span></code></a></p>
<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">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
Convolutional Neural Networks.</p>
<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">
<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>
@ -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
next-to-last layer).</p>
<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 torchs <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>
</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
<cite>n_samples</cite> is the number of documents, and <cite>n_dimensions</cite> is the
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>
<dd><p>Get hyper-parameters for this estimator</p>
<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>
</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">
<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>
<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>
</dl>
@ -235,12 +458,12 @@ dimensionality of the embedding</p>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.classification.neural.LSTMnet">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.classification.neural.</span></span><span class="sig-name descname"><span class="pre">LSTMnet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">vocabulary_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">256</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repr_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lstm_class_nlayers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.LSTMnet" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.classification.neural.TextClassifierNet" title="quapy.classification.neural.TextClassifierNet"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.classification.neural.TextClassifierNet</span></code></a></p>
<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">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
Long Short Term Memory networks.</p>
<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">
<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>
@ -258,11 +481,11 @@ Long Short Term Memory networks.</p>
<dd><p>Embeds documents (i.e., performs the forward pass up to the
next-to-last layer).</p>
<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 torchs <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>
</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
<cite>n_samples</cite> is the number of documents, and <cite>n_dimensions</cite> is the
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>
<dd><p>Get hyper-parameters for this estimator</p>
<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>
</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">
<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>
<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>
</dl>
@ -296,11 +524,11 @@ dimensionality of the embedding</p>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.classification.neural.</span></span><span class="sig-name descname"><span class="pre">NeuralClassifierTrainer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">net</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="#quapy.classification.neural.TextClassifierNet" title="quapy.classification.neural.TextClassifierNet"><span class="pre">quapy.classification.neural.TextClassifierNet</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">patience</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">64</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size_test</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">512</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cpu'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpointpath</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'../checkpoint/classifier_net.dat'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer" title="Permalink to this definition"></a></dt>
<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>
<p>Trains a neural network for text classification.</p>
<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">
<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>
@ -319,10 +547,10 @@ according to the evaluation in the held-out validation split (default ../chec
</dl>
<dl class="py property">
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.device">
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">device</span></span><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.device" title="Permalink to this definition"></a></dt>
<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>
<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>
</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>
<dd><p>Fits the model according to the given training data.</p>
<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">
<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>val_split</strong> proportion of training documents to be taken as the validation set (default 0.3)</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p></p>
</dd>
</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>
<dd><p>Get hyper-parameters for this estimator</p>
<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>
</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>
<dd><p>Predicts labels for the instances</p>
<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>
<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
instances in <cite>X</cite></p>
</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>
<dd><p>Predicts posterior probabilities for the instances</p>
<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>
<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>
</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>
<dd><p>Reinitialize the network parameters</p>
<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">
<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>
@ -407,7 +635,7 @@ instances in <cite>X</cite></p>
In this current version, parameter names for the trainer and learner should
be disjoint.</p>
<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>
</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>
<dd><p>Returns the embeddings of the instances</p>
<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>
<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,
where <cite>embed_size</cite> is defined by the classification network</p>
</dd>
@ -432,15 +660,15 @@ where <cite>embed_size</cite> is defined by the classification network</p>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.classification.neural.</span></span><span class="sig-name descname"><span class="pre">TextClassifierNet</span></span><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.modules.module.Module</span></code></p>
<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">Module</span></code></p>
<p>Abstract Text classifier (<cite>torch.nn.Module</cite>)</p>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.dimensions">
<span class="sig-name descname"><span class="pre">dimensions</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.dimensions" title="Permalink to this definition"></a></dt>
<dd><p>Gets the number of dimensions of the embedding space</p>
<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>
</dl>
@ -448,15 +676,15 @@ where <cite>embed_size</cite> is defined by the classification network</p>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.document_embedding">
