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<section id="quapy-method-package">
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<h1>quapy.method package<a class="headerlink" href="#quapy-method-package" title="Permalink to this headline"></a></h1>
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<section id="submodules">
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<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline"></a></h2>
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</section>
<section id="module-quapy.method.aggregative">
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<span id="quapy-method-aggregative-module"></span><h2>quapy.method.aggregative module<a class="headerlink" href="#module-quapy.method.aggregative" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.aggregative.ACC">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">ACC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">learner</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">sklearn.base.BaseEstimator</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">0.4</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.ACC" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeQuantifier" title="quapy.method.aggregative.AggregativeQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.AggregativeQuantifier</span></code></a></p>
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<p><a class="reference external" href="https://link.springer.com/article/10.1007/s10618-008-0097-y">Adjusted Classify &amp; Count</a>,
the “adjusted” variant of <a class="reference internal" href="#quapy.method.aggregative.CC" title="quapy.method.aggregative.CC"><code class="xref py py-class docutils literal notranslate"><span class="pre">CC</span></code></a>, that corrects the predictions of CC
according to the <cite>misclassification rates</cite>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>learner</strong> – a sklearn’s Estimator that generates a classifier</p></li>
<li><p><strong>val_split</strong> – indicates the proportion of data to be used as a stratified held-out validation set in which the
misclassification rates are to be estimated.
This parameter can be indicated as a real value (between 0 and 1, default 0.4), representing a proportion of
validation data, or as an integer, indicating that the misclassification rates should be estimated via
<cite>k</cite>-fold cross validation (this integer stands for the number of folds <cite>k</cite>), or as a
<a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> (the split itself).</p></li>
</ul>
</dd>
</dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.ACC.aggregate">
<span class="sig-name descname"><span class="pre">aggregate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_predictions</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.ACC.aggregate" title="Permalink to this definition"></a></dt>
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<dd><p>Implements the aggregation of label predictions.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>classif_predictions</strong> – <cite>np.ndarray</cite> of label predictions</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(self.n_classes_,)</cite> with class prevalence estimates.</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.ACC.classify">
<span class="sig-name descname"><span class="pre">classify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.ACC.classify" title="Permalink to this definition"></a></dt>
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<dd><p>Provides the label predictions for the given instances.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>instances</strong> – array-like</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>np.ndarray of shape <cite>(n_instances,)</cite> with label predictions</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.ACC.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">data</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_learner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span> </span><span class="pre">int</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.ACC.fit" title="Permalink to this definition"></a></dt>
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<dd><p>Trains a ACC quantifier.</p>
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<dl class="field-list simple">
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<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> – the training set</p></li>
<li><p><strong>fit_learner</strong> – set to False to bypass the training (the learner is assumed to be already fit)</p></li>
<li><p><strong>val_split</strong> – either a float in (0,1) indicating the proportion of training instances to use for
validation (e.g., 0.3 for using 30% of the training set as validation data), or a LabelledCollection
indicating the validation set itself, or an int indicating the number <cite>k</cite> of folds to be used in <cite>k</cite>-fold
cross validation to estimate the parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>self</p>
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</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.ACC.solve_adjustment">
<em class="property"><span class="pre">classmethod</span> </em><span class="sig-name descname"><span class="pre">solve_adjustment</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">PteCondEstim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prevs_estim</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.ACC.solve_adjustment" title="Permalink to this definition"></a></dt>
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<dd><p>Solves the system linear system <span class="math notranslate nohighlight">\(Ax = B\)</span> with <span class="math notranslate nohighlight">\(A\)</span> = <cite>PteCondEstim</cite> and <span class="math notranslate nohighlight">\(B\)</span> = <cite>prevs_estim</cite></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>PteCondEstim</strong> – a <cite>np.ndarray</cite> of shape <cite>(n_classes,n_classes,)</cite> with entry <cite>(i,j)</cite> being the estimate
of <span class="math notranslate nohighlight">\(P(y_i|y_j)\)</span>, that is, the probability that an instance that belongs to <span class="math notranslate nohighlight">\(y_j\)</span> ends up being
classified as belonging to <span class="math notranslate nohighlight">\(y_i\)</span></p></li>
<li><p><strong>prevs_estim</strong> – a <cite>np.ndarray</cite> of shape <cite>(n_classes,)</cite> with the class prevalence estimates</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>an adjusted <cite>np.ndarray</cite> of shape <cite>(n_classes,)</cite> with the corrected class prevalence estimates</p>
</dd>
</dl>
</dd></dl>
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</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="quapy.method.aggregative.AdjustedClassifyAndCount">
<span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">AdjustedClassifyAndCount</span></span><a class="headerlink" href="#quapy.method.aggregative.AdjustedClassifyAndCount" title="Permalink to this definition"></a></dt>
<dd><p>alias of <a class="reference internal" href="#quapy.method.aggregative.ACC" title="quapy.method.aggregative.ACC"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.ACC</span></code></a></p>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeProbabilisticQuantifier">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">AggregativeProbabilisticQuantifier</span></span><a class="headerlink" href="#quapy.method.aggregative.AggregativeProbabilisticQuantifier" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeQuantifier" title="quapy.method.aggregative.AggregativeQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.AggregativeQuantifier</span></code></a></p>
<p>Abstract class for quantification methods that base their estimations on the aggregation of posterior probabilities
as returned by a probabilistic classifier. Aggregative Probabilistic Quantifiers thus extend Aggregative
Quantifiers by implementing a _posterior_probabilities_ method returning values in [0,1] – the posterior
probabilities.</p>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeProbabilisticQuantifier.posterior_probabilities">
<span class="sig-name descname"><span class="pre">posterior_probabilities</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.method.aggregative.AggregativeProbabilisticQuantifier.posterior_probabilities" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeProbabilisticQuantifier.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.method.aggregative.AggregativeProbabilisticQuantifier.predict_proba" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py property">
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeProbabilisticQuantifier.probabilistic">
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">probabilistic</span></span><a class="headerlink" href="#quapy.method.aggregative.AggregativeProbabilisticQuantifier.probabilistic" title="Permalink to this definition"></a></dt>
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<dd><p>Indicates whether the quantifier is of type probabilistic or not</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>False (to be overridden)</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeProbabilisticQuantifier.quantify">
<span class="sig-name descname"><span class="pre">quantify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.AggregativeProbabilisticQuantifier.quantify" title="Permalink to this definition"></a></dt>
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<dd><p>Generate class prevalence estimates for the sample’s instances by aggregating the label predictions generated
by the classifier.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>instances</strong> – array-like</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(self.n_classes_,)</cite> with class prevalence estimates.</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeProbabilisticQuantifier.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.method.aggregative.AggregativeProbabilisticQuantifier.set_params" title="Permalink to this definition"></a></dt>
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<dd><p>Set the parameters of the quantifier.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>parameters</strong> – dictionary of param-value pairs</p>
</dd>
</dl>
</dd></dl>
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</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeQuantifier">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">AggregativeQuantifier</span></span><a class="headerlink" href="#quapy.method.aggregative.AggregativeQuantifier" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.base.BaseQuantifier</span></code></a></p>
<p>Abstract class for quantification methods that base their estimations on the aggregation of classification
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results. Aggregative Quantifiers thus implement a <a class="reference internal" href="#quapy.method.aggregative.AggregativeQuantifier.classify" title="quapy.method.aggregative.AggregativeQuantifier.classify"><code class="xref py py-meth docutils literal notranslate"><span class="pre">classify()</span></code></a> method and maintain a <a class="reference internal" href="#quapy.method.aggregative.AggregativeQuantifier.learner" title="quapy.method.aggregative.AggregativeQuantifier.learner"><code class="xref py py-attr docutils literal notranslate"><span class="pre">learner</span></code></a> attribute.
Subclasses of this abstract class must implement the method <a class="reference internal" href="#quapy.method.aggregative.AggregativeQuantifier.aggregate" title="quapy.method.aggregative.AggregativeQuantifier.aggregate"><code class="xref py py-meth docutils literal notranslate"><span class="pre">aggregate()</span></code></a> which computes the aggregation
of label predictions. The method <a class="reference internal" href="#quapy.method.aggregative.AggregativeQuantifier.quantify" title="quapy.method.aggregative.AggregativeQuantifier.quantify"><code class="xref py py-meth docutils literal notranslate"><span class="pre">quantify()</span></code></a> comes with a default implementation based on</p>
<blockquote>
<div><p><a class="reference internal" href="#quapy.method.aggregative.AggregativeQuantifier.classify" title="quapy.method.aggregative.AggregativeQuantifier.classify"><code class="xref py py-meth docutils literal notranslate"><span class="pre">classify()</span></code></a> and <a class="reference internal" href="#quapy.method.aggregative.AggregativeQuantifier.aggregate" title="quapy.method.aggregative.AggregativeQuantifier.aggregate"><code class="xref py py-meth docutils literal notranslate"><span class="pre">aggregate()</span></code></a>.</p>
</div></blockquote>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeQuantifier.aggregate">
<em class="property"><span class="pre">abstract</span> </em><span class="sig-name descname"><span class="pre">aggregate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_predictions</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.AggregativeQuantifier.aggregate" title="Permalink to this definition"></a></dt>
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<dd><p>Implements the aggregation of label predictions.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>classif_predictions</strong> – <cite>np.ndarray</cite> of label predictions</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(self.n_classes_,)</cite> with class prevalence estimates.</p>
</dd>
</dl>
</dd></dl>
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<dl class="py property">
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeQuantifier.aggregative">
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">aggregative</span></span><a class="headerlink" href="#quapy.method.aggregative.AggregativeQuantifier.aggregative" title="Permalink to this definition"></a></dt>
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<dd><p>Returns True, indicating the quantifier is of type aggregative.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>True</p>
</dd>
</dl>
</dd></dl>
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<dl class="py property">
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeQuantifier.classes_">
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">classes_</span></span><a class="headerlink" href="#quapy.method.aggregative.AggregativeQuantifier.classes_" title="Permalink to this definition"></a></dt>
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<dd><p>Class labels, in the same order in which class prevalence values are to be computed.
