<spanid="quapy-method-aggregative-module"></span><h2>quapy.method.aggregative module<aclass="headerlink"href="#module-quapy.method.aggregative"title="Permalink to this heading"></a></h2>
<p><aclass="reference external"href="https://link.springer.com/article/10.1007/s10618-008-0097-y">Adjusted Classify & Count</a>,
the “adjusted” variant of <aclass="reference internal"href="#quapy.method.aggregative.CC"title="quapy.method.aggregative.CC"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">CC</span></code></a>, that corrects the predictions of CC
according to the <cite>misclassification rates</cite>.</p>
<li><p>’exact-raise’: tries to solve the system using matrix inversion. Raises an error if the matrix has rank
strictly less than <cite>n_classes</cite>.</p></li>
<li><p>’exact-cc’: if the matrix is not of full rank, returns <cite>p_c</cite> as the estimates, which corresponds to
no adjustment (i.e., the classify and count method. See <aclass="reference internal"href="#quapy.method.aggregative.CC"title="quapy.method.aggregative.CC"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.method.aggregative.CC</span></code></a>)</p></li>
<li><p>’minimize’: minimizes the L2 norm of <spanclass="math notranslate nohighlight">\(|Ax-B|\)</span>. This one generally works better, and is the
default parameter. More details about this can be consulted in <aclass="reference external"href="https://lq-2022.github.io/proceedings/CompleteVolume.pdf">Bunse, M. “On Multi-Class Extensions of
Adjusted Classify and Count”, on proceedings of the 2nd International Workshop on Learning to Quantify:
Methods and Applications (LQ 2022), ECML/PKDD 2022, Grenoble (France)</a>.</p></li>
<spanclass="sig-name descname"><spanclass="pre">METHODS</span></span><emclass="property"><spanclass="w"></span><spanclass="p"><spanclass="pre">=</span></span><spanclass="w"></span><spanclass="pre">['inversion',</span><spanclass="pre">'invariant-ratio']</span></em><aclass="headerlink"href="#quapy.method.aggregative.ACC.METHODS"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">NORMALIZATIONS</span></span><emclass="property"><spanclass="w"></span><spanclass="p"><spanclass="pre">=</span></span><spanclass="w"></span><spanclass="pre">['clip',</span><spanclass="pre">'mapsimplex',</span><spanclass="pre">'condsoftmax',</span><spanclass="pre">None]</span></em><aclass="headerlink"href="#quapy.method.aggregative.ACC.NORMALIZATIONS"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">SOLVERS</span></span><emclass="property"><spanclass="w"></span><spanclass="p"><spanclass="pre">=</span></span><spanclass="w"></span><spanclass="pre">['exact',</span><spanclass="pre">'minimize',</span><spanclass="pre">'exact-raise',</span><spanclass="pre">'exact-cc']</span></em><aclass="headerlink"href="#quapy.method.aggregative.ACC.SOLVERS"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">aggregate</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classif_predictions</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#ACC.aggregate"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.ACC.aggregate"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">aggregation_fit</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classif_predictions</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">data</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#ACC.aggregation_fit"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.ACC.aggregation_fit"title="Permalink to this definition"></a></dt>
<li><p><strong>classif_predictions</strong>– a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> containing,
as instances, the label predictions issued by the classifier and, as labels, the true labels</p></li>
<li><p><strong>data</strong>– a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
<emclass="property"><spanclass="pre">classmethod</span><spanclass="w"></span></em><spanclass="sig-name descname"><spanclass="pre">getPteCondEstim</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classes</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">y</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">y_</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#ACC.getPteCondEstim"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.ACC.getPteCondEstim"title="Permalink to this definition"></a></dt>
<dd><p>Estimate the matrix with entry (i,j) being the estimate of P(hat_yi|yj), that is, the probability that a
document that belongs to yj ends up being classified as belonging to yi</p>
<emclass="property"><spanclass="pre">classmethod</span><spanclass="w"></span></em><spanclass="sig-name descname"><spanclass="pre">newInvariantRatioEstimation</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classifier</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><spanclass="pre">BaseEstimator</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">val_split</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">5</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">n_jobs</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">None</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#ACC.newInvariantRatioEstimation"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.ACC.newInvariantRatioEstimation"title="Permalink to this definition"></a></dt>
<dd><p>Constructs a quantifier that implements the Invariant Ratio Estimator of
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.method.aggregative.</span></span><spanclass="sig-name descname"><spanclass="pre">AdjustedClassifyAndCount</span></span><aclass="headerlink"href="#quapy.method.aggregative.AdjustedClassifyAndCount"title="Permalink to this definition"></a></dt>
<emclass="property"><spanclass="pre">class</span><spanclass="w"></span></em><spanclass="sig-prename descclassname"><spanclass="pre">quapy.method.aggregative.</span></span><spanclass="sig-name descname"><spanclass="pre">AggregativeCrispQuantifier</span></span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#AggregativeCrispQuantifier"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.AggregativeCrispQuantifier"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">fit</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">training</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em>, <emclass="sig-param"><spanclass="o"><spanclass="pre">**</span></span><spanclass="n"><spanclass="pre">kwargs</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#AggregativeMedianEstimator.fit"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.AggregativeMedianEstimator.fit"title="Permalink to this definition"></a></dt>
<ddclass="field-odd"><p><strong>data</strong>– a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p>
<spanclass="sig-name descname"><spanclass="pre">get_params</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">deep</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">True</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#AggregativeMedianEstimator.get_params"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.AggregativeMedianEstimator.get_params"title="Permalink to this definition"></a></dt>
<ddclass="field-odd"><p><strong>deep</strong> (<em>bool</em><em>, </em><em>default=True</em>) – If True, will return the parameters for this estimator and
<spanclass="sig-name descname"><spanclass="pre">quantify</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">instances</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#AggregativeMedianEstimator.quantify"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.AggregativeMedianEstimator.quantify"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">set_params</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="o"><spanclass="pre">**</span></span><spanclass="n"><spanclass="pre">params</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#AggregativeMedianEstimator.set_params"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.AggregativeMedianEstimator.set_params"title="Permalink to this definition"></a></dt>
<emclass="property"><spanclass="pre">class</span><spanclass="w"></span></em><spanclass="sig-prename descclassname"><spanclass="pre">quapy.method.aggregative.</span></span><spanclass="sig-name descname"><spanclass="pre">AggregativeQuantifier</span></span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#AggregativeQuantifier"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.AggregativeQuantifier"title="Permalink to this definition"></a></dt>
results. Aggregative quantifiers implement a pipeline that consists of generating classification predictions
and aggregating them. For this reason, the training phase is implemented by <codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">classification_fit()</span></code> followed
by <aclass="reference internal"href="#quapy.method.aggregative.AggregativeQuantifier.aggregation_fit"title="quapy.method.aggregative.AggregativeQuantifier.aggregation_fit"><codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">aggregation_fit()</span></code></a>, while the testing phase is implemented by <aclass="reference internal"href="#quapy.method.aggregative.AggregativeQuantifier.classify"title="quapy.method.aggregative.AggregativeQuantifier.classify"><codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">classify()</span></code></a> followed by
<aclass="reference internal"href="#quapy.method.aggregative.AggregativeQuantifier.aggregate"title="quapy.method.aggregative.AggregativeQuantifier.aggregate"><codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">aggregate()</span></code></a>. Subclasses of this abstract class must provide implementations for these methods.