<em class="property"><span class="pre">abstract</span> </em><span class="sig-name descname"><span class="pre">document_embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.document_embedding" title="Permalink to this definition"></a></dt>
<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
next-to-last layer).</p>
<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 torchs <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>
</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
<cite>n_samples</cite> is the number of documents, and <cite>n_dimensions</cite> is the
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>
<dd><p>Performs the forward pass.</p>
<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 torchs <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>
</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
for each of the instances and classes</p>
</dd>
@ -482,10 +710,10 @@ for each of the instances and classes</p>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.get_params">
<em class="property"><span class="pre">abstract</span> </em><span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.get_params" title="Permalink to this definition"></a></dt>
<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>
<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>
</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>
<dd><p>Predicts posterior probabilities for the instances in <cite>x</cite></p>
<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>
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>
</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>
</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">
<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>
<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>
</dl>
@ -528,11 +761,11 @@ is length of the pad in the batch</p>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.classification.neural.TorchDataset">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.classification.neural.</span></span><span class="sig-name descname"><span class="pre">TorchDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.TorchDataset" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.utils.data.dataset.Dataset</span></code></p>
<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">Dataset</span></code></p>
<p>Transforms labelled instances into a Torchs <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">
<dt class="field-odd">Parameters</dt>
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<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>
@ -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
the batch</p>
<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">
<li><p><strong>batch_size</strong> batch size</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>
</ul>
</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>
</dl>
@ -565,11 +798,11 @@ applied, meaning that if the longest document in the batch is shorter than
</section>
<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">
<dt class="sig sig-object py" id="quapy.classification.svmperf.SVMperf">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.classification.svmperf.</span></span><span class="sig-name descname"><span class="pre">SVMperf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">svmperf_base</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">C</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'01'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.svmperf.SVMperf" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.base.BaseEstimator</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.base.ClassifierMixin</span></code></p>
<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">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.
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>
@ -582,7 +815,7 @@ for further details.</p>
</ul>
</div></blockquote>
<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">
<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>
@ -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>
<dd><p>Evaluate the decision function for the samples in <cite>X</cite>.</p>
<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">
<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>
</ul>
</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>
</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>
<dd><p>Trains the SVM for the multivariate performance loss</p>
<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">
<li><p><strong>X</strong> training instances</p></li>
<li><p><strong>y</strong> a binary vector of labels</p></li>
</ul>
</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>
</dl>
@ -628,12 +861,16 @@ for further details.</p>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.svmperf.SVMperf.predict">
<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.svmperf.SVMperf.predict" title="Permalink to this definition"></a></dt>
<dd><p>Predicts labels for the instances <cite>X</cite>
:param X: array-like of shape <cite>(n_samples, n_features)</cite> instances to classify
:return: a <cite>numpy</cite> array of length <cite>n</cite> containing the label predictions, where <cite>n</cite> is the number of</p>
<blockquote>
<div><p>instances in <cite>X</cite></p>
</div></blockquote>
<dd><p>Predicts labels for the instances <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> instances to classify</p>
</dd>
<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>
<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>
<dd><p>Set the hyper-parameters for svm-perf. Currently, only the <cite>C</cite> parameter is supported</p>
<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>
</dl>
@ -649,14 +886,14 @@ for further details.</p>
<dl class="py attribute">
<dt class="sig sig-object py" id="quapy.classification.svmperf.SVMperf.valid_losses">
<span class="sig-name descname"><span class="pre">valid_losses</span></span><em class="property"> <span class="pre">=</span> <span class="pre">{'01':</span> <span class="pre">0,</span> <span class="pre">'f1':</span> <span class="pre">1,</span> <span class="pre">'kld':</span> <span class="pre">12,</span> <span class="pre">'mae':</span> <span class="pre">26,</span> <span class="pre">'mrae':</span> <span class="pre">27,</span> <span class="pre">'nkld':</span> <span class="pre">13,</span> <span class="pre">'q':</span> <span class="pre">22,</span> <span class="pre">'qacc':</span> <span class="pre">23,</span> <span class="pre">'qf1':</span> <span class="pre">24,</span> <span class="pre">'qgm':</span> <span class="pre">25}</span></em><a class="headerlink" href="#quapy.classification.svmperf.SVMperf.valid_losses" title="Permalink to this definition"></a></dt>
<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></dl>
</section>
<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>
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@ -2,11 +2,11 @@
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<link rel="stylesheet" type="text/css" href="_static/pygments.css" />
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@ -14,7 +14,9 @@
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@ -37,7 +39,7 @@
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@ -97,13 +99,13 @@
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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.