This default implementation actually returns the class labels of the learner.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>array-like</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeQuantifier.classify">
<span class="sig-name descname"><span class="pre">classify</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.method.aggregative.AggregativeQuantifier.classify" title="Permalink to this definition"></a></dt>
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<dd><p>Provides the label predictions for the given instances.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>instances</strong> – array-like</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>np.ndarray of shape <cite>(n_instances,)</cite> with label predictions</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeQuantifier.fit">
<em class="property"><span class="pre">abstract</span> </em><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">data</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_learner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.AggregativeQuantifier.fit" title="Permalink to this definition"></a></dt>
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<dd><p>Trains the aggregative quantifier</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> – a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
<li><p><strong>fit_learner</strong> – whether or not to train the learner (default is True). Set to False if the
learner has been trained outside the quantifier.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>self</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeQuantifier.get_params">
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.AggregativeQuantifier.get_params" title="Permalink to this definition"></a></dt>
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<dd><p>Return the current parameters of the quantifier.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>deep</strong> – for compatibility with sklearn</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a dictionary of param-value pairs</p>
</dd>
</dl>
</dd></dl>
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<dl class="py property">
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeQuantifier.learner">
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">learner</span></span><a class="headerlink" href="#quapy.method.aggregative.AggregativeQuantifier.learner" title="Permalink to this definition"></a></dt>
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<dd><p>Gives access to the classifier</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>the classifier (typically an sklearn’s Estimator)</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeQuantifier.quantify">
<span class="sig-name descname"><span class="pre">quantify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.AggregativeQuantifier.quantify" title="Permalink to this definition"></a></dt>
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<dd><p>Generate class prevalence estimates for the sample’s instances by aggregating the label predictions generated
by the classifier.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>instances</strong> – array-like</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(self.n_classes_,)</cite> with class prevalence estimates.</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeQuantifier.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.method.aggregative.AggregativeQuantifier.set_params" title="Permalink to this definition"></a></dt>
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<dd><p>Set the parameters of the quantifier.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>parameters</strong> – dictionary of param-value pairs</p>
</dd>
</dl>
</dd></dl>
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</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.aggregative.CC">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">CC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">learner</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">sklearn.base.BaseEstimator</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.CC" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeQuantifier" title="quapy.method.aggregative.AggregativeQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.AggregativeQuantifier</span></code></a></p>
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<p>The most basic Quantification method. One that simply classifies all instances and counts how many have been
attributed to each of the classes in order to compute class prevalence estimates.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>learner</strong> – a sklearn’s Estimator that generates a classifier</p>
</dd>
</dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.CC.aggregate">
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<span class="sig-name descname"><span class="pre">aggregate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_predictions</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.CC.aggregate" title="Permalink to this definition"></a></dt>
<dd><p>Computes class prevalence estimates by counting the prevalence of each of the predicted labels.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>classif_predictions</strong> – array-like with label predictions</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(self.n_classes_,)</cite> with class prevalence estimates.</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.CC.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">data</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_learner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.CC.fit" title="Permalink to this definition"></a></dt>
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<dd><p>Trains the Classify &amp; Count method unless <cite>fit_learner</cite> is False, in which case, the classifier is assumed to
be already fit and there is nothing else to do.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> – a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
<li><p><strong>fit_learner</strong> – if False, the classifier is assumed to be fit</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>self</p>
</dd>
</dl>
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</dd></dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="quapy.method.aggregative.ClassifyAndCount">
<span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">ClassifyAndCount</span></span><a class="headerlink" href="#quapy.method.aggregative.ClassifyAndCount" title="Permalink to this definition"></a></dt>
<dd><p>alias of <a class="reference internal" href="#quapy.method.aggregative.CC" title="quapy.method.aggregative.CC"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.CC</span></code></a></p>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.aggregative.ELM">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">ELM</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">svmperf_base</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">loss</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'01'</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.method.aggregative.ELM" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeQuantifier" title="quapy.method.aggregative.AggregativeQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.AggregativeQuantifier</span></code></a>, <a class="reference internal" href="#quapy.method.base.BinaryQuantifier" title="quapy.method.base.BinaryQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.base.BinaryQuantifier</span></code></a></p>
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<p>Class of Explicit Loss Minimization (ELM) quantifiers.
Quantifiers based on ELM represent a family of methods based on structured output learning;
these quantifiers rely on classifiers that have been optimized using a quantification-oriented loss
measure. This implementation relies on
<a class="reference external" href="https://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html">Joachims’ SVM perf</a> structured output
learning algorithm, which has to be installed and patched for the purpose (see this
<a class="reference external" href="https://github.com/HLT-ISTI/QuaPy/blob/master/prepare_svmperf.sh">script</a>).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>svmperf_base</strong> – path to the folder containing the binary files of <cite>SVM perf</cite></p></li>
<li><p><strong>loss</strong> – the loss to optimize (see <a class="reference internal" href="quapy.classification.html#quapy.classification.svmperf.SVMperf.valid_losses" title="quapy.classification.svmperf.SVMperf.valid_losses"><code class="xref py py-attr docutils literal notranslate"><span class="pre">quapy.classification.svmperf.SVMperf.valid_losses</span></code></a>)</p></li>
<li><p><strong>kwargs</strong> – rest of SVM perf’s parameters</p></li>
</ul>
</dd>
</dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.ELM.aggregate">
<span class="sig-name descname"><span class="pre">aggregate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_predictions</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">numpy.ndarray</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.ELM.aggregate" title="Permalink to this definition"></a></dt>
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<dd><p>Implements the aggregation of label predictions.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>classif_predictions</strong> – <cite>np.ndarray</cite> of label predictions</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(self.n_classes_,)</cite> with class prevalence estimates.</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.ELM.classify">
<span class="sig-name descname"><span class="pre">classify</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.method.aggregative.ELM.classify" title="Permalink to this definition"></a></dt>
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<dd><p>Provides the label predictions for the given instances.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>instances</strong> – array-like</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>np.ndarray of shape <cite>(n_instances,)</cite> with label predictions</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.ELM.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">data</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_learner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.ELM.fit" title="Permalink to this definition"></a></dt>
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<dd><p>Trains the aggregative quantifier</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> – a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
<li><p><strong>fit_learner</strong> – whether or not to train the learner (default is True). Set to False if the
learner has been trained outside the quantifier.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>self</p>
</dd>
</dl>
</dd></dl>
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</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.aggregative.EMQ">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">EMQ</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">learner</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">sklearn.base.BaseEstimator</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.EMQ" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeProbabilisticQuantifier" title="quapy.method.aggregative.AggregativeProbabilisticQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.AggregativeProbabilisticQuantifier</span></code></a></p>
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<p><a class="reference external" href="https://ieeexplore.ieee.org/abstract/document/6789744">Expectation Maximization for Quantification</a> (EMQ),
aka <cite>Saerens-Latinne-Decaestecker</cite> (SLD) algorithm.
EMQ consists of using the well-known <cite>Expectation Maximization algorithm</cite> to iteratively update the posterior
probabilities generated by a probabilistic classifier and the class prevalence estimates obtained via
maximum-likelihood estimation, in a mutually recursive way, until convergence.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>learner</strong> – a sklearn’s Estimator that generates a classifier</p>
</dd>
</dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.EMQ.EM">
<em class="property"><span class="pre">classmethod</span> </em><span class="sig-name descname"><span class="pre">EM</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tr_prev</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">posterior_probabilities</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.EMQ.EM" title="Permalink to this definition"></a></dt>
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<dd><p>Computes the <cite>Expectation Maximization</cite> routine.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tr_prev</strong> – array-like, the training prevalence</p></li>
<li><p><strong>posterior_probabilities</strong> – <cite>np.ndarray</cite> of shape <cite>(n_instances, n_classes,)</cite> with the
posterior probabilities</p></li>
<li><p><strong>epsilon</strong> – float, the threshold different between two consecutive iterations
to reach before stopping the loop</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a tuple with the estimated prevalence values (shape <cite>(n_classes,)</cite>) and
the corrected posterior probabilities (shape <cite>(n_instances, n_classes,)</cite>)</p>
</dd>
</dl>
</dd></dl>
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<dl class="py attribute">
<dt class="sig sig-object py" id="quapy.method.aggregative.EMQ.EPSILON">
<span class="sig-name descname"><span class="pre">EPSILON</span></span><em class="property"> <span class="pre">=</span> <span class="pre">0.0001</span></em><a class="headerlink" href="#quapy.method.aggregative.EMQ.EPSILON" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="quapy.method.aggregative.EMQ.MAX_ITER">
<span class="sig-name descname"><span class="pre">MAX_ITER</span></span><em class="property"> <span class="pre">=</span> <span class="pre">1000</span></em><a class="headerlink" href="#quapy.method.aggregative.EMQ.MAX_ITER" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.EMQ.aggregate">
<span class="sig-name descname"><span class="pre">aggregate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_posteriors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.EMQ.aggregate" title="Permalink to this definition"></a></dt>
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<dd><p>Implements the aggregation of label predictions.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>classif_predictions</strong> – <cite>np.ndarray</cite> of label predictions</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(self.n_classes_,)</cite> with class prevalence estimates.</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.EMQ.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">data</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_learner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.EMQ.fit" title="Permalink to this definition"></a></dt>
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<dd><p>Trains the aggregative quantifier</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> – a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
<li><p><strong>fit_learner</strong> – whether or not to train the learner (default is True). Set to False if the
learner has been trained outside the quantifier.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>self</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.EMQ.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>, <em class="sig-param"><span class="n"><span class="pre">epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.EMQ.predict_proba" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="quapy.method.aggregative.ExpectationMaximizationQuantifier">
<span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">ExpectationMaximizationQuantifier</span></span><a class="headerlink" href="#quapy.method.aggregative.ExpectationMaximizationQuantifier" title="Permalink to this definition"></a></dt>
<dd><p>alias of <a class="reference internal" href="#quapy.method.aggregative.EMQ" title="quapy.method.aggregative.EMQ"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.EMQ</span></code></a></p>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="quapy.method.aggregative.ExplicitLossMinimisation">
<span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">ExplicitLossMinimisation</span></span><a class="headerlink" href="#quapy.method.aggregative.ExplicitLossMinimisation" title="Permalink to this definition"></a></dt>
<dd><p>alias of <a class="reference internal" href="#quapy.method.aggregative.ELM" title="quapy.method.aggregative.ELM"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.ELM</span></code></a></p>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.aggregative.HDy">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">HDy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">learner</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">sklearn.base.BaseEstimator</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">0.4</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.HDy" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeProbabilisticQuantifier" title="quapy.method.aggregative.AggregativeProbabilisticQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.AggregativeProbabilisticQuantifier</span></code></a>, <a class="reference internal" href="#quapy.method.base.BinaryQuantifier" title="quapy.method.base.BinaryQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.base.BinaryQuantifier</span></code></a></p>
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<p><a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S0020025512004069">Hellinger Distance y</a> (HDy).
HDy is a probabilistic method for training binary quantifiers, that models quantification as the problem of
minimizing the divergence (in terms of the Hellinger Distance) between two cumulative distributions of posterior
probabilities returned by the classifier. One of the distributions is generated from the unlabelled examples and
the other is generated from a validation set. This latter distribution is defined as a mixture of the
class-conditional distributions of the posterior probabilities returned for the positive and negative validation
examples, respectively. The parameters of the mixture thus represent the estimates of the class prevalence values.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>learner</strong> – a sklearn’s Estimator that generates a binary classifier</p></li>
<li><p><strong>val_split</strong> – a float in range (0,1) indicating the proportion of data to be used as a stratified held-out
validation distribution, or a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> (the split itself).</p></li>
</ul>
</dd>
</dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.HDy.aggregate">
<span class="sig-name descname"><span class="pre">aggregate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_posteriors</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.HDy.aggregate" title="Permalink to this definition"></a></dt>
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<dd><p>Implements the aggregation of label predictions.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>classif_predictions</strong> – <cite>np.ndarray</cite> of label predictions</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(self.n_classes_,)</cite> with class prevalence estimates.</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.HDy.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">data</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_learner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.HDy.fit" title="Permalink to this definition"></a></dt>
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<dd><p>Trains a HDy quantifier.</p>
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<dl class="field-list simple">
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<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> – the training set</p></li>
<li><p><strong>fit_learner</strong> – set to False to bypass the training (the learner is assumed to be already fit)</p></li>
<li><p><strong>val_split</strong> – either a float in (0,1) indicating the proportion of training instances to use for
validation (e.g., 0.3 for using 30% of the training set as validation data), or a
<a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> indicating the validation set itself</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>self</p>
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</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="quapy.method.aggregative.HellingerDistanceY">
<span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">HellingerDistanceY</span></span><a class="headerlink" href="#quapy.method.aggregative.HellingerDistanceY" title="Permalink to this definition"></a></dt>
<dd><p>alias of <a class="reference internal" href="#quapy.method.aggregative.HDy" title="quapy.method.aggregative.HDy"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.HDy</span></code></a></p>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.aggregative.MAX">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">MAX</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">learner</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">sklearn.base.BaseEstimator</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">0.4</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.MAX" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.ThresholdOptimization" title="quapy.method.aggregative.ThresholdOptimization"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.ThresholdOptimization</span></code></a></p>
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<p>Threshold Optimization variant for <a class="reference internal" href="#quapy.method.aggregative.ACC" title="quapy.method.aggregative.ACC"><code class="xref py py-class docutils literal notranslate"><span class="pre">ACC</span></code></a> as proposed by
<a class="reference external" href="https://dl.acm.org/doi/abs/10.1145/1150402.1150423">Forman 2006</a> and
<a class="reference external" href="https://link.springer.com/article/10.1007/s10618-008-0097-y">Forman 2008</a> that looks
for the threshold that maximizes <cite>tpr-fpr</cite>.