Aggregative quantifiers also maintain a <aclass="reference internal"href="#quapy.method.aggregative.AggregativeQuantifier.classifier"title="quapy.method.aggregative.AggregativeQuantifier.classifier"><codeclass="xref py py-attr docutils literal notranslate"><spanclass="pre">classifier</span></code></a> attribute.</p>
<p>The method <aclass="reference internal"href="#quapy.method.aggregative.AggregativeQuantifier.fit"title="quapy.method.aggregative.AggregativeQuantifier.fit"><codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">fit()</span></code></a> comes with a default implementation based on <codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">classification_fit()</span></code>
and <aclass="reference internal"href="#quapy.method.aggregative.AggregativeQuantifier.aggregation_fit"title="quapy.method.aggregative.AggregativeQuantifier.aggregation_fit"><codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">aggregation_fit()</span></code></a>.</p>
<p>The method <aclass="reference internal"href="#quapy.method.aggregative.AggregativeQuantifier.quantify"title="quapy.method.aggregative.AggregativeQuantifier.quantify"><codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">quantify()</span></code></a> comes with a default implementation based on <aclass="reference internal"href="#quapy.method.aggregative.AggregativeQuantifier.classify"title="quapy.method.aggregative.AggregativeQuantifier.classify"><codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">classify()</span></code></a>
and <aclass="reference internal"href="#quapy.method.aggregative.AggregativeQuantifier.aggregate"title="quapy.method.aggregative.AggregativeQuantifier.aggregate"><codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">aggregate()</span></code></a>.</p>
<emclass="property"><spanclass="pre">abstract</span><spanclass="w"></span></em><spanclass="sig-name descname"><spanclass="pre">aggregate</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classif_predictions</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><spanclass="pre">ndarray</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#AggregativeQuantifier.aggregate"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.AggregativeQuantifier.aggregate"title="Permalink to this definition"></a></dt>
<emclass="property"><spanclass="pre">abstract</span><spanclass="w"></span></em><spanclass="sig-name descname"><spanclass="pre">aggregation_fit</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classif_predictions</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">data</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#AggregativeQuantifier.aggregation_fit"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.AggregativeQuantifier.aggregation_fit"title="Permalink to this definition"></a></dt>
<li><p><strong>data</strong>– a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
<emclass="property"><spanclass="pre">property</span><spanclass="w"></span></em><spanclass="sig-name descname"><spanclass="pre">classes_</span></span><aclass="headerlink"href="#quapy.method.aggregative.AggregativeQuantifier.classes_"title="Permalink to this definition"></a></dt>
<emclass="property"><spanclass="pre">property</span><spanclass="w"></span></em><spanclass="sig-name descname"><spanclass="pre">classifier</span></span><aclass="headerlink"href="#quapy.method.aggregative.AggregativeQuantifier.classifier"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">classifier_fit_predict</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">data</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">fit_classifier</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">True</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">predict_on</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">None</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#AggregativeQuantifier.classifier_fit_predict"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.AggregativeQuantifier.classifier_fit_predict"title="Permalink to this definition"></a></dt>
<li><p><strong>data</strong>– a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
<li><p><strong>fit_classifier</strong>– whether to train the learner (default is True). Set to False if the
learner has been trained outside the quantifier.</p></li>
<li><p><strong>predict_on</strong>– specifies the set on which predictions need to be issued. This parameter can
be specified as None (default) to indicate no prediction is needed; a float in (0, 1) to
indicate the proportion of instances to be used for predictions (the remainder is used for
training); an integer >1 to indicate that the predictions must be generated via k-fold
cross-validation, using this integer as k; or the data sample itself on which to generate
<spanclass="sig-name descname"><spanclass="pre">classify</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">instances</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#AggregativeQuantifier.classify"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.AggregativeQuantifier.classify"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">fit</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">data</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">fit_classifier</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">True</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">val_split</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">None</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#AggregativeQuantifier.fit"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.AggregativeQuantifier.fit"title="Permalink to this definition"></a></dt>
<li><p><strong>data</strong>– a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
<spanclass="sig-name descname"><spanclass="pre">quantify</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">instances</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#AggregativeQuantifier.quantify"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.AggregativeQuantifier.quantify"title="Permalink to this definition"></a></dt>
<emclass="property"><spanclass="pre">property</span><spanclass="w"></span></em><spanclass="sig-name descname"><spanclass="pre">val_split</span></span><aclass="headerlink"href="#quapy.method.aggregative.AggregativeQuantifier.val_split"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">val_split_</span></span><emclass="property"><spanclass="w"></span><spanclass="p"><spanclass="pre">=</span></span><spanclass="w"></span><spanclass="pre">None</span></em><aclass="headerlink"href="#quapy.method.aggregative.AggregativeQuantifier.val_split_"title="Permalink to this definition"></a></dt>
<emclass="property"><spanclass="pre">class</span><spanclass="w"></span></em><spanclass="sig-prename descclassname"><spanclass="pre">quapy.method.aggregative.</span></span><spanclass="sig-name descname"><spanclass="pre">AggregativeSoftQuantifier</span></span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#AggregativeSoftQuantifier"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.AggregativeSoftQuantifier"title="Permalink to this definition"></a></dt>
which is a variant of <aclass="reference internal"href="#quapy.method.aggregative.ACC"title="quapy.method.aggregative.ACC"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">ACC</span></code></a> that calculates the posterior probability distribution
