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<section id="quapy-classification-package">
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<h1>quapy.classification package<a class="headerlink" href="#quapy-classification-package" title="Permalink to this heading"></a></h1>
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<section id="submodules">
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<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>
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</section>
<section id="module-quapy.classification.methods">
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<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>
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<dl class="py class">
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<dt class="sig sig-object py" id="quapy.classification.methods.LowRankLogisticRegression">
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<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>
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<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
<code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.neural.QuaNet</span></code> on array-like real-valued instances.
The transformation consists of applying <code class="xref py py-class docutils literal notranslate"><span class="pre">sklearn.decomposition.TruncatedSVD</span></code>
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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>
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<dl class="field-list simple">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<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
<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html">Logistic Regression</a> classifier</p></li>
</ul>
</dd>
</dl>
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<dl class="py method">
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<dt class="sig sig-object py" id="quapy.classification.methods.LowRankLogisticRegression.fit">
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.methods.LowRankLogisticRegression.fit" title="Permalink to this definition"></a></dt>
<dd><p>Fit the model according to the given training data. The fit consists of
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fitting <cite>TruncatedSVD</cite> and then <cite>LogisticRegression</cite> on the low-rank representation.</p>
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<dl class="field-list simple">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<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>
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</dd>
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<dt class="field-even">Returns<span class="colon">:</span></dt>
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<dd class="field-even"><p><cite>self</cite></p>
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</dd>
</dl>
</dd></dl>
<dl class="py method">
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<dt class="sig sig-object py" id="quapy.classification.methods.LowRankLogisticRegression.get_params">
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.methods.LowRankLogisticRegression.get_params" title="Permalink to this definition"></a></dt>
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<dd><p>Get hyper-parameters for this estimator.</p>
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<dl class="field-list simple">
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<dt class="field-odd">Returns<span class="colon">:</span></dt>
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<dd class="field-odd"><p>a dictionary with parameter names mapped to their values</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
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<dt class="sig sig-object py" id="quapy.classification.methods.LowRankLogisticRegression.predict">
<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.methods.LowRankLogisticRegression.predict" title="Permalink to this definition"></a></dt>
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<dd><p>Predicts labels for the instances <cite>X</cite> embedded into the low-rank space.</p>
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<dl class="field-list simple">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<dd class="field-odd"><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> instances to classify</p>
</dd>
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<dt class="field-even">Returns<span class="colon">:</span></dt>
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<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>
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<dl class="py method">
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<dt class="sig sig-object py" id="quapy.classification.methods.LowRankLogisticRegression.predict_proba">
<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.methods.LowRankLogisticRegression.predict_proba" title="Permalink to this definition"></a></dt>
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<dd><p>Predicts posterior probabilities for the instances <cite>X</cite> embedded into the low-rank space.</p>
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<dl class="field-list simple">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<dd class="field-odd"><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> instances to classify</p>
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</dd>
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<dt class="field-even">Returns<span class="colon">:</span></dt>
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<dd class="field-even"><p>array-like of shape <cite>(n_samples, n_classes)</cite> with the posterior probabilities</p>
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</dd>
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</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.methods.LowRankLogisticRegression.set_params">
<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.methods.LowRankLogisticRegression.set_params" title="Permalink to this definition"></a></dt>
<dd><p>Set the parameters of this estimator.</p>
<dl class="field-list simple">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<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>
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and eventually also <cite>n_components</cite> for <cite>TruncatedSVD</cite></p>
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</dd>
</dl>
</dd></dl>
<dl class="py method">
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<dt class="sig sig-object py" id="quapy.classification.methods.LowRankLogisticRegression.transform">
