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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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<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>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<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>
<dd><p>Trains a ACC quantifier
:param data: the training set
:param fit_learner: set to False to bypass the training (the learner is assumed to be already fit)
:param val_split: either a float in (0,1) indicating the proportion of training instances to use for</p>
<blockquote>
<div><p>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>
</div></blockquote>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>self</p>
</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>
<dd></dd></dl>
</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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
</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
results. Aggregative Quantifiers thus implement a _classify_ method and maintain a _learner_ attribute.</p>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<dl class="py property">
<dt class="sig sig-object py" id="quapy.method.aggregative.AggregativeQuantifier.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.aggregative.AggregativeQuantifier.n_classes" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
</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>
<p>The most basic Quantification method. One that simply classifies all instances and countes how many have been
attributed each of the classes in order to compute class prevalence estimates.</p>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.CC.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.CC.aggregate" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<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>
<dd><p>Trains the Classify &amp; Count method unless _fit_learner_ is False, in which case it is assumed to be already fit.
:param data: training data
:param fit_learner: if False, the classifier is assumed to be fit
:return: self</p>
</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>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
</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>
<p>The method is described in:
Saerens, M., Latinne, P., and Decaestecker, C. (2002).
Adjusting the outputs of a classifier to new a priori probabilities: A simple procedure.
Neural Computation, 14(1): 21–41.</p>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<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>
<p>Implementation of the method based on the Hellinger Distance y (HDy) proposed by
González-Castro, V., Alaiz-Rodrı́guez, R., and Alegre, E. (2013). Class distribution
estimation based on the Hellinger distance. Information Sciences, 218:146–164.</p>
<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>
<dd></dd></dl>
<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>
<dd><p>Trains a HDy quantifier
:param data: the training set
:param fit_learner: set to False to bypass the training (the learner is assumed to be already fit)
:param val_split: either a float in (0,1) indicating the proportion of training instances to use for</p>
<blockquote>
<div><p>validation (e.g., 0.3 for using 30% of the training set as validation data), or a LabelledCollection
indicating the validation set itself</p>
</div></blockquote>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>self</p>
</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>
</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>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.MS.optimize_threshold">
<span class="sig-name descname"><span class="pre">optimize_threshold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">probabilities</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.MS.optimize_threshold" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</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>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.MS2.optimize_threshold">
<span class="sig-name descname"><span class="pre">optimize_threshold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">probabilities</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.MS2.optimize_threshold" 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.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>
<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 ExplicitLossMinimization quantifier in
Gao, W., Sebastiani, F.: From classification to quantification in tweet sentiment analysis.
Social Network Analysis and Mining 6(19), 1–22 (2016)</p>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<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>
<dd></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></dd></dl>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
</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>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<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>
<dd><p>Trains a PACC quantifier
:param data: the training set
:param fit_learner: set to False to bypass the training (the learner is assumed to be already fit)
:param val_split: either a float in (0,1) indicating the proportion of training instances to use for</p>
<blockquote>
<div><p>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>
</div></blockquote>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>self</p>
</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>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
</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>
<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>
</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>
<p>Esuli, A. and Sebastiani, F. (2015).
Optimizing text quantifiers for multivariate loss functions.
ACM Transactions on Knowledge Discovery and Data, 9(4):Article 27.</p>
</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>
<p>Esuli, A. and Sebastiani, F. (2015).
Optimizing text quantifiers for multivariate loss functions.
ACM Transactions on Knowledge Discovery and Data, 9(4):Article 27.</p>
</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>
<p>Barranquero, J., Díez, J., and del Coz, J. J. (2015).
Quantification-oriented learning based on reliable classifiers.