over the prevalence vectors, rather than providing a point estimate obtained
by matrix inversion.</p>
<p>Can be used to diagnose degeneracy in the predictions visible when the confusion
matrix has high condition number or to quantify uncertainty around the point estimate.</p>
<p>This method relies on extra dependencies, which have to be installed via:
<spanclass="sig-name descname"><spanclass="pre">aggregate</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classif_predictions</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#BayesianCC.aggregate"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.BayesianCC.aggregate"title="Permalink to this definition"></a></dt>
<dd><p>Implements the aggregation of label predictions.</p>
<spanclass="sig-name descname"><spanclass="pre">aggregation_fit</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classif_predictions</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">data</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#BayesianCC.aggregation_fit"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.BayesianCC.aggregation_fit"title="Permalink to this definition"></a></dt>
<li><p><strong>classif_predictions</strong>– a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> containing,
as instances, the label predictions issued by the classifier and, as labels, the true labels</p></li>
<li><p><strong>data</strong>– a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
<spanclass="sig-name descname"><spanclass="pre">get_conditional_probability_samples</span></span><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#BayesianCC.get_conditional_probability_samples"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.BayesianCC.get_conditional_probability_samples"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">get_prevalence_samples</span></span><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#BayesianCC.get_prevalence_samples"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.BayesianCC.get_prevalence_samples"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">sample_from_posterior</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classif_predictions</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#BayesianCC.sample_from_posterior"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.BayesianCC.sample_from_posterior"title="Permalink to this definition"></a></dt>
<emclass="property"><spanclass="pre">class</span><spanclass="w"></span></em><spanclass="sig-prename descclassname"><spanclass="pre">quapy.method.aggregative.</span></span><spanclass="sig-name descname"><spanclass="pre">BinaryAggregativeQuantifier</span></span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#BinaryAggregativeQuantifier"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.BinaryAggregativeQuantifier"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">fit</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">data</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">fit_classifier</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">True</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">val_split</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">None</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#BinaryAggregativeQuantifier.fit"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.BinaryAggregativeQuantifier.fit"title="Permalink to this definition"></a></dt>
<li><p><strong>data</strong>– a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
<li><p><strong>fit_classifier</strong>– whether to train the learner (default is True). Set to False if the
learner has been trained outside the quantifier.</p></li>
<emclass="property"><spanclass="pre">property</span><spanclass="w"></span></em><spanclass="sig-name descname"><spanclass="pre">neg_label</span></span><aclass="headerlink"href="#quapy.method.aggregative.BinaryAggregativeQuantifier.neg_label"title="Permalink to this definition"></a></dt>
<emclass="property"><spanclass="pre">property</span><spanclass="w"></span></em><spanclass="sig-name descname"><spanclass="pre">pos_label</span></span><aclass="headerlink"href="#quapy.method.aggregative.BinaryAggregativeQuantifier.pos_label"title="Permalink to this definition"></a></dt>
<emclass="property"><spanclass="pre">class</span><spanclass="w"></span></em><spanclass="sig-prename descclassname"><spanclass="pre">quapy.method.aggregative.</span></span><spanclass="sig-name descname"><spanclass="pre">CC</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classifier</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><spanclass="pre">BaseEstimator</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#CC"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.CC"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">aggregate</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classif_predictions</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><spanclass="pre">ndarray</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#CC.aggregate"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.CC.aggregate"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">aggregation_fit</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classif_predictions</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">data</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#CC.aggregation_fit"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.CC.aggregation_fit"title="Permalink to this definition"></a></dt>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.method.aggregative.</span></span><spanclass="sig-name descname"><spanclass="pre">ClassifyAndCount</span></span><aclass="headerlink"href="#quapy.method.aggregative.ClassifyAndCount"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">aggregate</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">posteriors</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><spanclass="pre">ndarray</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#DMy.aggregate"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.DMy.aggregate"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">aggregation_fit</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classif_predictions</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">data</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#DMy.aggregation_fit"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.DMy.aggregation_fit"title="Permalink to this definition"></a></dt>
<dd><p>Trains the aggregation function of a distribution matching method. This comes down to generating the
validation distributions out of the training data.
<li><p><strong>classif_predictions</strong>– a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> containing,
as instances, the posterior probabilities issued by the classifier and, as labels, the true labels</p></li>
<li><p><strong>data</strong>– a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.method.aggregative.</span></span><spanclass="sig-name descname"><spanclass="pre">DistributionMatchingY</span></span><aclass="headerlink"href="#quapy.method.aggregative.DistributionMatchingY"title="Permalink to this definition"></a></dt>
DyS is a generalization of HDy method, using a Ternary Search in order to find the prevalence that
minimizes the distance between distributions.