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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.
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.
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.
@ -51,47 +59,31 @@
multiclass quantification. That is to say, one could get a multiclass variant of the (originally binary) HDy
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:
- 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():
this is not working; I don't know how to make the isinstance work. Looks like there is some problem with the
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)
--------------
- OneVsAll is duplicated (in aggregative and in general), and is not well documented. It is not working either.
Check method def __parallel(self, func, *args, **kwargs) in aggregative.OneVsAll
- update unit tests
- Policies should be able to set their output to "labelled_collection" or "instances_prevalence" or something similar.
- 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...
- update Wikis...
- 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?
- test LoadSamplesFromDirectory (in protocols.py)
- improve plots?
- 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
- improve plots
- documentation of protocols is incomplete
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

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
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

View File

@ -12,12 +12,18 @@ import numpy as np
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
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>`_:
: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
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.
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 verbose: whether or not to display information in the standard output
"""
@ -38,6 +44,13 @@ class RecalibratedProbabilisticClassifierBase(BaseEstimator, RecalibratedProbabi
self.verbose = verbose
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
if isinstance(k, int):
if k < 2:
@ -49,6 +62,15 @@ class RecalibratedProbabilisticClassifierBase(BaseEstimator, RecalibratedProbabi
return self.fit_cv(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(
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
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)
self.classifier.fit(Xtr, ytr)
posteriors = self.classifier.predict_proba(Xva)
@ -66,32 +98,49 @@ class RecalibratedProbabilisticClassifierBase(BaseEstimator, RecalibratedProbabi
return self
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)
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)
return self.calibration_function(posteriors)
@property
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_
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>`_:
: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
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.
training set afterwards. Default value is 5.
: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
"""
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.calibrator = NoBiasVectorScaling(verbose=verbose)
self.val_split = val_split
@ -101,19 +150,19 @@ class NBVSCalibration(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>`_:
: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
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.
training set afterwards. Default value is 5.
: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
"""
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.calibrator = TempScaling(verbose=verbose, bias_positions='all')
self.val_split = val_split
@ -123,19 +172,19 @@ class BCTSCalibration(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>`_:
: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
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.
training set afterwards. Default value is 5.
: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
"""
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.calibrator = TempScaling(verbose=verbose)
self.val_split = val_split
@ -145,19 +194,19 @@ class TSCalibration(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>`_:
: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
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.
training set afterwards. Default value is 5.
: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
"""
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.calibrator = VectorScaling(verbose=verbose)
self.val_split = val_split

View File

@ -94,6 +94,7 @@ class SVMperf(BaseEstimator, ClassifierMixin):
def predict(self, X):
"""
Predicts labels for the instances `X`
: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
instances in `X`

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):
"""
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
assert task in LEQUA2022_TASKS, \

View File

@ -88,7 +88,7 @@ def standardize(dataset: Dataset, inplace=False):
: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
:class:`quapy.data.base.Dataset` is to be returned
:return:
:return: an instance of :class:`quapy.data.base.Dataset`
"""
s = StandardScaler(copy=not inplace)
training = s.fit_transform(dataset.training.instances)
@ -110,7 +110,7 @@ def index(dataset: Dataset, min_df=5, inplace=False, **kwargs):
:param min_df: minimum number of occurrences below which the term is replaced by a `UNK` index
:param inplace: whether or not to apply the transformation inplace (True), or to a new copy (False, default)
:param kwargs: the rest of parameters of the transformation (as for 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>_`)
:return: a new :class:`quapy.data.base.Dataset` (if inplace=False) or a reference to the current
:class:`quapy.data.base.Dataset` (inplace=True) consisting of lists of integer values representing indices.
"""
@ -147,7 +147,8 @@ class IndexTransformer:
contains, and that would be generated by sklearn's
`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):
@ -179,7 +180,7 @@ class IndexTransformer:
"""
# 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'
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)
return np.asarray(indexed)