The goal is to bring improved stability to the denominator of the adjustment.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>learner</strong> – a sklearn’s Estimator that generates a classifier</p></li>
<li><p><strong>val_split</strong> – indicates the proportion of data to be used as a stratified held-out validation set in which the
misclassification rates are to be estimated.
This parameter can be indicated as a real value (between 0 and 1, default 0.4), representing a proportion of
validation data, or as an integer, indicating that the misclassification rates should be estimated via
<cite>k</cite>-fold cross validation (this integer stands for the number of folds <cite>k</cite>), or as a
<a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> (the split itself).</p></li>
</ul>
</dd>
</dl>
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</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.aggregative.MS">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">MS</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">learner</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">sklearn.base.BaseEstimator</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">0.4</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.MS" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.ThresholdOptimization" title="quapy.method.aggregative.ThresholdOptimization"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.ThresholdOptimization</span></code></a></p>
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<p>Median Sweep. Threshold Optimization variant for <a class="reference internal" href="#quapy.method.aggregative.ACC" title="quapy.method.aggregative.ACC"><code class="xref py py-class docutils literal notranslate"><span class="pre">ACC</span></code></a> as proposed by
<a class="reference external" href="https://dl.acm.org/doi/abs/10.1145/1150402.1150423">Forman 2006</a> and
<a class="reference external" href="https://link.springer.com/article/10.1007/s10618-008-0097-y">Forman 2008</a> that generates
class prevalence estimates for all decision thresholds and returns the median of them all.
The goal is to bring improved stability to the denominator of the adjustment.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>learner</strong> – a sklearn’s Estimator that generates a classifier</p></li>
<li><p><strong>val_split</strong> – indicates the proportion of data to be used as a stratified held-out validation set in which the
misclassification rates are to be estimated.
This parameter can be indicated as a real value (between 0 and 1, default 0.4), representing a proportion of
validation data, or as an integer, indicating that the misclassification rates should be estimated via
<cite>k</cite>-fold cross validation (this integer stands for the number of folds <cite>k</cite>), or as a
<a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> (the split itself).</p></li>
</ul>
</dd>
</dl>
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</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.aggregative.MS2">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">MS2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">learner</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">sklearn.base.BaseEstimator</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">0.4</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.MS2" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.MS" title="quapy.method.aggregative.MS"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.MS</span></code></a></p>
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<p>Median Sweep 2. Threshold Optimization variant for <a class="reference internal" href="#quapy.method.aggregative.ACC" title="quapy.method.aggregative.ACC"><code class="xref py py-class docutils literal notranslate"><span class="pre">ACC</span></code></a> as proposed by
<a class="reference external" href="https://dl.acm.org/doi/abs/10.1145/1150402.1150423">Forman 2006</a> and
<a class="reference external" href="https://link.springer.com/article/10.1007/s10618-008-0097-y">Forman 2008</a> that generates
class prevalence estimates for all decision thresholds and returns the median of for cases in
which <cite>tpr-fpr&gt;0.25</cite>
The goal is to bring improved stability to the denominator of the adjustment.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>learner</strong> – a sklearn’s Estimator that generates a classifier</p></li>
<li><p><strong>val_split</strong> – indicates the proportion of data to be used as a stratified held-out validation set in which the
misclassification rates are to be estimated.
This parameter can be indicated as a real value (between 0 and 1, default 0.4), representing a proportion of
validation data, or as an integer, indicating that the misclassification rates should be estimated via
<cite>k</cite>-fold cross validation (this integer stands for the number of folds <cite>k</cite>), or as a
<a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> (the split itself).</p></li>
</ul>
</dd>
</dl>
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</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="quapy.method.aggregative.MedianSweep">
<span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">MedianSweep</span></span><a class="headerlink" href="#quapy.method.aggregative.MedianSweep" title="Permalink to this definition"></a></dt>
<dd><p>alias of <a class="reference internal" href="#quapy.method.aggregative.MS" title="quapy.method.aggregative.MS"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.MS</span></code></a></p>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="quapy.method.aggregative.MedianSweep2">
<span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">MedianSweep2</span></span><a class="headerlink" href="#quapy.method.aggregative.MedianSweep2" title="Permalink to this definition"></a></dt>
<dd><p>alias of <a class="reference internal" href="#quapy.method.aggregative.MS2" title="quapy.method.aggregative.MS2"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.MS2</span></code></a></p>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.aggregative.OneVsAll">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">OneVsAll</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">binary_quantifier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.OneVsAll" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeQuantifier" title="quapy.method.aggregative.AggregativeQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.AggregativeQuantifier</span></code></a></p>
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<p>Allows any binary quantifier to perform quantification on single-label datasets.
The method maintains one binary quantifier for each class, and then l1-normalizes the outputs so that the
class prevelences sum up to 1.
This variant was used, along with the <a class="reference internal" href="#quapy.method.aggregative.EMQ" title="quapy.method.aggregative.EMQ"><code class="xref py py-class docutils literal notranslate"><span class="pre">EMQ</span></code></a> quantifier, in
<a class="reference external" href="https://link.springer.com/content/pdf/10.1007/s13278-016-0327-z.pdf">Gao and Sebastiani, 2016</a>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>learner</strong> – a sklearn’s Estimator that generates a binary classifier</p></li>
<li><p><strong>n_jobs</strong> – number of parallel workers</p></li>
</ul>
</dd>
</dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.OneVsAll.aggregate">
<span class="sig-name descname"><span class="pre">aggregate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_predictions_bin</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.OneVsAll.aggregate" title="Permalink to this definition"></a></dt>
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<dd><p>Implements the aggregation of label predictions.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>classif_predictions</strong> – <cite>np.ndarray</cite> of label predictions</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(self.n_classes_,)</cite> with class prevalence estimates.</p>
</dd>
</dl>
</dd></dl>
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<dl class="py property">
<dt class="sig sig-object py" id="quapy.method.aggregative.OneVsAll.binary">
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">binary</span></span><a class="headerlink" href="#quapy.method.aggregative.OneVsAll.binary" title="Permalink to this definition"></a></dt>
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<dd><p>Informs that the classifier is not binary</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>False</p>
</dd>
</dl>
</dd></dl>
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<dl class="py property">
<dt class="sig sig-object py" id="quapy.method.aggregative.OneVsAll.classes_">
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">classes_</span></span><a class="headerlink" href="#quapy.method.aggregative.OneVsAll.classes_" title="Permalink to this definition"></a></dt>
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<dd><p>Class labels, in the same order in which class prevalence values are to be computed.
This default implementation actually returns the class labels of the learner.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>array-like</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.OneVsAll.classify">
<span class="sig-name descname"><span class="pre">classify</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.method.aggregative.OneVsAll.classify" title="Permalink to this definition"></a></dt>
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<dd><p>Returns a matrix of shape <cite>(n,m,)</cite> with <cite>n</cite> the number of instances and <cite>m</cite> the number of classes. The entry
<cite>(i,j)</cite> is a binary value indicating whether instance <cite>i `belongs to class `j</cite>. The binary classifications are
independent of each other, meaning that an instance can end up be attributed to 0, 1, or more classes.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>instances</strong> – array-like</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><cite>np.ndarray</cite></p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.OneVsAll.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">data</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_learner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.OneVsAll.fit" title="Permalink to this definition"></a></dt>
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<dd><p>Trains the aggregative quantifier</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> – a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
<li><p><strong>fit_learner</strong> – whether or not to train the learner (default is True). Set to False if the
learner has been trained outside the quantifier.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>self</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.OneVsAll.get_params">
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.OneVsAll.get_params" title="Permalink to this definition"></a></dt>
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<dd><p>Return the current parameters of the quantifier.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>deep</strong> – for compatibility with sklearn</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a dictionary of param-value pairs</p>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.OneVsAll.posterior_probabilities">
<span class="sig-name descname"><span class="pre">posterior_probabilities</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.method.aggregative.OneVsAll.posterior_probabilities" title="Permalink to this definition"></a></dt>
<dd><p>Returns a matrix of shape <cite>(n,m,2)</cite> with <cite>n</cite> the number of instances and <cite>m</cite> the number of classes. The entry
<cite>(i,j,1)</cite> (resp. <cite>(i,j,0)</cite>) is a value in [0,1] indicating the posterior probability that instance <cite>i</cite> belongs
(resp. does not belong) to class <cite>j</cite>.
The posterior probabilities are independent of each other, meaning that, in general, they do not sum
up to one.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>instances</strong> – array-like</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><cite>np.ndarray</cite></p>
</dd>
</dl>
</dd></dl>
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<dl class="py property">
<dt class="sig sig-object py" id="quapy.method.aggregative.OneVsAll.probabilistic">
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">probabilistic</span></span><a class="headerlink" href="#quapy.method.aggregative.OneVsAll.probabilistic" title="Permalink to this definition"></a></dt>
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<dd><p>Indicates if the classifier is probabilistic or not (depending on the nature of the base classifier).</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>boolean</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.OneVsAll.quantify">
<span class="sig-name descname"><span class="pre">quantify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.OneVsAll.quantify" title="Permalink to this definition"></a></dt>
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<dd><p>Generate class prevalence estimates for the sample’s instances by aggregating the label predictions generated
by the classifier.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>instances</strong> – array-like</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(self.n_classes_,)</cite> with class prevalence estimates.</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.OneVsAll.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.method.aggregative.OneVsAll.set_params" title="Permalink to this definition"></a></dt>
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<dd><p>Set the parameters of the quantifier.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>parameters</strong> – dictionary of param-value pairs</p>
</dd>
</dl>
</dd></dl>
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</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.aggregative.PACC">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">PACC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">learner</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">sklearn.base.BaseEstimator</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">0.4</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.PACC" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeProbabilisticQuantifier" title="quapy.method.aggregative.AggregativeProbabilisticQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.AggregativeProbabilisticQuantifier</span></code></a></p>
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<p><a class="reference external" href="https://ieeexplore.ieee.org/abstract/document/5694031">Probabilistic Adjusted Classify &amp; Count</a>,
the probabilistic variant of ACC that relies on the posterior probabilities returned by a probabilistic classifier.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>learner</strong> – a sklearn’s Estimator that generates a classifier</p></li>
<li><p><strong>val_split</strong> – indicates the proportion of data to be used as a stratified held-out validation set in which the
misclassification rates are to be estimated.