Details for the ternary search have been got from <<aclass="reference external"href="https://dl.acm.org/doi/pdf/10.1145/3219819.3220059">https://dl.acm.org/doi/pdf/10.1145/3219819.3220059</a>></p>
validation distribution, or a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> (the split itself), or an integer indicating the number of folds (default 5)..</p></li>
<spanclass="sig-name descname"><spanclass="pre">aggregate</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classif_posteriors</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#DyS.aggregate"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.DyS.aggregate"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">aggregation_fit</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classif_predictions</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">data</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#DyS.aggregation_fit"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.DyS.aggregation_fit"title="Permalink to this definition"></a></dt>
<dd><p>Trains the aggregation function of DyS.</p>
<li><p><strong>data</strong>– a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
<p>This implementation also gives access to the heuristics proposed by <aclass="reference external"href="http://proceedings.mlr.press/v119/alexandari20a.html">Alexandari et al. paper</a>. These heuristics consist of using, as the training
prevalence, an estimate of it obtained via k-fold cross validation (instead of the true training prevalence),
and to recalibrate the posterior probabilities of the classifier.</p>
<emclass="property"><spanclass="pre">classmethod</span><spanclass="w"></span></em><spanclass="sig-name descname"><spanclass="pre">EM</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">tr_prev</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">posterior_probabilities</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">epsilon</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">0.0001</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#EMQ.EM"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.EMQ.EM"title="Permalink to this definition"></a></dt>
<emclass="property"><spanclass="pre">classmethod</span><spanclass="w"></span></em><spanclass="sig-name descname"><spanclass="pre">EMQ_BCTS</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classifier</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><spanclass="pre">BaseEstimator</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">n_jobs</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">None</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#EMQ.EMQ_BCTS"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.EMQ.EMQ_BCTS"title="Permalink to this definition"></a></dt>
<dd><p>Constructs an instance of EMQ using the best configuration found in the <aclass="reference external"href="http://proceedings.mlr.press/v119/alexandari20a.html">Alexandari et al. paper</a>, i.e., one that relies on Bias-Corrected Temperature
Scaling (BCTS) as a recalibration function, and that uses an estimate of the training prevalence instead of
<spanclass="sig-name descname"><spanclass="pre">EPSILON</span></span><emclass="property"><spanclass="w"></span><spanclass="p"><spanclass="pre">=</span></span><spanclass="w"></span><spanclass="pre">0.0001</span></em><aclass="headerlink"href="#quapy.method.aggregative.EMQ.EPSILON"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">MAX_ITER</span></span><emclass="property"><spanclass="w"></span><spanclass="p"><spanclass="pre">=</span></span><spanclass="w"></span><spanclass="pre">1000</span></em><aclass="headerlink"href="#quapy.method.aggregative.EMQ.MAX_ITER"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">aggregate</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classif_posteriors</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">epsilon</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">0.0001</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#EMQ.aggregate"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.EMQ.aggregate"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">aggregation_fit</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classif_predictions</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">data</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#EMQ.aggregation_fit"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.EMQ.aggregation_fit"title="Permalink to this definition"></a></dt>
<dd><p>Trains the aggregation function of EMQ. This comes down to recalibrating the posterior probabilities
<li><p><strong>data</strong>– a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
<spanclass="sig-name descname"><spanclass="pre">classify</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">instances</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#EMQ.classify"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.EMQ.classify"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">predict_proba</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">instances</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">epsilon</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">0.0001</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#EMQ.predict_proba"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.EMQ.predict_proba"title="Permalink to this definition"></a></dt>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.method.aggregative.</span></span><spanclass="sig-name descname"><spanclass="pre">ExpectationMaximizationQuantifier</span></span><aclass="headerlink"href="#quapy.method.aggregative.ExpectationMaximizationQuantifier"title="Permalink to this definition"></a></dt>
<emclass="property"><spanclass="pre">class</span><spanclass="w"></span></em><spanclass="sig-prename descclassname"><spanclass="pre">quapy.method.aggregative.</span></span><spanclass="sig-name descname"><spanclass="pre">HDy</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classifier</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><spanclass="pre">BaseEstimator</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">val_split</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">5</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#HDy"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.HDy"title="Permalink to this definition"></a></dt>
validation distribution, or a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> (the split itself), or an integer indicating the number of folds (default 5)..</p></li>
<spanclass="sig-name descname"><spanclass="pre">aggregate</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classif_posteriors</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#HDy.aggregate"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.HDy.aggregate"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">aggregation_fit</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classif_predictions</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">data</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#HDy.aggregation_fit"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.HDy.aggregation_fit"title="Permalink to this definition"></a></dt>
<dd><p>Trains the aggregation function of HDy.</p>
<li><p><strong>classif_predictions</strong>– a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> containing,
as instances, the posterior probabilities issued by the classifier and, as labels, the true labels</p></li>
<li><p><strong>data</strong>– a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.method.aggregative.</span></span><spanclass="sig-name descname"><spanclass="pre">HellingerDistanceY</span></span><aclass="headerlink"href="#quapy.method.aggregative.HellingerDistanceY"title="Permalink to this definition"></a></dt>
<emclass="property"><spanclass="pre">class</span><spanclass="w"></span></em><spanclass="sig-prename descclassname"><spanclass="pre">quapy.method.aggregative.</span></span><spanclass="sig-name descname"><spanclass="pre">OneVsAllAggregative</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">binary_quantifier</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">n_jobs</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">None</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">parallel_backend</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">'multiprocessing'</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#OneVsAllAggregative"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.OneVsAllAggregative"title="Permalink to this definition"></a></dt>
<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 <aclass="reference internal"href="#quapy.method.aggregative.EMQ"title="quapy.method.aggregative.EMQ"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">EMQ</span></code></a> quantifier, in
<aclass="reference external"href="https://link.springer.com/content/pdf/10.1007/s13278-016-0327-z.pdf">Gao and Sebastiani, 2016</a>.</p>
<spanclass="sig-name descname"><spanclass="pre">aggregate</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classif_predictions</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#OneVsAllAggregative.aggregate"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.OneVsAllAggregative.aggregate"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">classify</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">instances</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#OneVsAllAggregative.classify"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.OneVsAllAggregative.classify"title="Permalink to this definition"></a></dt>
<li><p>’exact-raise’: tries to solve the system using matrix inversion.