<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.methods.LowRankLogisticRegression.transform" title="Permalink to this definition"></a></dt>
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<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>
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<dl class="field-list simple">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<dd class="field-odd"><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> instances to embed</p>
</dd>
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<dt class="field-even">Returns<span class="colon">:</span></dt>
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<dd class="field-even"><p>array-like of shape <cite>(n_samples, n_components)</cite> with the embedded instances</p>
</dd>
</dl>
</dd></dl>
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</dd></dl>
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</section>
<section id="module-quapy.classification.neural">
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<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>
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<dl class="py class">
<dt class="sig sig-object py" id="quapy.classification.neural.CNNnet">
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<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>
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<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">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<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>
<li><p><strong>embedding_size</strong> – the dimensionality of the word embeddings space (default 100)</p></li>
<li><p><strong>hidden_size</strong> – the dimensionality of the hidden space (default 256)</p></li>
<li><p><strong>repr_size</strong> – the dimensionality of the document embeddings space (default 100)</p></li>
<li><p><strong>kernel_heights</strong> – list of kernel lengths (default [3,5,7]), i.e., the number of
consecutive tokens that each kernel covers</p></li>
<li><p><strong>stride</strong> – convolutional stride (default 1)</p></li>
<li><p><strong>stride</strong> – convolutional pad (default 0)</p></li>
<li><p><strong>drop_p</strong> – drop probability for dropout (default 0.5)</p></li>
</ul>
</dd>
</dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.neural.CNNnet.document_embedding">
<span class="sig-name descname"><span class="pre">document_embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.CNNnet.document_embedding" title="Permalink to this definition"></a></dt>
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<dd><p>Embeds documents (i.e., performs the forward pass up to the
next-to-last layer).</p>
<dl class="field-list simple">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<dd class="field-odd"><p><strong>input</strong> – a batch of instances, typically generated by a torch’s <cite>DataLoader</cite>
instance (see <a class="reference internal" href="#quapy.classification.neural.TorchDataset" title="quapy.classification.neural.TorchDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.classification.neural.TorchDataset</span></code></a>)</p>
</dd>
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<dt class="field-even">Returns<span class="colon">:</span></dt>
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<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>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.neural.CNNnet.get_params">
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.CNNnet.get_params" title="Permalink to this definition"></a></dt>
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<dd><p>Get hyper-parameters for this estimator</p>
<dl class="field-list simple">
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<dt class="field-odd">Returns<span class="colon">:</span></dt>
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<dd class="field-odd"><p>a dictionary with parameter names mapped to their values</p>
</dd>
</dl>
</dd></dl>
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<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>
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<dl class="py property">
<dt class="sig sig-object py" id="quapy.classification.neural.CNNnet.vocabulary_size">
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<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>
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<dd><p>Return the size of the vocabulary</p>
<dl class="field-list simple">
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<dt class="field-odd">Returns<span class="colon">:</span></dt>
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<dd class="field-odd"><p>integer</p>
</dd>
</dl>
</dd></dl>
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</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.classification.neural.LSTMnet">
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<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>
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<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">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<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>
<li><p><strong>embedding_size</strong> – the dimensionality of the word embeddings space (default 100)</p></li>
<li><p><strong>hidden_size</strong> – the dimensionality of the hidden space (default 256)</p></li>
<li><p><strong>repr_size</strong> – the dimensionality of the document embeddings space (default 100)</p></li>
<li><p><strong>lstm_class_nlayers</strong> – number of LSTM layers (default 1)</p></li>
<li><p><strong>drop_p</strong> – drop probability for dropout (default 0.5)</p></li>
</ul>
</dd>
</dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.neural.LSTMnet.document_embedding">
<span class="sig-name descname"><span class="pre">document_embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.LSTMnet.document_embedding" title="Permalink to this definition"></a></dt>
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<dd><p>Embeds documents (i.e., performs the forward pass up to the
next-to-last layer).</p>
<dl class="field-list simple">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<dd class="field-odd"><p><strong>x</strong> – a batch of instances, typically generated by a torch’s <cite>DataLoader</cite>
instance (see <a class="reference internal" href="#quapy.classification.neural.TorchDataset" title="quapy.classification.neural.TorchDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.classification.neural.TorchDataset</span></code></a>)</p>
</dd>
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<dt class="field-even">Returns<span class="colon">:</span></dt>
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<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>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.neural.LSTMnet.get_params">