This parameter can be indicated as a real value (between 0 and 1, default 0.4), representing a proportion of
validation data, or as an integer, indicating that the misclassification rates should be estimated via
<cite>k</cite>-fold cross validation (this integer stands for the number of folds <cite>k</cite>), or as a
<a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> (the split itself).</p></li>
</ul>
</dd>
</dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.PACC.aggregate">
<span class="sig-name descname"><span class="pre">aggregate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_posteriors</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.PACC.aggregate" title="Permalink to this definition"></a></dt>
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<dd><p>Implements the aggregation of label predictions.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>classif_predictions</strong> – <cite>np.ndarray</cite> of label predictions</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(self.n_classes_,)</cite> with class prevalence estimates.</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.PACC.classify">
<span class="sig-name descname"><span class="pre">classify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.PACC.classify" title="Permalink to this definition"></a></dt>
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<dd><p>Provides the label predictions for the given instances.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>instances</strong> – array-like</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>np.ndarray of shape <cite>(n_instances,)</cite> with label predictions</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.PACC.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">data</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_learner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span> </span><span class="pre">int</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.PACC.fit" title="Permalink to this definition"></a></dt>
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<dd><p>Trains a PACC quantifier.</p>
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<dl class="field-list simple">
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<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> – the training set</p></li>
<li><p><strong>fit_learner</strong> – set to False to bypass the training (the learner is assumed to be already fit)</p></li>
<li><p><strong>val_split</strong> – either a float in (0,1) indicating the proportion of training instances to use for
validation (e.g., 0.3 for using 30% of the training set as validation data), or a LabelledCollection
indicating the validation set itself, or an int indicating the number k of folds to be used in kFCV
to estimate the parameters</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>self</p>
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</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.aggregative.PCC">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">PCC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">learner</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">sklearn.base.BaseEstimator</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.PCC" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeProbabilisticQuantifier" title="quapy.method.aggregative.AggregativeProbabilisticQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.AggregativeProbabilisticQuantifier</span></code></a></p>
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<p><a class="reference external" href="https://ieeexplore.ieee.org/abstract/document/5694031">Probabilistic Classify &amp; Count</a>,
the probabilistic variant of CC that relies on the posterior probabilities returned by a probabilistic classifier.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>learner</strong> – a sklearn’s Estimator that generates a classifier</p>
</dd>
</dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.PCC.aggregate">
<span class="sig-name descname"><span class="pre">aggregate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_posteriors</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.PCC.aggregate" title="Permalink to this definition"></a></dt>
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<dd><p>Implements the aggregation of label predictions.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>classif_predictions</strong> – <cite>np.ndarray</cite> of label predictions</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(self.n_classes_,)</cite> with class prevalence estimates.</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.PCC.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">data</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_learner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.PCC.fit" title="Permalink to this definition"></a></dt>
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<dd><p>Trains the aggregative quantifier</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> – a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
<li><p><strong>fit_learner</strong> – whether or not to train the learner (default is True). Set to False if the
learner has been trained outside the quantifier.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>self</p>
</dd>
</dl>
</dd></dl>
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</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="quapy.method.aggregative.ProbabilisticAdjustedClassifyAndCount">
<span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">ProbabilisticAdjustedClassifyAndCount</span></span><a class="headerlink" href="#quapy.method.aggregative.ProbabilisticAdjustedClassifyAndCount" title="Permalink to this definition"></a></dt>
<dd><p>alias of <a class="reference internal" href="#quapy.method.aggregative.PACC" title="quapy.method.aggregative.PACC"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.PACC</span></code></a></p>
</dd></dl>
<dl class="py attribute">
<dt class="sig sig-object py" id="quapy.method.aggregative.ProbabilisticClassifyAndCount">
<span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">ProbabilisticClassifyAndCount</span></span><a class="headerlink" href="#quapy.method.aggregative.ProbabilisticClassifyAndCount" title="Permalink to this definition"></a></dt>
<dd><p>alias of <a class="reference internal" href="#quapy.method.aggregative.PCC" title="quapy.method.aggregative.PCC"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.PCC</span></code></a></p>
</dd></dl>
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<dl class="py attribute">
<dt class="sig sig-object py" id="quapy.method.aggregative.SLD">
<span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">SLD</span></span><a class="headerlink" href="#quapy.method.aggregative.SLD" title="Permalink to this definition"></a></dt>
<dd><p>alias of <a class="reference internal" href="#quapy.method.aggregative.EMQ" title="quapy.method.aggregative.EMQ"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.EMQ</span></code></a></p>
</dd></dl>
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<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.aggregative.SVMAE">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">SVMAE</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">svmperf_base</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="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.method.aggregative.SVMAE" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.ELM" title="quapy.method.aggregative.ELM"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.ELM</span></code></a></p>
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<p>SVM(AE), which attempts to minimize Absolute Error as first used by
<a class="reference external" href="https://arxiv.org/abs/2011.02552">Moreo and Sebastiani, 2021</a>.
Equivalent to:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ELM</span><span class="p">(</span><span class="n">svmperf_base</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;mae&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>svmperf_base</strong> – path to the folder containing the binary files of <cite>SVM perf</cite></p></li>
<li><p><strong>kwargs</strong> – rest of SVM perf’s parameters</p></li>
</ul>
</dd>
</dl>
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</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.aggregative.SVMKLD">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">SVMKLD</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">svmperf_base</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="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.method.aggregative.SVMKLD" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.ELM" title="quapy.method.aggregative.ELM"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.ELM</span></code></a></p>
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<p>SVM(KLD), which attempts to minimize the Kullback-Leibler Divergence as proposed by
<a class="reference external" href="https://dl.acm.org/doi/abs/10.1145/2700406">Esuli et al. 2015</a>.
Equivalent to:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ELM</span><span class="p">(</span><span class="n">svmperf_base</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;kld&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>svmperf_base</strong> – path to the folder containing the binary files of <cite>SVM perf</cite></p></li>
<li><p><strong>kwargs</strong> – rest of SVM perf’s parameters</p></li>
</ul>
</dd>
</dl>
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</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.aggregative.SVMNKLD">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">SVMNKLD</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">svmperf_base</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="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.method.aggregative.SVMNKLD" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.ELM" title="quapy.method.aggregative.ELM"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.ELM</span></code></a></p>
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<p>SVM(NKLD), which attempts to minimize a version of the the Kullback-Leibler Divergence normalized
via the logistic function, as proposed by
<a class="reference external" href="https://dl.acm.org/doi/abs/10.1145/2700406">Esuli et al. 2015</a>.
Equivalent to:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ELM</span><span class="p">(</span><span class="n">svmperf_base</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;nkld&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>svmperf_base</strong> – path to the folder containing the binary files of <cite>SVM perf</cite></p></li>
<li><p><strong>kwargs</strong> – rest of SVM perf’s parameters</p></li>
</ul>
</dd>
</dl>
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</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.aggregative.SVMQ">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">SVMQ</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">svmperf_base</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="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.method.aggregative.SVMQ" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.ELM" title="quapy.method.aggregative.ELM"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.ELM</span></code></a></p>
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<p>SVM(Q), which attempts to minimize the <cite>Q</cite> loss combining a classification-oriented loss and a
quantification-oriented loss, as proposed by
<a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S003132031400291X">Barranquero et al. 2015</a>.
Equivalent to:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ELM</span><span class="p">(</span><span class="n">svmperf_base</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;q&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>svmperf_base</strong> – path to the folder containing the binary files of <cite>SVM perf</cite></p></li>
<li><p><strong>kwargs</strong> – rest of SVM perf’s parameters</p></li>
</ul>
</dd>
</dl>
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</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.aggregative.SVMRAE">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">SVMRAE</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">svmperf_base</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="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.method.aggregative.SVMRAE" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.ELM" title="quapy.method.aggregative.ELM"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.ELM</span></code></a></p>
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<p>SVM(RAE), which attempts to minimize Relative Absolute Error as first used by
<a class="reference external" href="https://arxiv.org/abs/2011.02552">Moreo and Sebastiani, 2021</a>.
Equivalent to:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ELM</span><span class="p">(</span><span class="n">svmperf_base</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;mrae&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>svmperf_base</strong> – path to the folder containing the binary files of <cite>SVM perf</cite></p></li>
<li><p><strong>kwargs</strong> – rest of SVM perf’s parameters</p></li>
</ul>
</dd>
</dl>
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</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.aggregative.T50">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">T50</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">learner</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">sklearn.base.BaseEstimator</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">0.4</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.T50" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.ThresholdOptimization" title="quapy.method.aggregative.ThresholdOptimization"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.ThresholdOptimization</span></code></a></p>
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<p>Threshold Optimization variant for <a class="reference internal" href="#quapy.method.aggregative.ACC" title="quapy.method.aggregative.ACC"><code class="xref py py-class docutils literal notranslate"><span class="pre">ACC</span></code></a> as proposed by
<a class="reference external" href="https://dl.acm.org/doi/abs/10.1145/1150402.1150423">Forman 2006</a> and
<a class="reference external" href="https://link.springer.com/article/10.1007/s10618-008-0097-y">Forman 2008</a> that looks
for the threshold that makes <cite>tpr</cite> cosest to 0.5.
The goal is to bring improved stability to the denominator of the adjustment.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>learner</strong> – a sklearn’s Estimator that generates a classifier</p></li>
<li><p><strong>val_split</strong> – indicates the proportion of data to be used as a stratified held-out validation set in which the
misclassification rates are to be estimated.
This parameter can be indicated as a real value (between 0 and 1, default 0.4), representing a proportion of
validation data, or as an integer, indicating that the misclassification rates should be estimated via
<cite>k</cite>-fold cross validation (this integer stands for the number of folds <cite>k</cite>), or as a
<a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> (the split itself).</p></li>
</ul>
</dd>
</dl>
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</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.aggregative.ThresholdOptimization">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">ThresholdOptimization</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">learner</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">sklearn.base.BaseEstimator</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">0.4</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.ThresholdOptimization" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeQuantifier" title="quapy.method.aggregative.AggregativeQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.AggregativeQuantifier</span></code></a>, <a class="reference internal" href="#quapy.method.base.BinaryQuantifier" title="quapy.method.base.BinaryQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.base.BinaryQuantifier</span></code></a></p>
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<p>Abstract class of Threshold Optimization variants for <a class="reference internal" href="#quapy.method.aggregative.ACC" title="quapy.method.aggregative.ACC"><code class="xref py py-class docutils literal notranslate"><span class="pre">ACC</span></code></a> as proposed by
<a class="reference external" href="https://dl.acm.org/doi/abs/10.1145/1150402.1150423">Forman 2006</a> and
<a class="reference external" href="https://link.springer.com/article/10.1007/s10618-008-0097-y">Forman 2008</a>.
The goal is to bring improved stability to the denominator of the adjustment.
The different variants are based on different heuristics for choosing a decision threshold
that would allow for more true positives and many more false positives, on the grounds this
would deliver larger denominators.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>learner</strong> – a sklearn’s Estimator that generates a classifier</p></li>
<li><p><strong>val_split</strong> – indicates the proportion of data to be used as a stratified held-out validation set in which the
misclassification rates are to be estimated.