Raises an error if the matrix has rank strictly less than <cite>n_classes</cite>.</p></li>
<li><p>’exact-cc’: if the matrix is not of full rank, returns <cite>p_c</cite> as the estimates, which
corresponds to no adjustment (i.e., the classify and count method. See <aclass="reference internal"href="#quapy.method.aggregative.CC"title="quapy.method.aggregative.CC"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.method.aggregative.CC</span></code></a>)</p></li>
<li><p>’minimize’: minimizes the L2 norm of <spanclass="math notranslate nohighlight">\(|Ax-B|\)</span>. This one generally works better, and is the
default parameter. More details about this can be consulted in <aclass="reference external"href="https://lq-2022.github.io/proceedings/CompleteVolume.pdf">Bunse, M. “On Multi-Class Extensions
of Adjusted Classify and Count”, on proceedings of the 2nd International Workshop on Learning to
Quantify: Methods and Applications (LQ 2022), ECML/PKDD 2022, Grenoble (France)</a>.</p></li>
<spanclass="sig-name descname"><spanclass="pre">aggregate</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classif_posteriors</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#PACC.aggregate"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.PACC.aggregate"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">aggregation_fit</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classif_predictions</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">data</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#PACC.aggregation_fit"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.PACC.aggregation_fit"title="Permalink to this definition"></a></dt>
<li><p><strong>classif_predictions</strong>– a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> containing,
as instances, the posterior probabilities issued by the classifier and, as labels, the true labels</p></li>
<li><p><strong>data</strong>– a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
<emclass="property"><spanclass="pre">classmethod</span><spanclass="w"></span></em><spanclass="sig-name descname"><spanclass="pre">getPteCondEstim</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classes</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">y</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">y_</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#PACC.getPteCondEstim"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.PACC.getPteCondEstim"title="Permalink to this definition"></a></dt>
<emclass="property"><spanclass="pre">class</span><spanclass="w"></span></em><spanclass="sig-prename descclassname"><spanclass="pre">quapy.method.aggregative.</span></span><spanclass="sig-name descname"><spanclass="pre">PCC</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classifier</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><spanclass="pre">BaseEstimator</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#PCC"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.PCC"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">aggregate</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classif_posteriors</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#PCC.aggregate"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.PCC.aggregate"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">aggregation_fit</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classif_predictions</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">data</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#PCC.aggregation_fit"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.PCC.aggregation_fit"title="Permalink to this definition"></a></dt>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.method.aggregative.</span></span><spanclass="sig-name descname"><spanclass="pre">ProbabilisticAdjustedClassifyAndCount</span></span><aclass="headerlink"href="#quapy.method.aggregative.ProbabilisticAdjustedClassifyAndCount"title="Permalink to this definition"></a></dt>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.method.aggregative.</span></span><spanclass="sig-name descname"><spanclass="pre">ProbabilisticClassifyAndCount</span></span><aclass="headerlink"href="#quapy.method.aggregative.ProbabilisticClassifyAndCount"title="Permalink to this definition"></a></dt>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.method.aggregative.</span></span><spanclass="sig-name descname"><spanclass="pre">SLD</span></span><aclass="headerlink"href="#quapy.method.aggregative.SLD"title="Permalink to this definition"></a></dt>
<emclass="property"><spanclass="pre">class</span><spanclass="w"></span></em><spanclass="sig-prename descclassname"><spanclass="pre">quapy.method.aggregative.</span></span><spanclass="sig-name descname"><spanclass="pre">SMM</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classifier</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><spanclass="pre">BaseEstimator</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">val_split</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">5</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#SMM"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.SMM"title="Permalink to this definition"></a></dt>
validation distribution, or a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> (the split itself), or an integer indicating the number of folds (default 5)..</p></li>
<spanclass="sig-name descname"><spanclass="pre">aggregate</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classif_posteriors</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#SMM.aggregate"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.SMM.aggregate"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">aggregation_fit</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classif_predictions</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">data</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#SMM.aggregation_fit"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.SMM.aggregation_fit"title="Permalink to this definition"></a></dt>
<dd><p>Trains the aggregation function of SMM.</p>
<li><p><strong>data</strong>– a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.method.aggregative.</span></span><spanclass="sig-name descname"><spanclass="pre">newELM</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">svmperf_base</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">None</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">loss</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">'01'</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">C</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">1</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#newELM"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.newELM"title="Permalink to this definition"></a></dt>
<li><p><strong>svmperf_base</strong>– path to the folder containing the binary files of <cite>SVM perf</cite>; if set to None (default)
this path will be obtained from qp.environ[‘SVMPERF_HOME’]</p></li>
<li><p><strong>loss</strong>– the loss to optimize (see <aclass="reference internal"href="quapy.classification.html#quapy.classification.svmperf.SVMperf.valid_losses"title="quapy.classification.svmperf.SVMperf.valid_losses"><codeclass="xref py py-attr docutils literal notranslate"><spanclass="pre">quapy.classification.svmperf.SVMperf.valid_losses</span></code></a>)</p></li>
<li><p><strong>C</strong>– trade-off between training error and margin (default 0.01)</p></li>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.method.aggregative.</span></span><spanclass="sig-name descname"><spanclass="pre">newSVMAE</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">svmperf_base</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">None</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">C</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">1</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#newSVMAE"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.newSVMAE"title="Permalink to this definition"></a></dt>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.method.aggregative.</span></span><spanclass="sig-name descname"><spanclass="pre">newSVMKLD</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">svmperf_base</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">None</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">C</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">1</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#newSVMKLD"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.newSVMKLD"title="Permalink to this definition"></a></dt>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.method.aggregative.</span></span><spanclass="sig-name descname"><spanclass="pre">newSVMQ</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">svmperf_base</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">None</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">C</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">1</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#newSVMQ"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.newSVMQ"title="Permalink to this definition"></a></dt>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.method.aggregative.</span></span><spanclass="sig-name descname"><spanclass="pre">newSVMRAE</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">svmperf_base</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">None</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">C</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">1</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/aggregative.html#newSVMRAE"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.aggregative.newSVMRAE"title="Permalink to this definition"></a></dt>
<emclass="property"><spanclass="pre">class</span><spanclass="w"></span></em><spanclass="sig-prename descclassname"><spanclass="pre">quapy.method._kdey.</span></span><spanclass="sig-name descname"><spanclass="pre">KDEBase</span></span><aclass="reference internal"href="_modules/quapy/method/_kdey.html#KDEBase"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._kdey.KDEBase"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">BANDWIDTH_METHOD</span></span><emclass="property"><spanclass="w"></span><spanclass="p"><spanclass="pre">=</span></span><spanclass="w"></span><spanclass="pre">['scott',</span><spanclass="pre">'silverman']</span></em><aclass="headerlink"href="#quapy.method._kdey.KDEBase.BANDWIDTH_METHOD"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">get_kde_function</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">X</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">bandwidth</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/_kdey.html#KDEBase.get_kde_function"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._kdey.KDEBase.get_kde_function"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">get_mixture_components</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">X</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">y</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">classes</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">bandwidth</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/_kdey.html#KDEBase.get_mixture_components"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._kdey.KDEBase.get_mixture_components"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">pdf</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">kde</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">X</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/_kdey.html#KDEBase.pdf"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._kdey.KDEBase.pdf"title="Permalink to this definition"></a></dt>