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.LSTMnet.get_params" title="Permalink to this definition"></a></dt>
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<dd><p>Get hyper-parameters for this estimator</p>
<dl class="field-list simple">
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<dt class="field-odd">Returns<span class="colon">:</span></dt>
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<dd class="field-odd"><p>a dictionary with parameter names mapped to their values</p>
</dd>
</dl>
</dd></dl>
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<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>
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<dl class="py property">
<dt class="sig sig-object py" id="quapy.classification.neural.LSTMnet.vocabulary_size">
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<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>
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<dd><p>Return the size of the vocabulary</p>
<dl class="field-list simple">
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<dt class="field-odd">Returns<span class="colon">:</span></dt>
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<dd class="field-odd"><p>integer</p>
</dd>
</dl>
</dd></dl>
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</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer">
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<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>
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<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
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<p>Trains a neural network for text classification.</p>
<dl class="field-list simple">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<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>
<li><p><strong>weight_decay</strong> – weight decay (default 0)</p></li>
<li><p><strong>patience</strong> – number of epochs that do not show any improvement in validation
to wait before applying early stop (default 10)</p></li>
<li><p><strong>epochs</strong> – maximum number of training epochs (default 200)</p></li>
<li><p><strong>batch_size</strong> – batch size for training (default 64)</p></li>
<li><p><strong>batch_size_test</strong> – batch size for test (default 512)</p></li>
<li><p><strong>padding_length</strong> – maximum number of tokens to consider in a document (default 300)</p></li>
<li><p><strong>device</strong> – specify ‘cpu’ (default) or ‘cuda’ for enabling gpu</p></li>
<li><p><strong>checkpointpath</strong> – where to store the parameters of the best model found so far
according to the evaluation in the held-out validation split (default ‘../checkpoint/classifier_net.dat’)</p></li>
</ul>
</dd>
</dl>
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<dl class="py property">
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.device">
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<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>
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<dd><p>Gets the device in which the network is allocated</p>
<dl class="field-list simple">
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<dt class="field-odd">Returns<span class="colon">:</span></dt>
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<dd class="field-odd"><p>device</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.fit">
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.fit" title="Permalink to this definition"></a></dt>
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<dd><p>Fits the model according to the given training data.</p>
<dl class="field-list simple">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<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>
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<dt class="field-even">Returns<span class="colon">:</span></dt>
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<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.get_params">
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.get_params" title="Permalink to this definition"></a></dt>
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<dd><p>Get hyper-parameters for this estimator</p>
<dl class="field-list simple">
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<dt class="field-odd">Returns<span class="colon">:</span></dt>
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<dd class="field-odd"><p>a dictionary with parameter names mapped to their values</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.predict">
<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.predict" title="Permalink to this definition"></a></dt>
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<dd><p>Predicts labels for the instances</p>
<dl class="field-list simple">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<dd class="field-odd"><p><strong>instances</strong> – list of lists of indexed tokens</p>
</dd>
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<dt class="field-even">Returns<span class="colon">:</span></dt>
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<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>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.predict_proba">
<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.predict_proba" title="Permalink to this definition"></a></dt>
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<dd><p>Predicts posterior probabilities for the instances</p>
<dl class="field-list simple">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<dd class="field-odd"><p><strong>X</strong> – array-like of shape <cite>(n_samples, n_features)</cite> instances to classify</p>
</dd>
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<dt class="field-even">Returns<span class="colon">:</span></dt>
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<dd class="field-even"><p>array-like of shape <cite>(n_samples, n_classes)</cite> with the posterior probabilities</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.reset_net_params">
<span class="sig-name descname"><span class="pre">reset_net_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">vocab_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.reset_net_params" title="Permalink to this definition"></a></dt>
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<dd><p>Reinitialize the network parameters</p>
<dl class="field-list simple">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<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>
</ul>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.set_params">
<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.set_params" title="Permalink to this definition"></a></dt>
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<dd><p>Set the parameters of this trainer and the learner it is training.