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

View File

@ -11,11 +11,6 @@ def from_name(err_name):
"""
assert err_name in ERROR_NAMES, f'unknown error {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

View File

@ -70,7 +70,7 @@ def HellingerDistance(P, Q):
The HD for two discrete distributions of `k` bins is defined as:
.. 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 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))
def TopsoeDistance(P, Q, epsilon=1e-20):
""" Topsoe
"""
return np.sum(P*np.log((2*P+epsilon)/(P+Q+epsilon)) +
Q*np.log((2*Q+epsilon)/(P+Q+epsilon)))
Topsoe distance between two (discretized) distributions `P` and `Q`.
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):
@ -136,7 +146,6 @@ def adjusted_quantification(prevalence_estim, tpr, fpr, clip=True):
.. math::
ACC(p) = \\frac{ p - fpr }{ tpr - fpr }
:param prevalence_estim: float, the estimated value for the positive class
:param tpr: float, the true positive rate of the classifier
:param fpr: float, the false positive rate of the classifier
@ -184,7 +193,7 @@ def __num_prevalence_combinations_depr(n_prevpoints:int, n_classes:int, n_repeat
:param n_prevpoints: integer, number of prevalence points.
:param n_repeats: integer, number of repetitions for each prevalence combination
:return: The number of possible combinations. For example, if n_classes=2, n_prevpoints=5, n_repeats=1, then the
number of possible combinations are 5, i.e.: [0,1], [0.25,0.75], [0.50,0.50], [0.75,0.25], and [1.0,0.0]
number of possible combinations are 5, i.e.: [0,1], [0.25,0.75], [0.50,0.50], [0.75,0.25], and [1.0,0.0]
"""
__cache={}
def __f(nc,np):
@ -216,7 +225,7 @@ def num_prevalence_combinations(n_prevpoints:int, n_classes:int, n_repeats:int=1
:param n_prevpoints: integer, number of prevalence points.
:param n_repeats: integer, number of repetitions for each prevalence combination
:return: The number of possible combinations. For example, if n_classes=2, n_prevpoints=5, n_repeats=1, then the
number of possible combinations are 5, i.e.: [0,1], [0.25,0.75], [0.50,0.50], [0.75,0.25], and [1.0,0.0]
number of possible combinations are 5, i.e.: [0,1], [0.25,0.75], [0.50,0.50], [0.75,0.25], and [1.0,0.0]
"""
N = n_prevpoints-1
C = n_classes
@ -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
`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_repeats: integer, number of repetitions for each prevalence combination
: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):
"""
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
: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')
return False
return True

View File

@ -76,7 +76,7 @@ class AggregativeQuantifier(BaseQuantifier):
by the classifier.
: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)
return self.aggregate(classif_predictions)
@ -87,7 +87,7 @@ class AggregativeQuantifier(BaseQuantifier):
Implements the aggregation 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):
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
# ------------------------------------
@ -198,7 +185,7 @@ def cross_generate_predictions(
n_jobs
):
n_jobs = qp.get_njobs(n_jobs)
n_jobs = qp._get_njobs(n_jobs)
if isinstance(val_split, int):
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.
: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_)
@ -328,7 +315,7 @@ class ACC(AggregativeQuantifier):
def __init__(self, classifier: BaseEstimator, val_split=0.4, n_jobs=None):
self.classifier = classifier
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):
"""
@ -435,7 +422,7 @@ class PACC(AggregativeProbabilisticQuantifier):
def __init__(self, classifier: BaseEstimator, val_split=0.4, n_jobs=None):
self.classifier = classifier
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):
"""
@ -660,6 +647,20 @@ class HDy(AggregativeProbabilisticQuantifier, BinaryQuantifier):
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):
"""
`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])
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):
"""
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.
: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
validation distribution.
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):
self.classifier = classifier
self.val_split = val_split
self.nbins = nbins
@ -1020,7 +1008,7 @@ class ThresholdOptimization(AggregativeQuantifier, BinaryQuantifier):
def __init__(self, classifier: BaseEstimator, val_split=0.4, n_jobs=None):
self.classifier = classifier
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):
self._check_binary(data, "Threshold Optimization")
@ -1277,7 +1265,7 @@ class OneVsAll(AggregativeQuantifier):
assert isinstance(self.binary_quantifier, AggregativeQuantifier), \
f'{self.binary_quantifier} does not seem to be of type Aggregative'
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):
assert not data.binary, \