This parameter can be indicated as a real value (between 0 and 1, default 0.4), representing a proportion of
validation data, or as an integer, indicating that the misclassification rates should be estimated via
<cite>k</cite>-fold cross validation (this integer stands for the number of folds <cite>k</cite>), or as a
<a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> (the split itself).</p></li>
</ul>
</dd>
</dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.ThresholdOptimization.aggregate">
<span class="sig-name descname"><span class="pre">aggregate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classif_predictions</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.ThresholdOptimization.aggregate" title="Permalink to this definition"></a></dt>
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<dd><p>Implements the aggregation of label predictions.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>classif_predictions</strong> – <cite>np.ndarray</cite> of label predictions</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(self.n_classes_,)</cite> with class prevalence estimates.</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.ThresholdOptimization.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">data</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_learner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span> </span><span class="pre">int</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.ThresholdOptimization.fit" title="Permalink to this definition"></a></dt>
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<dd><p>Trains the aggregative quantifier</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> – a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
<li><p><strong>fit_learner</strong> – whether or not to train the learner (default is True). Set to False if the
learner has been trained outside the quantifier.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>self</p>
</dd>
</dl>
</dd></dl>
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</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.aggregative.X">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">X</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">learner</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">sklearn.base.BaseEstimator</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">0.4</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.X" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.ThresholdOptimization" title="quapy.method.aggregative.ThresholdOptimization"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.ThresholdOptimization</span></code></a></p>
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<p>Threshold Optimization variant for <a class="reference internal" href="#quapy.method.aggregative.ACC" title="quapy.method.aggregative.ACC"><code class="xref py py-class docutils literal notranslate"><span class="pre">ACC</span></code></a> as proposed by
<a class="reference external" href="https://dl.acm.org/doi/abs/10.1145/1150402.1150423">Forman 2006</a> and
<a class="reference external" href="https://link.springer.com/article/10.1007/s10618-008-0097-y">Forman 2008</a> that looks
for the threshold that yields <cite>tpr=1-fpr</cite>.
The goal is to bring improved stability to the denominator of the adjustment.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>learner</strong> – a sklearn’s Estimator that generates a classifier</p></li>
<li><p><strong>val_split</strong> – indicates the proportion of data to be used as a stratified held-out validation set in which the
misclassification rates are to be estimated.
This parameter can be indicated as a real value (between 0 and 1, default 0.4), representing a proportion of
validation data, or as an integer, indicating that the misclassification rates should be estimated via
<cite>k</cite>-fold cross validation (this integer stands for the number of folds <cite>k</cite>), or as a
<a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> (the split itself).</p></li>
</ul>
</dd>
</dl>
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</dd></dl>
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</section>
<section id="module-quapy.method.base">
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<span id="quapy-method-base-module"></span><h2>quapy.method.base module<a class="headerlink" href="#module-quapy.method.base" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.base.BaseQuantifier">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.method.base.</span></span><span class="sig-name descname"><span class="pre">BaseQuantifier</span></span><a class="headerlink" href="#quapy.method.base.BaseQuantifier" 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>
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<p>Abstract Quantifier. A quantifier is defined as an object of a class that implements the method <a class="reference internal" href="#quapy.method.base.BaseQuantifier.fit" title="quapy.method.base.BaseQuantifier.fit"><code class="xref py py-meth docutils literal notranslate"><span class="pre">fit()</span></code></a> on
<a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a>, the method <a class="reference internal" href="#quapy.method.base.BaseQuantifier.quantify" title="quapy.method.base.BaseQuantifier.quantify"><code class="xref py py-meth docutils literal notranslate"><span class="pre">quantify()</span></code></a>, and the <a class="reference internal" href="#quapy.method.base.BaseQuantifier.set_params" title="quapy.method.base.BaseQuantifier.set_params"><code class="xref py py-meth docutils literal notranslate"><span class="pre">set_params()</span></code></a> and
<a class="reference internal" href="#quapy.method.base.BaseQuantifier.get_params" title="quapy.method.base.BaseQuantifier.get_params"><code class="xref py py-meth docutils literal notranslate"><span class="pre">get_params()</span></code></a> for model selection (see <a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ" title="quapy.model_selection.GridSearchQ"><code class="xref py py-meth docutils literal notranslate"><span class="pre">quapy.model_selection.GridSearchQ()</span></code></a>)</p>
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<dl class="py property">
<dt class="sig sig-object py" id="quapy.method.base.BaseQuantifier.aggregative">
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">aggregative</span></span><a class="headerlink" href="#quapy.method.base.BaseQuantifier.aggregative" title="Permalink to this definition"></a></dt>
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<dd><p>Indicates whether the quantifier is of type aggregative or not</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>False (to be overridden)</p>
</dd>
</dl>
</dd></dl>
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<dl class="py property">
<dt class="sig sig-object py" id="quapy.method.base.BaseQuantifier.binary">
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">binary</span></span><a class="headerlink" href="#quapy.method.base.BaseQuantifier.binary" title="Permalink to this definition"></a></dt>
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<dd><p>Indicates whether the quantifier is binary or not.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>False (to be overridden)</p>
</dd>
</dl>
</dd></dl>
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<dl class="py property">
<dt class="sig sig-object py" id="quapy.method.base.BaseQuantifier.classes_">
<em class="property"><span class="pre">abstract</span> <span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">classes_</span></span><a class="headerlink" href="#quapy.method.base.BaseQuantifier.classes_" title="Permalink to this definition"></a></dt>
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<dd><p>Class labels, in the same order in which class prevalence values are to be computed.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>array-like</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.base.BaseQuantifier.fit">
<em class="property"><span class="pre">abstract</span> </em><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">data</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.base.BaseQuantifier.fit" title="Permalink to this definition"></a></dt>
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<dd><p>Trains a quantifier.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>data</strong> – a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>self</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.base.BaseQuantifier.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><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.base.BaseQuantifier.get_params" title="Permalink to this definition"></a></dt>
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<dd><p>Return the current parameters of the quantifier.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>deep</strong> – for compatibility with sklearn</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a dictionary of param-value pairs</p>
</dd>
</dl>
</dd></dl>
<dl class="py property">
<dt class="sig sig-object py" id="quapy.method.base.BaseQuantifier.n_classes">
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">n_classes</span></span><a class="headerlink" href="#quapy.method.base.BaseQuantifier.n_classes" title="Permalink to this definition"></a></dt>
<dd><p>Returns the number of classes</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>integer</p>
</dd>
</dl>
</dd></dl>
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<dl class="py property">
<dt class="sig sig-object py" id="quapy.method.base.BaseQuantifier.probabilistic">
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">probabilistic</span></span><a class="headerlink" href="#quapy.method.base.BaseQuantifier.probabilistic" title="Permalink to this definition"></a></dt>
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<dd><p>Indicates whether the quantifier is of type probabilistic or not</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>False (to be overridden)</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.base.BaseQuantifier.quantify">
<em class="property"><span class="pre">abstract</span> </em><span class="sig-name descname"><span class="pre">quantify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.base.BaseQuantifier.quantify" title="Permalink to this definition"></a></dt>
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<dd><p>Generate class prevalence estimates for the sample’s instances</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>instances</strong> – array-like</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(self.n_classes_,)</cite> with class prevalence estimates.</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.base.BaseQuantifier.set_params">
<em class="property"><span class="pre">abstract</span> </em><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.method.base.BaseQuantifier.set_params" title="Permalink to this definition"></a></dt>
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<dd><p>Set the parameters of the quantifier.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>parameters</strong> – dictionary of param-value pairs</p>
</dd>
</dl>
</dd></dl>
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</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.base.BinaryQuantifier">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.method.base.</span></span><span class="sig-name descname"><span class="pre">BinaryQuantifier</span></span><a class="headerlink" href="#quapy.method.base.BinaryQuantifier" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.base.BaseQuantifier</span></code></a></p>
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<p>Abstract class of binary quantifiers, i.e., quantifiers estimating class prevalence values for only two classes
(typically, to be interpreted as one class and its complement).</p>
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<dl class="py property">
<dt class="sig sig-object py" id="quapy.method.base.BinaryQuantifier.binary">
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">binary</span></span><a class="headerlink" href="#quapy.method.base.BinaryQuantifier.binary" title="Permalink to this definition"></a></dt>
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<dd><p>Informs that the quantifier is binary</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>True</p>
</dd>
</dl>
</dd></dl>
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</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="quapy.method.base.isaggregative">
<span class="sig-prename descclassname"><span class="pre">quapy.method.base.</span></span><span class="sig-name descname"><span class="pre">isaggregative</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><span class="pre">quapy.method.base.BaseQuantifier</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.base.isaggregative" title="Permalink to this definition"></a></dt>
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<dd><p>Alias for property <cite>aggregative</cite></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>model</strong> – the model</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>True if the model is aggregative, False otherwise</p>
</dd>
</dl>
</dd></dl>
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<dl class="py function">
<dt class="sig sig-object py" id="quapy.method.base.isbinary">
<span class="sig-prename descclassname"><span class="pre">quapy.method.base.</span></span><span class="sig-name descname"><span class="pre">isbinary</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><span class="pre">quapy.method.base.BaseQuantifier</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.base.isbinary" title="Permalink to this definition"></a></dt>
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<dd><p>Alias for property <cite>binary</cite></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>model</strong> – the model</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>True if the model is binary, False otherwise</p>
</dd>
</dl>
</dd></dl>
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<dl class="py function">
<dt class="sig sig-object py" id="quapy.method.base.isprobabilistic">
<span class="sig-prename descclassname"><span class="pre">quapy.method.base.</span></span><span class="sig-name descname"><span class="pre">isprobabilistic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><span class="pre">quapy.method.base.BaseQuantifier</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.base.isprobabilistic" title="Permalink to this definition"></a></dt>
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<dd><p>Alias for property <cite>probabilistic</cite></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>model</strong> – the model</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>True if the model is probabilistic, False otherwise</p>
</dd>
</dl>
</dd></dl>
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</section>
<section id="module-quapy.method.meta">
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<span id="quapy-method-meta-module"></span><h2>quapy.method.meta module<a class="headerlink" href="#module-quapy.method.meta" title="Permalink to this headline"></a></h2>
<dl class="py function">
<dt class="sig sig-object py" id="quapy.method.meta.EACC">
<span class="sig-prename descclassname"><span class="pre">quapy.method.meta.</span></span><span class="sig-name descname"><span class="pre">EACC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">learner</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</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">optim</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">param_mod_sel</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="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.method.meta.EACC" title="Permalink to this definition"></a></dt>