In this current version, parameter names for the trainer and learner should
be disjoint.</p>
<dl class="field-list simple">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<dd class="field-odd"><p><strong>params</strong> – a <cite>**kwargs</cite> dictionary with the parameters</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.neural.NeuralClassifierTrainer.transform">
<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.NeuralClassifierTrainer.transform" title="Permalink to this definition"></a></dt>
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<dd><p>Returns the embeddings of the instances</p>
<dl class="field-list simple">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<dd class="field-odd"><p><strong>instances</strong> – list of lists of indexed tokens</p>
</dd>
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<dt class="field-even">Returns<span class="colon">:</span></dt>
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<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>
</dl>
</dd></dl>
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</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet">
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<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>
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<p>Abstract Text classifier (<cite>torch.nn.Module</cite>)</p>
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<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>
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<dd><p>Gets the number of dimensions of the embedding space</p>
<dl class="field-list simple">
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<dt class="field-odd">Returns<span class="colon">:</span></dt>
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<dd class="field-odd"><p>integer</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.document_embedding">
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<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>
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<dd><p>Embeds documents (i.e., performs the forward pass up to the
next-to-last layer).</p>
<dl class="field-list simple">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<dd class="field-odd"><p><strong>x</strong> – a batch of instances, typically generated by a torch’s <cite>DataLoader</cite>
instance (see <a class="reference internal" href="#quapy.classification.neural.TorchDataset" title="quapy.classification.neural.TorchDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.classification.neural.TorchDataset</span></code></a>)</p>
</dd>
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<dt class="field-even">Returns<span class="colon">:</span></dt>
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<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>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.forward">
<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.forward" title="Permalink to this definition"></a></dt>
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<dd><p>Performs the forward pass.</p>
<dl class="field-list simple">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<dd class="field-odd"><p><strong>x</strong> – a batch of instances, typically generated by a torch’s <cite>DataLoader</cite>
instance (see <a class="reference internal" href="#quapy.classification.neural.TorchDataset" title="quapy.classification.neural.TorchDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.classification.neural.TorchDataset</span></code></a>)</p>
</dd>
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<dt class="field-even">Returns<span class="colon">:</span></dt>
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<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>
</dl>
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</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.get_params">
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<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>
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<dd><p>Get hyper-parameters for this estimator</p>
<dl class="field-list simple">
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<dt class="field-odd">Returns<span class="colon">:</span></dt>
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<dd class="field-odd"><p>a dictionary with parameter names mapped to their values</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.predict_proba">
<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.predict_proba" title="Permalink to this definition"></a></dt>
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<dd><p>Predicts posterior probabilities for the instances in <cite>x</cite></p>
<dl class="field-list simple">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<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>
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<dt class="field-even">Returns<span class="colon">:</span></dt>
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<dd class="field-even"><p>array-like of shape <cite>(n_samples, n_classes)</cite> with the posterior probabilities</p>
</dd>
</dl>
</dd></dl>
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<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>
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<dl class="py property">
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.vocabulary_size">
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<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>
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<dd><p>Return the size of the vocabulary</p>
<dl class="field-list simple">
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<dt class="field-odd">Returns<span class="colon">:</span></dt>
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<dd class="field-odd"><p>integer</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.neural.TextClassifierNet.xavier_uniform">
<span class="sig-name descname"><span class="pre">xavier_uniform</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.TextClassifierNet.xavier_uniform" title="Permalink to this definition"></a></dt>
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<dd><p>Performs Xavier initialization of the network parameters</p>
</dd></dl>
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</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.classification.neural.TorchDataset">
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<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>
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<p>Transforms labelled instances into a Torch’s <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.utils.data.DataLoader</span></code> object</p>