<emclass="property"><spanclass="pre">class</span><spanclass="w"></span></em><spanclass="sig-prename descclassname"><spanclass="pre">quapy.method._kdey.</span></span><spanclass="sig-name descname"><spanclass="pre">KDEyCS</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classifier</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><spanclass="pre">BaseEstimator</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">val_split</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">10</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">bandwidth</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">0.1</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">n_jobs</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">None</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/_kdey.html#KDEyCS"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._kdey.KDEyCS"title="Permalink to this definition"></a></dt>
<p>where <spanclass="math notranslate nohighlight">\(p_{\alpha}\)</span> is the mixture of class-specific KDEs with mixture parameter (hence class prevalence)
<spanclass="math notranslate nohighlight">\(\alpha\)</span> defined by</p>
<spanclass="sig-name descname"><spanclass="pre">aggregate</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">posteriors</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><spanclass="pre">ndarray</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/_kdey.html#KDEyCS.aggregate"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._kdey.KDEyCS.aggregate"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">aggregation_fit</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classif_predictions</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">data</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/_kdey.html#KDEyCS.aggregation_fit"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._kdey.KDEyCS.aggregation_fit"title="Permalink to this definition"></a></dt>
<li><p><strong>data</strong>– a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
<spanclass="sig-name descname"><spanclass="pre">gram_matrix_mix_sum</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">X</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">Y</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">None</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/_kdey.html#KDEyCS.gram_matrix_mix_sum"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._kdey.KDEyCS.gram_matrix_mix_sum"title="Permalink to this definition"></a></dt>
<p>where <spanclass="math notranslate nohighlight">\(p_{\alpha}\)</span> is the mixture of class-specific KDEs with mixture parameter (hence class prevalence)
<spanclass="math notranslate nohighlight">\(\alpha\)</span> defined by</p>
<p>where the datapoints (trials) <spanclass="math notranslate nohighlight">\(x_1,\ldots,x_t\sim_{\mathrm{iid}} r\)</span> with <spanclass="math notranslate nohighlight">\(r\)</span> the
<spanclass="sig-name descname"><spanclass="pre">aggregate</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">posteriors</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><spanclass="pre">ndarray</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/_kdey.html#KDEyHD.aggregate"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._kdey.KDEyHD.aggregate"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">aggregation_fit</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classif_predictions</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">data</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/_kdey.html#KDEyHD.aggregation_fit"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._kdey.KDEyHD.aggregation_fit"title="Permalink to this definition"></a></dt>
<li><p><strong>data</strong>– a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
<p>where <spanclass="math notranslate nohighlight">\(p_{\alpha}\)</span> is the mixture of class-specific KDEs with mixture parameter (hence class prevalence)
<spanclass="math notranslate nohighlight">\(\alpha\)</span> defined by</p>
<spanclass="sig-name descname"><spanclass="pre">aggregate</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">posteriors</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><spanclass="pre">ndarray</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/_kdey.html#KDEyML.aggregate"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._kdey.KDEyML.aggregate"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">aggregation_fit</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classif_predictions</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">data</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/_kdey.html#KDEyML.aggregation_fit"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._kdey.KDEyML.aggregation_fit"title="Permalink to this definition"></a></dt>
<li><p><strong>data</strong>– a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
<p>Implements the <aclass="reference external"href="https://dl.acm.org/doi/abs/10.1145/3269206.3269287">QuaNet</a> forward pass.
See <aclass="reference internal"href="#quapy.method._neural.QuaNetTrainer"title="quapy.method._neural.QuaNetTrainer"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">QuaNetTrainer</span></code></a> for training QuaNet.</p>
<emclass="property"><spanclass="pre">property</span><spanclass="w"></span></em><spanclass="sig-name descname"><spanclass="pre">device</span></span><aclass="headerlink"href="#quapy.method._neural.QuaNetModule.device"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">forward</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">doc_embeddings</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">doc_posteriors</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">statistics</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/_neural.html#QuaNetModule.forward"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="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>
<divclass="admonition note">
<pclass="admonition-title">Note</p>
<p>Although the recipe for forward pass needs to be defined within
this function, one should call the <codeclass="xref py py-class docutils literal notranslate"><spanclass="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>
<spanclass="sig-name descname"><spanclass="pre">training</span></span><emclass="property"><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="pre">bool</span></em><aclass="headerlink"href="#quapy.method._neural.QuaNetModule.training"title="Permalink to this definition"></a></dt>
<emclass="property"><spanclass="pre">property</span><spanclass="w"></span></em><spanclass="sig-name descname"><spanclass="pre">classes_</span></span><aclass="headerlink"href="#quapy.method._neural.QuaNetTrainer.classes_"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">clean_checkpoint</span></span><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/_neural.html#QuaNetTrainer.clean_checkpoint"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._neural.QuaNetTrainer.clean_checkpoint"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">clean_checkpoint_dir</span></span><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/_neural.html#QuaNetTrainer.clean_checkpoint_dir"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._neural.QuaNetTrainer.clean_checkpoint_dir"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">fit</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">data</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">fit_classifier</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">True</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/_neural.html#QuaNetTrainer.fit"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._neural.QuaNetTrainer.fit"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">get_params</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">deep</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">True</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/_neural.html#QuaNetTrainer.get_params"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._neural.QuaNetTrainer.get_params"title="Permalink to this definition"></a></dt>
<ddclass="field-odd"><p><strong>deep</strong> (<em>bool</em><em>, </em><em>default=True</em>) – If True, will return the parameters for this estimator and
<spanclass="sig-name descname"><spanclass="pre">quantify</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">instances</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/_neural.html#QuaNetTrainer.quantify"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._neural.QuaNetTrainer.quantify"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">set_params</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="o"><spanclass="pre">**</span></span><spanclass="n"><spanclass="pre">parameters</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/_neural.html#QuaNetTrainer.set_params"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._neural.QuaNetTrainer.set_params"title="Permalink to this definition"></a></dt>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.method._neural.</span></span><spanclass="sig-name descname"><spanclass="pre">mae_loss</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">output</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">target</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/_neural.html#mae_loss"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._neural.mae_loss"title="Permalink to this definition"></a></dt>
<emclass="property"><spanclass="pre">class</span><spanclass="w"></span></em><spanclass="sig-prename descclassname"><spanclass="pre">quapy.method._threshold_optim.</span></span><spanclass="sig-name descname"><spanclass="pre">MAX</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classifier</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><spanclass="pre">BaseEstimator</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">val_split</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">5</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/_threshold_optim.html#MAX"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._threshold_optim.MAX"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">condition</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">tpr</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">fpr</span></span></em><spanclass="sig-paren">)</span><spanclass="sig-return"><spanclass="sig-return-icon">→</span><spanclass="sig-return-typehint"><spanclass="pre">float</span></span></span><aclass="reference internal"href="_modules/quapy/method/_threshold_optim.html#MAX.condition"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._threshold_optim.MAX.condition"title="Permalink to this definition"></a></dt>
<emclass="property"><spanclass="pre">class</span><spanclass="w"></span></em><spanclass="sig-prename descclassname"><spanclass="pre">quapy.method._threshold_optim.</span></span><spanclass="sig-name descname"><spanclass="pre">MS</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classifier</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><spanclass="pre">BaseEstimator</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">val_split</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">5</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/_threshold_optim.html#MS"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._threshold_optim.MS"title="Permalink to this definition"></a></dt>
<p>Median Sweep. Threshold Optimization variant for <codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">ACC</span></code> as proposed by
<aclass="reference external"href="https://dl.acm.org/doi/abs/10.1145/1150402.1150423">Forman 2006</a> and
<aclass="reference external"href="https://link.springer.com/article/10.1007/s10618-008-0097-y">Forman 2008</a> that generates
class prevalence estimates for all decision thresholds and returns the median of them all.