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<dd><p>Implements an ensemble of <a class="reference internal" href="#quapy.method.aggregative.ACC" title="quapy.method.aggregative.ACC"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.ACC</span></code></a> quantifiers, as used by
<a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S1566253517303652">Pérez-Gállego et al., 2019</a>.</p>
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<p>Equivalent to:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ensembleFactory</span><span class="p">(</span><span class="n">learner</span><span class="p">,</span> <span class="n">ACC</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">optim</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
</pre></div>
</div>
<p>See <a class="reference internal" href="#quapy.method.meta.ensembleFactory" title="quapy.method.meta.ensembleFactory"><code class="xref py py-meth docutils literal notranslate"><span class="pre">ensembleFactory()</span></code></a> for further details.</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>learner</strong> – sklearn’s Estimator that generates a classifier</p></li>
<li><p><strong>param_grid</strong> – a dictionary with the grid of parameters to optimize for</p></li>
<li><p><strong>optim</strong> – a valid quantification or classification error, or a string name of it</p></li>
<li><p><strong>param_model_sel</strong> – a dictionary containing any keyworded argument to pass to
<a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ" title="quapy.model_selection.GridSearchQ"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.model_selection.GridSearchQ</span></code></a></p></li>
<li><p><strong>kwargs</strong> – kwargs for the class <a class="reference internal" href="#quapy.method.meta.Ensemble" title="quapy.method.meta.Ensemble"><code class="xref py py-class docutils literal notranslate"><span class="pre">Ensemble</span></code></a></p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>an instance of <a class="reference internal" href="#quapy.method.meta.Ensemble" title="quapy.method.meta.Ensemble"><code class="xref py py-class docutils literal notranslate"><span class="pre">Ensemble</span></code></a></p>
</dd>
</dl>
</dd></dl>
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<dl class="py function">
<dt class="sig sig-object py" id="quapy.method.meta.ECC">
<span class="sig-prename descclassname"><span class="pre">quapy.method.meta.</span></span><span class="sig-name descname"><span class="pre">ECC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">learner</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</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">optim</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">param_mod_sel</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="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.method.meta.ECC" title="Permalink to this definition"></a></dt>
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<dd><p>Implements an ensemble of <a class="reference internal" href="#quapy.method.aggregative.CC" title="quapy.method.aggregative.CC"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.CC</span></code></a> quantifiers, as used by
<a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S1566253517303652">Pérez-Gállego et al., 2019</a>.</p>
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<p>Equivalent to:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ensembleFactory</span><span class="p">(</span><span class="n">learner</span><span class="p">,</span> <span class="n">CC</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">optim</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
</pre></div>
</div>
<p>See <a class="reference internal" href="#quapy.method.meta.ensembleFactory" title="quapy.method.meta.ensembleFactory"><code class="xref py py-meth docutils literal notranslate"><span class="pre">ensembleFactory()</span></code></a> for further details.</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>learner</strong> – sklearn’s Estimator that generates a classifier</p></li>
<li><p><strong>param_grid</strong> – a dictionary with the grid of parameters to optimize for</p></li>
<li><p><strong>optim</strong> – a valid quantification or classification error, or a string name of it</p></li>
<li><p><strong>param_model_sel</strong> – a dictionary containing any keyworded argument to pass to
<a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ" title="quapy.model_selection.GridSearchQ"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.model_selection.GridSearchQ</span></code></a></p></li>
<li><p><strong>kwargs</strong> – kwargs for the class <a class="reference internal" href="#quapy.method.meta.Ensemble" title="quapy.method.meta.Ensemble"><code class="xref py py-class docutils literal notranslate"><span class="pre">Ensemble</span></code></a></p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>an instance of <a class="reference internal" href="#quapy.method.meta.Ensemble" title="quapy.method.meta.Ensemble"><code class="xref py py-class docutils literal notranslate"><span class="pre">Ensemble</span></code></a></p>
</dd>
</dl>
</dd></dl>
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<dl class="py function">
<dt class="sig sig-object py" id="quapy.method.meta.EEMQ">
<span class="sig-prename descclassname"><span class="pre">quapy.method.meta.</span></span><span class="sig-name descname"><span class="pre">EEMQ</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">learner</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</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">optim</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">param_mod_sel</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="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.method.meta.EEMQ" title="Permalink to this definition"></a></dt>
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<dd><p>Implements an ensemble of <a class="reference internal" href="#quapy.method.aggregative.EMQ" title="quapy.method.aggregative.EMQ"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.EMQ</span></code></a> quantifiers.</p>
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<p>Equivalent to:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ensembleFactory</span><span class="p">(</span><span class="n">learner</span><span class="p">,</span> <span class="n">EMQ</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">optim</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
</pre></div>
</div>
<p>See <a class="reference internal" href="#quapy.method.meta.ensembleFactory" title="quapy.method.meta.ensembleFactory"><code class="xref py py-meth docutils literal notranslate"><span class="pre">ensembleFactory()</span></code></a> for further details.</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>learner</strong> – sklearn’s Estimator that generates a classifier</p></li>
<li><p><strong>param_grid</strong> – a dictionary with the grid of parameters to optimize for</p></li>
<li><p><strong>optim</strong> – a valid quantification or classification error, or a string name of it</p></li>
<li><p><strong>param_model_sel</strong> – a dictionary containing any keyworded argument to pass to
<a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ" title="quapy.model_selection.GridSearchQ"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.model_selection.GridSearchQ</span></code></a></p></li>
<li><p><strong>kwargs</strong> – kwargs for the class <a class="reference internal" href="#quapy.method.meta.Ensemble" title="quapy.method.meta.Ensemble"><code class="xref py py-class docutils literal notranslate"><span class="pre">Ensemble</span></code></a></p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>an instance of <a class="reference internal" href="#quapy.method.meta.Ensemble" title="quapy.method.meta.Ensemble"><code class="xref py py-class docutils literal notranslate"><span class="pre">Ensemble</span></code></a></p>
</dd>
</dl>
</dd></dl>
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<dl class="py function">
<dt class="sig sig-object py" id="quapy.method.meta.EHDy">
<span class="sig-prename descclassname"><span class="pre">quapy.method.meta.</span></span><span class="sig-name descname"><span class="pre">EHDy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">learner</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</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">optim</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">param_mod_sel</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="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.method.meta.EHDy" title="Permalink to this definition"></a></dt>
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<dd><p>Implements an ensemble of <a class="reference internal" href="#quapy.method.aggregative.HDy" title="quapy.method.aggregative.HDy"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.HDy</span></code></a> quantifiers, as used by
<a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S1566253517303652">Pérez-Gállego et al., 2019</a>.</p>
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<p>Equivalent to:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ensembleFactory</span><span class="p">(</span><span class="n">learner</span><span class="p">,</span> <span class="n">HDy</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">optim</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
</pre></div>
</div>
<p>See <a class="reference internal" href="#quapy.method.meta.ensembleFactory" title="quapy.method.meta.ensembleFactory"><code class="xref py py-meth docutils literal notranslate"><span class="pre">ensembleFactory()</span></code></a> for further details.</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>learner</strong> – sklearn’s Estimator that generates a classifier</p></li>
<li><p><strong>param_grid</strong> – a dictionary with the grid of parameters to optimize for</p></li>
<li><p><strong>optim</strong> – a valid quantification or classification error, or a string name of it</p></li>
<li><p><strong>param_model_sel</strong> – a dictionary containing any keyworded argument to pass to
<a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ" title="quapy.model_selection.GridSearchQ"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.model_selection.GridSearchQ</span></code></a></p></li>
<li><p><strong>kwargs</strong> – kwargs for the class <a class="reference internal" href="#quapy.method.meta.Ensemble" title="quapy.method.meta.Ensemble"><code class="xref py py-class docutils literal notranslate"><span class="pre">Ensemble</span></code></a></p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>an instance of <a class="reference internal" href="#quapy.method.meta.Ensemble" title="quapy.method.meta.Ensemble"><code class="xref py py-class docutils literal notranslate"><span class="pre">Ensemble</span></code></a></p>
</dd>
</dl>
</dd></dl>
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<dl class="py function">
<dt class="sig sig-object py" id="quapy.method.meta.EPACC">
<span class="sig-prename descclassname"><span class="pre">quapy.method.meta.</span></span><span class="sig-name descname"><span class="pre">EPACC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">learner</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</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">optim</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">param_mod_sel</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="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.method.meta.EPACC" title="Permalink to this definition"></a></dt>
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<dd><p>Implements an ensemble of <a class="reference internal" href="#quapy.method.aggregative.PACC" title="quapy.method.aggregative.PACC"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.PACC</span></code></a> quantifiers.</p>
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<p>Equivalent to:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">ensembleFactory</span><span class="p">(</span><span class="n">learner</span><span class="p">,</span> <span class="n">PACC</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">optim</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
</pre></div>
</div>
<p>See <a class="reference internal" href="#quapy.method.meta.ensembleFactory" title="quapy.method.meta.ensembleFactory"><code class="xref py py-meth docutils literal notranslate"><span class="pre">ensembleFactory()</span></code></a> for further details.</p>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>learner</strong> – sklearn’s Estimator that generates a classifier</p></li>
<li><p><strong>param_grid</strong> – a dictionary with the grid of parameters to optimize for</p></li>
<li><p><strong>optim</strong> – a valid quantification or classification error, or a string name of it</p></li>
<li><p><strong>param_model_sel</strong> – a dictionary containing any keyworded argument to pass to
<a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ" title="quapy.model_selection.GridSearchQ"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.model_selection.GridSearchQ</span></code></a></p></li>
<li><p><strong>kwargs</strong> – kwargs for the class <a class="reference internal" href="#quapy.method.meta.Ensemble" title="quapy.method.meta.Ensemble"><code class="xref py py-class docutils literal notranslate"><span class="pre">Ensemble</span></code></a></p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>an instance of <a class="reference internal" href="#quapy.method.meta.Ensemble" title="quapy.method.meta.Ensemble"><code class="xref py py-class docutils literal notranslate"><span class="pre">Ensemble</span></code></a></p>
</dd>
</dl>
</dd></dl>
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<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.meta.Ensemble">
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<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.method.meta.</span></span><span class="sig-name descname"><span class="pre">Ensemble</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">quantifier</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><span class="pre">quapy.method.base.BaseQuantifier</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">red_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_pos</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">policy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ave'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_sample_size</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">val_split</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">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.method.meta.Ensemble" title="Permalink to this definition"></a></dt>
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<dd><p>Bases: <a class="reference internal" href="#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.base.BaseQuantifier</span></code></a></p>
<dl class="py attribute">
<dt class="sig sig-object py" id="quapy.method.meta.Ensemble.VALID_POLICIES">
<span class="sig-name descname"><span class="pre">VALID_POLICIES</span></span><em class="property"> <span class="pre">=</span> <span class="pre">{'ave',</span> <span class="pre">'ds',</span> <span class="pre">'mae',</span> <span class="pre">'mkld',</span> <span class="pre">'mnkld',</span> <span class="pre">'mrae',</span> <span class="pre">'mse',</span> <span class="pre">'ptr'}</span></em><a class="headerlink" href="#quapy.method.meta.Ensemble.VALID_POLICIES" title="Permalink to this definition"></a></dt>
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<dd><p>Implementation of the Ensemble methods for quantification described by
<a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S1566253516300628">Pérez-Gállego et al., 2017</a>
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and
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<a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S1566253517303652">Pérez-Gállego et al., 2019</a>.