<dl class="field-list simple">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<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>
</ul>
</dd>
</dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.neural.TorchDataset.asDataloader">
<span class="sig-name descname"><span class="pre">asDataloader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad_length</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.neural.TorchDataset.asDataloader" title="Permalink to this definition"></a></dt>
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<dd><p>Converts the labelled collection into a Torch DataLoader with dynamic padding for
the batch</p>
<dl class="field-list simple">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<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>
<li><p><strong>pad_length</strong> – the maximum length for the list of tokens (dynamic padding is
applied, meaning that if the longest document in the batch is shorter than
<cite>pad_length</cite>, then the batch is padded up to its length, and not to <cite>pad_length</cite>.</p></li>
<li><p><strong>device</strong> – whether to allocate tensors in cpu or in cuda</p></li>
</ul>
</dd>
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<dt class="field-even">Returns<span class="colon">:</span></dt>
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<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>
</dd></dl>
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</dd></dl>
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</section>
<section id="module-quapy.classification.svmperf">
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<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>
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<dl class="py class">
<dt class="sig sig-object py" id="quapy.classification.svmperf.SVMperf">
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<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>
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<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>
for further details.</p>
<p>References:</p>
<blockquote>
<div><ul class="simple">
<li><p><a class="reference external" href="https://dl.acm.org/doi/abs/10.1145/2700406?casa_token=8D2fHsGCVn0AAAAA:ZfThYOvrzWxMGfZYlQW_y8Cagg-o_l6X_PcF09mdETQ4Tu7jK98mxFbGSXp9ZSO14JkUIYuDGFG0">Esuli et al.2015</a></p></li>
<li><p><a class="reference external" href="https://www.sciencedirect.com/science/article/abs/pii/S003132031400291X">Barranquero et al.2015</a></p></li>
</ul>
</div></blockquote>
<dl class="field-list simple">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<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>
<li><p><strong>verbose</strong> – set to True to print svm-perf std outputs</p></li>
<li><p><strong>loss</strong> – the loss to optimize for. Available losses are “01”, “f1”, “kld”, “nkld”, “q”, “qacc”, “qf1”, “qgm”, “mae”, “mrae”.</p></li>
</ul>
</dd>
</dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.svmperf.SVMperf.decision_function">
<span class="sig-name descname"><span class="pre">decision_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.svmperf.SVMperf.decision_function" title="Permalink to this definition"></a></dt>
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<dd><p>Evaluate the decision function for the samples in <cite>X</cite>.</p>
<dl class="field-list simple">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<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>
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<dt class="field-even">Returns<span class="colon">:</span></dt>
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<dd class="field-even"><p>array-like of shape <cite>(n_samples,)</cite> containing the decision scores of the instances</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.svmperf.SVMperf.fit">
<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.svmperf.SVMperf.fit" title="Permalink to this definition"></a></dt>
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<dd><p>Trains the SVM for the multivariate performance loss</p>
<dl class="field-list simple">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<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>
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<dt class="field-even">Returns<span class="colon">:</span></dt>
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<dd class="field-even"><p><cite>self</cite></p>
</dd>
</dl>
</dd></dl>
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<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>
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<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>
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</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.classification.svmperf.SVMperf.set_params">
<span class="sig-name descname"><span class="pre">set_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">parameters</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.classification.svmperf.SVMperf.set_params" title="Permalink to this definition"></a></dt>
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<dd><p>Set the hyper-parameters for svm-perf. Currently, only the <cite>C</cite> parameter is supported</p>
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<dl class="field-list simple">
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<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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<dd class="field-odd"><p><strong>parameters</strong> – a <cite>**kwargs</cite> dictionary <cite>{‘C’: &lt;float&gt;}</cite></p>
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</dd>
</dl>
</dd></dl>
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<dt class="sig sig-object py" id="quapy.classification.svmperf.SVMperf.valid_losses">
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<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>
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<dd></dd></dl>
</dd></dl>
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</section>
<section id="module-quapy.classification">
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<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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</section>
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<li><a class="reference internal" href="#">quapy.classification package</a><ul>
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<li><a class="reference internal" href="#quapy-classification-calibration">quapy.classification.calibration</a></li>
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