View File

@ -32,29 +32,10 @@ class BaseQuantifier(BaseEstimator):
Generate class prevalence estimates for the sample's instances
: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):
"""
@ -77,7 +58,7 @@ class OneVsAllGeneric:
assert isinstance(binary_quantifier, BaseQuantifier), \
f'{binary_quantifier} does not seem to be a 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):
assert not data.binary, \

View File

@ -84,7 +84,7 @@ class Ensemble(BaseQuantifier):
self.red_size = red_size
self.policy = policy
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.verbose = verbose
self.max_sample_size = max_sample_size
@ -147,7 +147,7 @@ class Ensemble(BaseQuantifier):
with the abstract class).
Instead, use `Ensemble(GridSearchQ(q),...)`, with `q` a Quantifier (recommended), or
`Ensemble(Q(GridSearchCV(l)))` with `Q` a quantifier class that has a classifier `l` optimized for
classification (not recommended).
classification (not recommended).
:param parameters: dictionary
:return: raises an Exception
@ -163,10 +163,12 @@ class Ensemble(BaseQuantifier):
with the abstract class).
Instead, use `Ensemble(GridSearchQ(q),...)`, with `q` a Quantifier (recommended), or
`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
"""
raise NotImplementedError()
def _accuracy_policy(self, error_name):

View File

@ -21,7 +21,6 @@ class MaximumLikelihoodPrevalenceEstimation(BaseQuantifier):
:param data: the training sample
:return: self
"""
self._classes_ = data.classes_
self.estimated_prevalence = data.prevalence()
return self
@ -34,29 +33,3 @@ class MaximumLikelihoodPrevalenceEstimation(BaseQuantifier):
"""
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

View File

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

View File

@ -11,13 +11,17 @@ from glob import glob
class AbstractProtocol(metaclass=ABCMeta):
"""
Abstract parent class for sample generation protocols.
"""
@abstractmethod
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):
"""
An AbstractStochasticSeededProtocol is a protocol that generates, via any random procedure (e.g.,
via random sapling), sequences of `LabelledCollection` samples. The protocol abstraction enforces
the object to be instantiated using a seed, so that the sequence can be completely replicated.
An `AbstractStochasticSeededProtocol` is a protocol that generates, via any random procedure (e.g.,
via random sampling), sequences of :class:`quapy.data.base.LabelledCollection` samples.
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
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,
@ -128,7 +133,8 @@ class APP(AbstractStochasticSeededProtocol, OnLabelledCollectionProtocol):
combination of prevalence values is indicated by `repeats`.
: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
grid (default is 21)
: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
"""
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)
self.data = data
self.sample_size = sample_size
self.sample_size = qp._get_sample_size(sample_size)
self.n_prevalences = n_prevalences
self.repeats = repeats
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.
: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 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
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)
self.data = data
self.sample_size = sample_size
self.sample_size = qp._get_sample_size(sample_size)
self.repeats = repeats
self.random_state = random_state
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.
: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 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
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)
self.data = data
self.sample_size = sample_size
self.sample_size = qp._get_sample_size(sample_size)
self.repeats = repeats
self.random_state = random_state
self.collator = OnLabelledCollectionProtocol.get_collator(return_type)
@ -259,32 +269,7 @@ class USimplexPP(AbstractStochasticSeededProtocol, OnLabelledCollectionProtocol)
return self.repeats
# class LoadSamplesFromDirectory(AbstractProtocol):
#
# 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):
class DomainMixer(AbstractStochasticSeededProtocol):
"""
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,
random_state=None,
return_type='sample_prev'):
super(CovariateShiftPP, self).__init__(random_state)
super(DomainMixer, self).__init__(random_state)
self.A = domainA
self.B = domainB
self.sample_size = sample_size
self.sample_size = qp._get_sample_size(sample_size)
self.repeats = repeats
if prevalence is None:
self.prevalence = domainA.prevalence()