The policies implemented include:</p>
<ul class="simple">
<li><p>Average (<cite>policy=’ave’</cite>): computes class prevalence estimates as the average of the estimates
returned by the base quantifiers.</p></li>
<li><p>Training Prevalence (<cite>policy=’ptr’</cite>): applies a dynamic selection to the ensemble’s members by retaining only
those members such that the class prevalence values in the samples they use as training set are closest to
preliminary class prevalence estimates computed as the average of the estimates of all the members. The final
estimate is recomputed by considering only the selected members.</p></li>
<li><p>Distribution Similarity (<cite>policy=’ds’</cite>): performs a dynamic selection of base members by retaining
the members trained on samples whose distribution of posterior probabilities is closest, in terms of the
Hellinger Distance, to the distribution of posterior probabilities in the test sample</p></li>
<li><p>Accuracy (<cite>policy=’&lt;valid error name&gt;’</cite>): performs a static selection of the ensemble members by
retaining those that minimize a quantification error measure, which is passed as an argument.</p></li>
</ul>
<p>Example:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">model</span> <span class="o">=</span> <span class="n">Ensemble</span><span class="p">(</span><span class="n">quantifier</span><span class="o">=</span><span class="n">ACC</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">()),</span> <span class="n">size</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">policy</span><span class="o">=</span><span class="s1">&#39;ave&#39;</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>quantifier</strong> – base quantification member of the ensemble</p></li>
<li><p><strong>size</strong> – number of members</p></li>
<li><p><strong>red_size</strong> – number of members to retain after selection (depending on the policy)</p></li>
<li><p><strong>min_pos</strong> – minimum number of positive instances to consider a sample as valid</p></li>
<li><p><strong>policy</strong> – the selection policy; available policies include: <cite>ave</cite> (default), <cite>ptr</cite>, <cite>ds</cite>, and accuracy
(which is instantiated via a valid error name, e.g., <cite>mae</cite>)</p></li>
<li><p><strong>max_sample_size</strong> – maximum number of instances to consider in the samples (set to None
to indicate no limit, default)</p></li>
<li><p><strong>val_split</strong> – a float in range (0,1) indicating the proportion of data to be used as a stratified held-out
validation split, or a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> (the split itself).</p></li>
<li><p><strong>n_jobs</strong> – number of parallel workers (default 1)</p></li>
<li><p><strong>verbose</strong> – set to True (default is False) to get some information in standard output</p></li>
</ul>
</dd>
</dl>
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</dd></dl>
<dl class="py property">
<dt class="sig sig-object py" id="quapy.method.meta.Ensemble.aggregative">
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">aggregative</span></span><a class="headerlink" href="#quapy.method.meta.Ensemble.aggregative" title="Permalink to this definition"></a></dt>
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<dd><p>Indicates that the quantifier is not aggregative.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>False</p>
</dd>
</dl>
</dd></dl>
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<dl class="py property">
<dt class="sig sig-object py" id="quapy.method.meta.Ensemble.binary">
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">binary</span></span><a class="headerlink" href="#quapy.method.meta.Ensemble.binary" title="Permalink to this definition"></a></dt>
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<dd><p>Returns a boolean indicating whether the base quantifiers are binary or not</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>boolean</p>
</dd>
</dl>
</dd></dl>
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<dl class="py property">
<dt class="sig sig-object py" id="quapy.method.meta.Ensemble.classes_">
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">classes_</span></span><a class="headerlink" href="#quapy.method.meta.Ensemble.classes_" title="Permalink to this definition"></a></dt>
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<dd><p>Class labels, in the same order in which class prevalence values are to be computed.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>array-like</p>
</dd>
</dl>
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</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.meta.Ensemble.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">data</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">float</span><span class="p"><span class="pre">,</span> </span><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.meta.Ensemble.fit" title="Permalink to this definition"></a></dt>
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<dd><p>Trains a quantifier.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>data</strong> – a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>self</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.meta.Ensemble.get_params">
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.meta.Ensemble.get_params" title="Permalink to this definition"></a></dt>
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<dd><p>This function should not be used within <a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ" title="quapy.model_selection.GridSearchQ"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.model_selection.GridSearchQ</span></code></a> (is here for compatibility
with the abstract class).
Instead, use <cite>Ensemble(GridSearchQ(q),…)</cite>, with <cite>q</cite> a Quantifier (recommended), or
<cite>Ensemble(Q(GridSearchCV(l)))</cite> with <cite>Q</cite> a quantifier class that has a learner <cite>l</cite> optimized for</p>
<blockquote>
<div><p>classification (not recommended).</p>
</div></blockquote>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>raises an Exception</p>
</dd>
</dl>
</dd></dl>
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<dl class="py property">
<dt class="sig sig-object py" id="quapy.method.meta.Ensemble.probabilistic">
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">probabilistic</span></span><a class="headerlink" href="#quapy.method.meta.Ensemble.probabilistic" title="Permalink to this definition"></a></dt>
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<dd><p>Indicates that the quantifier is not probabilistic.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>False</p>
</dd>
</dl>
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</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.meta.Ensemble.quantify">
<span class="sig-name descname"><span class="pre">quantify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.meta.Ensemble.quantify" title="Permalink to this definition"></a></dt>
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<dd><p>Generate class prevalence estimates for the sample’s instances</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>instances</strong> – array-like</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(self.n_classes_,)</cite> with class prevalence estimates.</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.meta.Ensemble.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.method.meta.Ensemble.set_params" title="Permalink to this definition"></a></dt>
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<dd><p>This function should not be used within <a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ" title="quapy.model_selection.GridSearchQ"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.model_selection.GridSearchQ</span></code></a> (is here for compatibility
with the abstract class).
Instead, use <cite>Ensemble(GridSearchQ(q),…)</cite>, with <cite>q</cite> a Quantifier (recommended), or
<cite>Ensemble(Q(GridSearchCV(l)))</cite> with <cite>Q</cite> a quantifier class that has a learner <cite>l</cite> optimized for</p>
<blockquote>
<div><p>classification (not recommended).</p>
</div></blockquote>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>parameters</strong> – dictionary</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>raises an Exception</p>
</dd>
</dl>
</dd></dl>
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</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="quapy.method.meta.ensembleFactory">
<span class="sig-prename descclassname"><span class="pre">quapy.method.meta.</span></span><span class="sig-name descname"><span class="pre">ensembleFactory</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">learner</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_quantifier_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</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">optim</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">param_model_sel</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">dict</span><span class="p"><span class="pre">]</span></span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="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.method.meta.ensembleFactory" title="Permalink to this definition"></a></dt>
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<dd><p>Ensemble factory. Provides a unified interface for instantiating ensembles that can be optimized (via model
selection for quantification) for a given evaluation metric using <a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ" title="quapy.model_selection.GridSearchQ"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.model_selection.GridSearchQ</span></code></a>.
If the evaluation metric is classification-oriented
(instead of quantification-oriented), then the optimization will be carried out via sklearn’s
<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html">GridSearchCV</a>.</p>
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<p>Example to instantiate an <a class="reference internal" href="#quapy.method.meta.Ensemble" title="quapy.method.meta.Ensemble"><code class="xref py py-class docutils literal notranslate"><span class="pre">Ensemble</span></code></a> based on <a class="reference internal" href="#quapy.method.aggregative.PACC" title="quapy.method.aggregative.PACC"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.PACC</span></code></a>
in which the base members are optimized for <a class="reference internal" href="quapy.html#quapy.error.mae" title="quapy.error.mae"><code class="xref py py-meth docutils literal notranslate"><span class="pre">quapy.error.mae()</span></code></a> via
<a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ" title="quapy.model_selection.GridSearchQ"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.model_selection.GridSearchQ</span></code></a>. The ensemble follows the policy <cite>Accuracy</cite> based
on <a class="reference internal" href="quapy.html#quapy.error.mae" title="quapy.error.mae"><code class="xref py py-meth docutils literal notranslate"><span class="pre">quapy.error.mae()</span></code></a> (the same measure being optimized),
meaning that a static selection of members of the ensemble is made based on their performance
in terms of this error.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">param_grid</span> <span class="o">=</span> <span class="p">{</span>
<span class="gp">&gt;&gt;&gt; </span> <span class="s1">&#39;C&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">7</span><span class="p">),</span>
<span class="gp">&gt;&gt;&gt; </span> <span class="s1">&#39;class_weight&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;balanced&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="p">}</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">param_mod_sel</span> <span class="o">=</span> <span class="p">{</span>
<span class="gp">&gt;&gt;&gt; </span> <span class="s1">&#39;sample_size&#39;</span><span class="p">:</span> <span class="mi">500</span><span class="p">,</span>
<span class="gp">&gt;&gt;&gt; </span> <span class="s1">&#39;protocol&#39;</span><span class="p">:</span> <span class="s1">&#39;app&#39;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="p">}</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">common</span><span class="o">=</span><span class="p">{</span>
<span class="gp">&gt;&gt;&gt; </span> <span class="s1">&#39;max_sample_size&#39;</span><span class="p">:</span> <span class="mi">1000</span><span class="p">,</span>
<span class="gp">&gt;&gt;&gt; </span> <span class="s1">&#39;n_jobs&#39;</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span>
<span class="gp">&gt;&gt;&gt; </span> <span class="s1">&#39;param_grid&#39;</span><span class="p">:</span> <span class="n">param_grid</span><span class="p">,</span>
<span class="gp">&gt;&gt;&gt; </span> <span class="s1">&#39;param_mod_sel&#39;</span><span class="p">:</span> <span class="n">param_mod_sel</span><span class="p">,</span>
<span class="gp">&gt;&gt;&gt; </span><span class="p">}</span>
<span class="go">&gt;&gt;&gt;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">ensembleFactory</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">(),</span> <span class="n">PACC</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="s1">&#39;mae&#39;</span><span class="p">,</span> <span class="n">policy</span><span class="o">=</span><span class="s1">&#39;mae&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">common</span><span class="p">)</span>
</pre></div>
</div>
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<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>learner</strong> – sklearn’s Estimator that generates a classifier</p></li>
<li><p><strong>base_quantifier_class</strong> – a class of quantifiers</p></li>
<li><p><strong>param_grid</strong> – a dictionary with the grid of parameters to optimize for</p></li>
<li><p><strong>optim</strong> – a valid quantification or classification error, or a string name of it</p></li>
<li><p><strong>param_model_sel</strong> – a dictionary containing any keyworded argument to pass to
<a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ" title="quapy.model_selection.GridSearchQ"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.model_selection.GridSearchQ</span></code></a></p></li>
<li><p><strong>kwargs</strong> – kwargs for the class <a class="reference internal" href="#quapy.method.meta.Ensemble" title="quapy.method.meta.Ensemble"><code class="xref py py-class docutils literal notranslate"><span class="pre">Ensemble</span></code></a></p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>an instance of <a class="reference internal" href="#quapy.method.meta.Ensemble" title="quapy.method.meta.Ensemble"><code class="xref py py-class docutils literal notranslate"><span class="pre">Ensemble</span></code></a></p>
</dd>
</dl>
</dd></dl>
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<dl class="py function">
<dt class="sig sig-object py" id="quapy.method.meta.get_probability_distribution">
<span class="sig-prename descclassname"><span class="pre">quapy.method.meta.</span></span><span class="sig-name descname"><span class="pre">get_probability_distribution</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">posterior_probabilities</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.meta.get_probability_distribution" title="Permalink to this definition"></a></dt>
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<dd><p>Gets a histogram out of the posterior probabilities (only for the binary case).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>posterior_probabilities</strong> – array-like of shape <cite>(n_instances, 2,)</cite></p></li>
<li><p><strong>bins</strong> – integer</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><cite>np.ndarray</cite> with the relative frequencies for each bin (for the positive class only)</p>
</dd>
</dl>
</dd></dl>
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</section>
<section id="module-quapy.method.neural">
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<span id="quapy-method-neural-module"></span><h2>quapy.method.neural module<a class="headerlink" href="#module-quapy.method.neural" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.neural.QuaNetModule">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.method.neural.</span></span><span class="sig-name descname"><span class="pre">QuaNetModule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">doc_embedding_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">stats_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lstm_hidden_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">lstm_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">ff_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[1024,</span> <span class="pre">512]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bidirectional</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qdrop_p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">order_by</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.neural.QuaNetModule" 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>
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<p>Implements the <a class="reference external" href="https://dl.acm.org/doi/abs/10.1145/3269206.3269287">QuaNet</a> forward pass.