Pattern Recognition, 48(2):591–604.</p>
</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>
</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>
</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>
<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>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.ThresholdOptimization.compute_fpr">
<span class="sig-name descname"><span class="pre">compute_fpr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">FP</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">TN</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.ThresholdOptimization.compute_fpr" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.ThresholdOptimization.compute_table">
<span class="sig-name descname"><span class="pre">compute_table</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.ThresholdOptimization.compute_table" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.ThresholdOptimization.compute_tpr">
<span class="sig-name descname"><span class="pre">compute_tpr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">TP</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">FP</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.ThresholdOptimization.compute_tpr" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<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>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.aggregative.ThresholdOptimization.optimize_threshold">
<span class="sig-name descname"><span class="pre">optimize_threshold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">probabilities</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.aggregative.ThresholdOptimization.optimize_threshold" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</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>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="quapy.method.aggregative.training_helper">
<span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">training_helper</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">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="p"><span class="pre">:</span></span> <span class="n"><span class="pre">bool</span></span> <span class="o"><span class="pre">=</span></span> <span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ensure_probabilistic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">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.training_helper" title="Permalink to this definition"></a></dt>
<dd><p>Training procedure common to all Aggregative Quantifiers.
:param learner: the learner to be fit
:param data: the data on which to fit the learner. If requested, the data will be split before fitting the learner.
:param fit_learner: whether or not to fit the learner (if False, then bypasses any action)
:param ensure_probabilistic: if True, guarantees that the resulting classifier implements predict_proba (if the
learner is not probabilistic, then a CalibratedCV instance of it is trained)
:param val_split: if specified as a float, indicates the proportion of training instances that will define the
validation split (e.g., 0.3 for using 30% of the training set as validation data); if specified as a
LabelledCollection, represents the validation split itself
:return: the learner trained on the training set, and the unused data (a _LabelledCollection_ if train_val_split&gt;0
or None otherwise) to be used as a validation set for any subsequent parameter fitting</p>
</dd></dl>
adding documentation, adding brokenbar plots, merging plots from tweetsent with density
2021-11-22 18:10:48 +01:00
</section>
<section id="module-quapy.method.base">
doc with sphinx
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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>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
</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>
<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>
<dd></dd></dl>
</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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
adding documentation, adding brokenbar plots, merging plots from tweetsent with density
2021-11-22 18:10:48 +01:00
</section>
<section id="module-quapy.method.meta">
doc with sphinx
2021-11-09 15:50:53 +01:00
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.meta.Ensemble">
<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="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>
<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>
<dd><p>Methods from the articles:
Pérez-Gállego, P., Quevedo, J. R., &amp; del Coz, J. J. (2017).
Using ensembles for problems with characterizable changes in data distribution: A case study on quantification.
Information Fusion, 34, 87-100.
and
Pérez-Gállego, P., Castano, A., Quevedo, J. R., &amp; del Coz, J. J. (2019).
Dynamic ensemble selection for quantification tasks.
Information Fusion, 45, 1-15.</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.meta.Ensemble.accuracy_policy">
<span class="sig-name descname"><span class="pre">accuracy_policy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">error_name</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.meta.Ensemble.accuracy_policy" title="Permalink to this definition"></a></dt>
<dd><p>Selects the red_size best performant quantifiers in a static way (i.e., dropping all non-selected instances).
For each model in the ensemble, the performance is measured in terms of _error_name_ on the quantification of
the samples used for training the rest of the models in the ensemble.</p>
</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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.meta.Ensemble.ds_policy">
<span class="sig-name descname"><span class="pre">ds_policy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">predictions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.meta.Ensemble.ds_policy" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.meta.Ensemble.ds_policy_get_posteriors">
<span class="sig-name descname"><span class="pre">ds_policy_get_posteriors</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.meta.Ensemble.ds_policy_get_posteriors" title="Permalink to this definition"></a></dt>
<dd><p>In the original article, this procedure is not described in a sufficient level of detail. The paper only says
that the distribution of posterior probabilities from training and test examples is compared by means of the
Hellinger Distance. However, how these posterior probabilities are generated is not specified. In the article,
a Logistic Regressor (LR) is used as the classifier device and that could be used for this purpose. However, in
general, a Quantifier is not necessarily an instance of Aggreggative Probabilistic Quantifiers, and so, that the
quantifier builds on top of a probabilistic classifier cannot be given for granted. Additionally, it would not
be correct to generate the posterior probabilities for training documents that have concurred in training the
classifier that generates them.