The goal is to bring improved stability to the denominator of the adjustment.</p>
<spanclass="sig-name descname"><spanclass="pre">aggregate</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classif_predictions</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><spanclass="pre">ndarray</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/_threshold_optim.html#MS.aggregate"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._threshold_optim.MS.aggregate"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">aggregation_fit</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classif_predictions</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">data</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/_threshold_optim.html#MS.aggregation_fit"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._threshold_optim.MS.aggregation_fit"title="Permalink to this definition"></a></dt>
<li><p><strong>data</strong>– a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
<spanclass="sig-name descname"><spanclass="pre">condition</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">tpr</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">fpr</span></span></em><spanclass="sig-paren">)</span><spanclass="sig-return"><spanclass="sig-return-icon">→</span><spanclass="sig-return-typehint"><spanclass="pre">float</span></span></span><aclass="reference internal"href="_modules/quapy/method/_threshold_optim.html#MS.condition"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._threshold_optim.MS.condition"title="Permalink to this definition"></a></dt>
<emclass="property"><spanclass="pre">class</span><spanclass="w"></span></em><spanclass="sig-prename descclassname"><spanclass="pre">quapy.method._threshold_optim.</span></span><spanclass="sig-name descname"><spanclass="pre">MS2</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classifier</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><spanclass="pre">BaseEstimator</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">val_split</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">5</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/_threshold_optim.html#MS2"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._threshold_optim.MS2"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">discard</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">tpr</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">fpr</span></span></em><spanclass="sig-paren">)</span><spanclass="sig-return"><spanclass="sig-return-icon">→</span><spanclass="sig-return-typehint"><spanclass="pre">bool</span></span></span><aclass="reference internal"href="_modules/quapy/method/_threshold_optim.html#MS2.discard"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._threshold_optim.MS2.discard"title="Permalink to this definition"></a></dt>
<emclass="property"><spanclass="pre">class</span><spanclass="w"></span></em><spanclass="sig-prename descclassname"><spanclass="pre">quapy.method._threshold_optim.</span></span><spanclass="sig-name descname"><spanclass="pre">T50</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classifier</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><spanclass="pre">BaseEstimator</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">val_split</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">5</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/_threshold_optim.html#T50"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._threshold_optim.T50"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">condition</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">tpr</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">fpr</span></span></em><spanclass="sig-paren">)</span><spanclass="sig-return"><spanclass="sig-return-icon">→</span><spanclass="sig-return-typehint"><spanclass="pre">float</span></span></span><aclass="reference internal"href="_modules/quapy/method/_threshold_optim.html#T50.condition"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._threshold_optim.T50.condition"title="Permalink to this definition"></a></dt>
<emclass="property"><spanclass="pre">class</span><spanclass="w"></span></em><spanclass="sig-prename descclassname"><spanclass="pre">quapy.method._threshold_optim.</span></span><spanclass="sig-name descname"><spanclass="pre">ThresholdOptimization</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classifier</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><spanclass="pre">BaseEstimator</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">val_split</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">5</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">n_jobs</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">None</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/_threshold_optim.html#ThresholdOptimization"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._threshold_optim.ThresholdOptimization"title="Permalink to this definition"></a></dt>
<p>Abstract class of Threshold Optimization variants for <codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">ACC</span></code> as proposed by
<aclass="reference external"href="https://dl.acm.org/doi/abs/10.1145/1150402.1150423">Forman 2006</a> and
<spanclass="sig-name descname"><spanclass="pre">aggregate</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classif_predictions</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><spanclass="pre">ndarray</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/_threshold_optim.html#ThresholdOptimization.aggregate"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._threshold_optim.ThresholdOptimization.aggregate"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">aggregate_with_threshold</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classif_predictions</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">tprs</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">fprs</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">thresholds</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/_threshold_optim.html#ThresholdOptimization.aggregate_with_threshold"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._threshold_optim.ThresholdOptimization.aggregate_with_threshold"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">aggregation_fit</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classif_predictions</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">data</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/_threshold_optim.html#ThresholdOptimization.aggregation_fit"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._threshold_optim.ThresholdOptimization.aggregation_fit"title="Permalink to this definition"></a></dt>
<li><p><strong>data</strong>– a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
<emclass="property"><spanclass="pre">abstract</span><spanclass="w"></span></em><spanclass="sig-name descname"><spanclass="pre">condition</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">tpr</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">fpr</span></span></em><spanclass="sig-paren">)</span><spanclass="sig-return"><spanclass="sig-return-icon">→</span><spanclass="sig-return-typehint"><spanclass="pre">float</span></span></span><aclass="reference internal"href="_modules/quapy/method/_threshold_optim.html#ThresholdOptimization.condition"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._threshold_optim.ThresholdOptimization.condition"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">discard</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">tpr</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">fpr</span></span></em><spanclass="sig-paren">)</span><spanclass="sig-return"><spanclass="sig-return-icon">→</span><spanclass="sig-return-typehint"><spanclass="pre">bool</span></span></span><aclass="reference internal"href="_modules/quapy/method/_threshold_optim.html#ThresholdOptimization.discard"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._threshold_optim.ThresholdOptimization.discard"title="Permalink to this definition"></a></dt>