View File

@ -4,6 +4,7 @@ from sklearn.linear_model import LogisticRegression
from sklearn.svm import LinearSVC
import quapy as qp
from quapy.method.base import BinaryQuantifier
from quapy.data import Dataset, LabelledCollection
from quapy.method import AGGREGATIVE_METHODS, NON_AGGREGATIVE_METHODS, EXPLICIT_LOSS_MINIMIZATION_METHODS
from quapy.method.aggregative import ACC, PACC, HDy
@ -21,7 +22,7 @@ learners = [LogisticRegression, LinearSVC]
def test_aggregative_methods(dataset: Dataset, 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}')
return
@ -45,7 +46,7 @@ def test_elm_methods(dataset: Dataset, elm_method):
print('Missing SVMperf binary program, skipping test')
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}')
return
@ -64,7 +65,7 @@ def test_elm_methods(dataset: Dataset, elm_method):
def test_non_aggregative_methods(dataset: Dataset, 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}')
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):
qp.environ['SAMPLE_SIZE'] = len(dataset.training)
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}')
return
@ -120,7 +121,7 @@ def test_quanet_method():
from quapy.method.meta import QuaNet
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}')
return
@ -138,7 +139,7 @@ def models_to_test_for_str_label_names():
models = list()
learner = LogisticRegression
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:
models.append(method())
return models
@ -156,6 +157,7 @@ def test_str_label_names(model):
dataset.test.sampling(1000, *dataset.test.prevalence()))
qp.data.preprocessing.text2tfidf(dataset, min_df=5, inplace=True)
numpy.random.seed(0)
model.fit(dataset.training)
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]),
LabelledCollection(dataset.test.instances,
['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)
str_estim_prevalences = model.quantify(dataset_str.test.instances)

View File

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

View File

@ -1,7 +1,7 @@
import unittest
import numpy as np
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=''):
@ -94,7 +94,7 @@ class TestProtocols(unittest.TestCase):
def test_covariate_shift_replicate(self):
dataA = mock_labelled_collection('domA')
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)
samples2 = samples_to_str(p)
@ -104,7 +104,7 @@ class TestProtocols(unittest.TestCase):
def test_covariate_shift_not_replicate(self):
dataA = mock_labelled_collection('domA')
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)
samples2 = samples_to_str(p)

View File

@ -22,7 +22,7 @@ def _get_parallel_slices(n_tasks, 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
that already works with a list of arguments.
@ -128,6 +128,7 @@ def create_if_not_exist(path):
def get_quapy_home():
"""
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
"""
@ -162,7 +163,7 @@ def save_text_file(path, text):
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:
>>> 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.
: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(0.9, epoch=0)
>>> earlystop(0.7, epoch=1)
@ -206,14 +203,14 @@ class EarlyStop:
>>> earlystop.best_epoch # is 1
>>> 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.
: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 STOP: flag (boolean) indicating the stopping condition
:ivar IMPROVED: flag (boolean) indicating whether there was an improvement in the last call
"""
def __init__(self, patience, lower_is_better=True):
@ -243,4 +240,5 @@ class EarlyStop:
else:
self.patience -= 1
if self.patience <= 0:
self.STOP = True
self.STOP = True