This function thus generates the posterior probabilities for all training documents in a cross-validation way,
using a LR with hyperparameters that have previously been optimized via grid search in 5FCV.
:return P,f, where P is a ndarray containing the posterior probabilities of the training data, generated via
cross-validation and using an optimized LR, and the function to be used in order to generate posterior
probabilities for test instances.</p>
</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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.meta.Ensemble.ptr_policy">
<span class="sig-name descname"><span class="pre">ptr_policy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">predictions</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.meta.Ensemble.ptr_policy" title="Permalink to this definition"></a></dt>
<dd><p>Selects the predictions made by models that have been trained on samples with a prevalence that is most similar
to a first approximation of the test prevalence as made by all models in the ensemble.</p>
</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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.meta.Ensemble.sout">
<span class="sig-name descname"><span class="pre">sout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">msg</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.meta.Ensemble.sout" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</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>
<dd></dd></dl>
<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>
<dd></dd></dl>
adding documentation, adding brokenbar plots, merging plots from tweetsent with density
2021-11-22 18:10:48 +01:00
</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>
<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>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.neural.QuaNetModule.init_hidden">
<span class="sig-name descname"><span class="pre">init_hidden</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.neural.QuaNetModule.init_hidden" title="Permalink to this definition"></a></dt>
<dd></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>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<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>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.neural.QuaNetTrainer.epoch">
<span class="sig-name descname"><span class="pre">epoch</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">posteriors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iterations</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">early_stop</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.neural.QuaNetTrainer.epoch" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<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>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>data</strong> – the training data on which to train QuaNet. If fit_learner=True, the data will be split in</p>
</dd>
</dl>
<p>40/40/20 for training the classifier, training QuaNet, and validating QuaNet, respectively. If
fit_learner=False, the data will be split in 66/34 for training QuaNet and validating it, respectively.
:param fit_learner: if true, trains the classifier on a split containing 40% of the data
:return: self</p>
</dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.neural.QuaNetTrainer.get_aggregative_estims">
<span class="sig-name descname"><span class="pre">get_aggregative_estims</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">posteriors</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.neural.QuaNetTrainer.get_aggregative_estims" title="Permalink to this definition"></a></dt>
<dd></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>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.neural.QuaNetTrainer.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>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.neural.QuaNetTrainer.quantify" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<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>
<dd></dd></dl>
</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>
<dd></dd></dl>
adding documentation, adding brokenbar plots, merging plots from tweetsent with density
2021-11-22 18:10:48 +01:00
</section>
<section id="module-quapy.method.non_aggregative">
doc with sphinx
2021-11-09 15:50:53 +01:00
<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">
<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><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation" 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>
<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>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.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="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.fit" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.get_params">
<span class="sig-name descname"><span class="pre">get_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.get_params" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py method">
<dt class="sig sig-object py" id="quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.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">documents</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.quantify" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<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>
<dd></dd></dl>
</dd></dl>
adding documentation, adding brokenbar plots, merging plots from tweetsent with density
2021-11-22 18:10:48 +01:00
</section>
<section id="module-quapy.method">
doc with sphinx
2021-11-09 15:50:53 +01:00
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-quapy.method" title="Permalink to this headline"></a></h2>
adding documentation, adding brokenbar plots, merging plots from tweetsent with density
2021-11-22 18:10:48 +01:00
</section>
</section>
doc with sphinx
2021-11-09 15:50:53 +01:00
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<li><a class="reference internal" href="#module-quapy.method.aggregative">quapy.method.aggregative module</a></li>
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<li><a class="reference internal" href="#module-quapy.method.non_aggregative">quapy.method.non_aggregative module</a></li>
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