See <a class="reference internal" href="#quapy.method.neural.QuaNetTrainer" title="quapy.method.neural.QuaNetTrainer"><code class="xref py py-class docutils literal notranslate"><span class="pre">QuaNetTrainer</span></code></a> for training QuaNet.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>doc_embedding_size</strong> – integer, the dimensionality of the document embeddings</p></li>
<li><p><strong>n_classes</strong> – integer, number of classes</p></li>
<li><p><strong>stats_size</strong> – integer, number of statistics estimated by simple quantification methods</p></li>
<li><p><strong>lstm_hidden_size</strong> – integer, hidden dimensionality of the LSTM cell</p></li>
<li><p><strong>lstm_nlayers</strong> – integer, number of LSTM layers</p></li>
<li><p><strong>ff_layers</strong> – list of integers, dimensions of the densely-connected FF layers on top of the
quantification embedding</p></li>
<li><p><strong>bidirectional</strong> – boolean, whether or not to use bidirectional LSTM</p></li>
<li><p><strong>qdrop_p</strong> – float, dropout probability</p></li>
<li><p><strong>order_by</strong> – integer, class for which the document embeddings are to be sorted</p></li>
</ul>
</dd>
</dl>
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<dl class="py property">
<dt class="sig sig-object py" id="quapy.method.neural.QuaNetModule.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.method.neural.QuaNetModule.device" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.neural.QuaNetModule.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">doc_embeddings</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">doc_posteriors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">statistics</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.neural.QuaNetModule.forward" title="Permalink to this definition"></a></dt>
<dd><p>Defines the computation performed at every call.</p>
<p>Should be overridden by all subclasses.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Although the recipe for forward pass needs to be defined within
this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
instead of this since the former takes care of running the
registered hooks while the latter silently ignores them.</p>
</div>
</dd></dl>
</dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.neural.QuaNetTrainer">
<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.method.neural.</span></span><span class="sig-name descname"><span class="pre">QuaNetTrainer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">learner</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_epochs</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">tr_iter_per_poch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">va_iter_per_poch</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">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">lstm_hidden_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">lstm_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">ff_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[1024,</span> <span class="pre">512]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bidirectional</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qdrop_p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</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">checkpointdir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'../checkpoint'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpointname</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">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.neural.QuaNetTrainer" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.base.BaseQuantifier</span></code></a></p>
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<p>Implementation of <a class="reference external" href="https://dl.acm.org/doi/abs/10.1145/3269206.3269287">QuaNet</a>, a neural network for
quantification. This implementation uses <a class="reference external" href="https://pytorch.org/">PyTorch</a> and can take advantage of GPU
for speeding-up the training phase.</p>
<p>Example:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">quapy.method.meta</span> <span class="kn">import</span> <span class="n">QuaNet</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">quapy.classification.neural</span> <span class="kn">import</span> <span class="n">NeuralClassifierTrainer</span><span class="p">,</span> <span class="n">CNNnet</span>
<span class="go">&gt;&gt;&gt;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># use samples of 100 elements</span>
<span class="gp">&gt;&gt;&gt; </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="o">=</span> <span class="mi">100</span>
<span class="go">&gt;&gt;&gt;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># load the kindle dataset as text, and convert words to numerical indexes</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">dataset</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="s1">&#39;kindle&#39;</span><span class="p">,</span> <span class="n">pickle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">preprocessing</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="go">&gt;&gt;&gt;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># the text classifier is a CNN trained by NeuralClassifierTrainer</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">cnn</span> <span class="o">=</span> <span class="n">CNNnet</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">vocabulary_size</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">n_classes</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">learner</span> <span class="o">=</span> <span class="n">NeuralClassifierTrainer</span><span class="p">(</span><span class="n">cnn</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">)</span>
<span class="go">&gt;&gt;&gt;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># train QuaNet (QuaNet is an alias to QuaNetTrainer)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span> <span class="o">=</span> <span class="n">QuaNet</span><span class="p">(</span><span class="n">learner</span><span class="p">,</span> <span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;SAMPLE_SIZE&#39;</span><span class="p">],</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">estim_prevalence</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>learner</strong> – an object implementing <cite>fit</cite> (i.e., that can be trained on labelled data),
<cite>predict_proba</cite> (i.e., that can generate posterior probabilities of unlabelled examples) and
<cite>transform</cite> (i.e., that can generate embedded representations of the unlabelled instances).</p></li>
<li><p><strong>sample_size</strong> – integer, the sample size</p></li>
<li><p><strong>n_epochs</strong> – integer, maximum number of training epochs</p></li>
<li><p><strong>tr_iter_per_poch</strong> – integer, number of training iterations before considering an epoch complete</p></li>
<li><p><strong>va_iter_per_poch</strong> – integer, number of validation iterations to perform after each epoch</p></li>
<li><p><strong>lr</strong> – float, the learning rate</p></li>
<li><p><strong>lstm_hidden_size</strong> – integer, hidden dimensionality of the LSTM cells</p></li>
<li><p><strong>lstm_nlayers</strong> – integer, number of LSTM layers</p></li>
<li><p><strong>ff_layers</strong> – list of integers, dimensions of the densely-connected FF layers on top of the
quantification embedding</p></li>
<li><p><strong>bidirectional</strong> – boolean, indicates whether the LSTM is bidirectional or not</p></li>
<li><p><strong>qdrop_p</strong> – float, dropout probability</p></li>
<li><p><strong>patience</strong> – integer, number of epochs showing no improvement in the validation set before stopping the
training phase (early stopping)</p></li>
<li><p><strong>checkpointdir</strong> – string, a path where to store models’ checkpoints</p></li>
<li><p><strong>checkpointname</strong> – string (optional), the name of the model’s checkpoint</p></li>
<li><p><strong>device</strong> – string, indicate “cpu” or “cuda”</p></li>
</ul>
</dd>
</dl>
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<dl class="py property">
<dt class="sig sig-object py" id="quapy.method.neural.QuaNetTrainer.classes_">
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">classes_</span></span><a class="headerlink" href="#quapy.method.neural.QuaNetTrainer.classes_" title="Permalink to this definition"></a></dt>
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<dd><p>Class labels, in the same order in which class prevalence values are to be computed.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>array-like</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.neural.QuaNetTrainer.clean_checkpoint">
<span class="sig-name descname"><span class="pre">clean_checkpoint</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.neural.QuaNetTrainer.clean_checkpoint" title="Permalink to this definition"></a></dt>
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<dd><p>Removes the checkpoint</p>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.neural.QuaNetTrainer.clean_checkpoint_dir">
<span class="sig-name descname"><span class="pre">clean_checkpoint_dir</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.neural.QuaNetTrainer.clean_checkpoint_dir" title="Permalink to this definition"></a></dt>
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<dd><p>Removes anything contained in the checkpoint directory</p>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.neural.QuaNetTrainer.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">data</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_learner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.neural.QuaNetTrainer.fit" title="Permalink to this definition"></a></dt>
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<dd><p>Trains QuaNet.</p>
<dl class="field-list simple">
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<dt class="field-odd">Parameters</dt>
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<dd class="field-odd"><ul class="simple">
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<li><p><strong>data</strong> – the training data on which to train QuaNet. If <cite>fit_learner=True</cite>, the data will be split in
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40/40/20 for training the classifier, training QuaNet, and validating QuaNet, respectively. If
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<cite>fit_learner=False</cite>, the data will be split in 66/34 for training QuaNet and validating it, respectively.</p></li>
<li><p><strong>fit_learner</strong> – if True, trains the classifier on a split containing 40% of the data</p></li>
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</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>self</p>
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</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.neural.QuaNetTrainer.get_params">
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.neural.QuaNetTrainer.get_params" title="Permalink to this definition"></a></dt>
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<dd><p>Return the current parameters of the quantifier.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>deep</strong> – for compatibility with sklearn</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a dictionary of param-value pairs</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.neural.QuaNetTrainer.quantify">
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<span class="sig-name descname"><span class="pre">quantify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.neural.QuaNetTrainer.quantify" title="Permalink to this definition"></a></dt>
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<dd><p>Generate class prevalence estimates for the sample’s instances</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>instances</strong> – array-like</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><cite>np.ndarray</cite> of shape <cite>(self.n_classes_,)</cite> with class prevalence estimates.</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.neural.QuaNetTrainer.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.method.neural.QuaNetTrainer.set_params" title="Permalink to this definition"></a></dt>
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<dd><p>Set the parameters of the quantifier.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>parameters</strong> – dictionary of param-value pairs</p>
</dd>
</dl>
</dd></dl>
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</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="quapy.method.neural.mae_loss">
<span class="sig-prename descclassname"><span class="pre">quapy.method.neural.</span></span><span class="sig-name descname"><span class="pre">mae_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.neural.mae_loss" title="Permalink to this definition"></a></dt>
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<dd><p>Torch-like wrapper for the Mean Absolute Error</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>output</strong> – predictions</p></li>
<li><p><strong>target</strong> – ground truth values</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>mean absolute error loss</p>
</dd>
</dl>
</dd></dl>
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</section>
<section id="module-quapy.method.non_aggregative">
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<span id="quapy-method-non-aggregative-module"></span><h2>quapy.method.non_aggregative module<a class="headerlink" href="#module-quapy.method.non_aggregative" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation">
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<em class="property"><span class="pre">class</span> </em><span class="sig-prename descclassname"><span class="pre">quapy.method.non_aggregative.</span></span><span class="sig-name descname"><span class="pre">MaximumLikelihoodPrevalenceEstimation</span></span><a class="headerlink" href="#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation" title="Permalink to this definition"></a></dt>
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<dd><p>Bases: <a class="reference internal" href="#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.base.BaseQuantifier</span></code></a></p>
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<p>The <cite>Maximum Likelihood Prevalence Estimation</cite> (MLPE) method is a lazy method that assumes there is no prior
probability shift between training and test instances (put it other way, that the i.i.d. assumpion holds).
The estimation of class prevalence values for any test sample is always (i.e., irrespective of the test sample
itself) the class prevalence seen during training. This method is considered to be a lower-bound quantifier that
any quantification method should beat.</p>
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<dl class="py property">
<dt class="sig sig-object py" id="quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.classes_">
<em class="property"><span class="pre">property</span> </em><span class="sig-name descname"><span class="pre">classes_</span></span><a class="headerlink" href="#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.classes_" title="Permalink to this definition"></a></dt>
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<dd><p>Number of classes</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<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.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.fit">
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<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">data</span></span><span class="p"><span class="pre">:</span></span> <span class="n"><a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">quapy.data.base.LabelledCollection</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.fit" title="Permalink to this definition"></a></dt>
<dd><p>Computes the training prevalence and stores it.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>data</strong> – the training sample</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>self</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.get_params">
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<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deep</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.get_params" title="Permalink to this definition"></a></dt>
<dd><p>Does nothing, since this learner has no parameters.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>deep</strong> – for compatibility with sklearn</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><cite>None</cite></p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.quantify">
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<span class="sig-name descname"><span class="pre">quantify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">instances</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.quantify" title="Permalink to this definition"></a></dt>
<dd><p>Ignores the input instances and returns, as the class prevalence estimantes, the training prevalence.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>instances</strong> – array-like (ignored)</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>the class prevalence seen during training</p>
</dd>
</dl>
</dd></dl>
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<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.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.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.set_params" title="Permalink to this definition"></a></dt>
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<dd><p>Does nothing, since this learner has no parameters.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>parameters</strong> – dictionary of param-value pairs (ignored)</p>
</dd>
</dl>
</dd></dl>
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</dd></dl>
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</section>
<section id="module-quapy.method">
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<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-quapy.method" title="Permalink to this headline"></a></h2>
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</section>
</section>
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<div class="clearer"></div>
</div>
</div>
</div>
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<h3><a href="index.html">Table of Contents</a></h3>
<ul>
<li><a class="reference internal" href="#">quapy.method package</a><ul>
<li><a class="reference internal" href="#submodules">Submodules</a></li>
<li><a class="reference internal" href="#module-quapy.method.aggregative">quapy.method.aggregative module</a></li>
<li><a class="reference internal" href="#module-quapy.method.base">quapy.method.base module</a></li>
<li><a class="reference internal" href="#module-quapy.method.meta">quapy.method.meta module</a></li>
<li><a class="reference internal" href="#module-quapy.method.neural">quapy.method.neural module</a></li>
<li><a class="reference internal" href="#module-quapy.method.non_aggregative">quapy.method.non_aggregative module</a></li>
<li><a class="reference internal" href="#module-quapy.method">Module contents</a></li>
</ul>
</li>
</ul>
<h4>Previous topic</h4>
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