<emclass="property"><spanclass="pre">class</span><spanclass="w"></span></em><spanclass="sig-prename descclassname"><spanclass="pre">quapy.method._threshold_optim.</span></span><spanclass="sig-name descname"><spanclass="pre">X</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">classifier</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><spanclass="pre">BaseEstimator</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">val_split</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">5</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/_threshold_optim.html#X"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._threshold_optim.X"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">condition</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">tpr</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">fpr</span></span></em><spanclass="sig-paren">)</span><spanclass="sig-return"><spanclass="sig-return-icon">→</span><spanclass="sig-return-typehint"><spanclass="pre">float</span></span></span><aclass="reference internal"href="_modules/quapy/method/_threshold_optim.html#X.condition"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method._threshold_optim.X.condition"title="Permalink to this definition"></a></dt>
<spanid="quapy-method-base-module"></span><h2>quapy.method.base module<aclass="headerlink"href="#module-quapy.method.base"title="Permalink to this heading"></a></h2>
<emclass="property"><spanclass="pre">class</span><spanclass="w"></span></em><spanclass="sig-prename descclassname"><spanclass="pre">quapy.method.base.</span></span><spanclass="sig-name descname"><spanclass="pre">BaseQuantifier</span></span><aclass="reference internal"href="_modules/quapy/method/base.html#BaseQuantifier"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.base.BaseQuantifier"title="Permalink to this definition"></a></dt>
<p>Abstract Quantifier. A quantifier is defined as an object of a class that implements the method <aclass="reference internal"href="#quapy.method.base.BaseQuantifier.fit"title="quapy.method.base.BaseQuantifier.fit"><codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">fit()</span></code></a> on
<aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a>, the method <aclass="reference internal"href="#quapy.method.base.BaseQuantifier.quantify"title="quapy.method.base.BaseQuantifier.quantify"><codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">quantify()</span></code></a>, and the <codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">set_params()</span></code> and
<codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">get_params()</span></code> for model selection (see <aclass="reference internal"href="quapy.html#quapy.model_selection.GridSearchQ"title="quapy.model_selection.GridSearchQ"><codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">quapy.model_selection.GridSearchQ()</span></code></a>)</p>
<emclass="property"><spanclass="pre">abstract</span><spanclass="w"></span></em><spanclass="sig-name descname"><spanclass="pre">fit</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">data</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/base.html#BaseQuantifier.fit"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.base.BaseQuantifier.fit"title="Permalink to this definition"></a></dt>
<ddclass="field-odd"><p><strong>data</strong>– a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p>
<emclass="property"><spanclass="pre">abstract</span><spanclass="w"></span></em><spanclass="sig-name descname"><spanclass="pre">quantify</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">instances</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/base.html#BaseQuantifier.quantify"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.base.BaseQuantifier.quantify"title="Permalink to this definition"></a></dt>
<emclass="property"><spanclass="pre">class</span><spanclass="w"></span></em><spanclass="sig-prename descclassname"><spanclass="pre">quapy.method.base.</span></span><spanclass="sig-name descname"><spanclass="pre">BinaryQuantifier</span></span><aclass="reference internal"href="_modules/quapy/method/base.html#BinaryQuantifier"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.base.BinaryQuantifier"title="Permalink to this definition"></a></dt>
<emclass="property"><spanclass="pre">class</span><spanclass="w"></span></em><spanclass="sig-prename descclassname"><spanclass="pre">quapy.method.base.</span></span><spanclass="sig-name descname"><spanclass="pre">OneVsAll</span></span><aclass="reference internal"href="_modules/quapy/method/base.html#OneVsAll"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.base.OneVsAll"title="Permalink to this definition"></a></dt>
<emclass="property"><spanclass="pre">class</span><spanclass="w"></span></em><spanclass="sig-prename descclassname"><spanclass="pre">quapy.method.base.</span></span><spanclass="sig-name descname"><spanclass="pre">OneVsAllGeneric</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">binary_quantifier</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">n_jobs</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">None</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/base.html#OneVsAllGeneric"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.base.OneVsAllGeneric"title="Permalink to this definition"></a></dt>
<emclass="property"><spanclass="pre">property</span><spanclass="w"></span></em><spanclass="sig-name descname"><spanclass="pre">classes_</span></span><aclass="headerlink"href="#quapy.method.base.OneVsAllGeneric.classes_"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">fit</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">data</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">fit_classifier</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">True</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/base.html#OneVsAllGeneric.fit"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.base.OneVsAllGeneric.fit"title="Permalink to this definition"></a></dt>
<ddclass="field-odd"><p><strong>data</strong>– a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p>
<spanclass="sig-name descname"><spanclass="pre">quantify</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">instances</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/base.html#OneVsAllGeneric.quantify"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.base.OneVsAllGeneric.quantify"title="Permalink to this definition"></a></dt>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.method.base.</span></span><spanclass="sig-name descname"><spanclass="pre">newOneVsAll</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">binary_quantifier</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">n_jobs</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">None</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/base.html#newOneVsAll"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.base.newOneVsAll"title="Permalink to this definition"></a></dt>
<spanid="quapy-method-meta-module"></span><h2>quapy.method.meta module<aclass="headerlink"href="#module-quapy.method.meta"title="Permalink to this heading"></a></h2>
<dd><p>Implements an ensemble of <aclass="reference internal"href="#quapy.method.aggregative.ACC"title="quapy.method.aggregative.ACC"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.method.aggregative.ACC</span></code></a> quantifiers, as used by
<li><p><strong>kwargs</strong>– kwargs for the class <aclass="reference internal"href="#quapy.method.meta.Ensemble"title="quapy.method.meta.Ensemble"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">Ensemble</span></code></a></p></li>
<dd><p>Implements an ensemble of <aclass="reference internal"href="#quapy.method.aggregative.CC"title="quapy.method.aggregative.CC"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.method.aggregative.CC</span></code></a> quantifiers, as used by
<aclass="reference external"href="https://www.sciencedirect.com/science/article/pii/S1566253517303652">Pérez-Gállego et al., 2019</a>.</p>
<li><p><strong>kwargs</strong>– kwargs for the class <aclass="reference internal"href="#quapy.method.meta.Ensemble"title="quapy.method.meta.Ensemble"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">Ensemble</span></code></a></p></li>
<li><p><strong>kwargs</strong>– kwargs for the class <aclass="reference internal"href="#quapy.method.meta.Ensemble"title="quapy.method.meta.Ensemble"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">Ensemble</span></code></a></p></li>
<dd><p>Implements an ensemble of <aclass="reference internal"href="#quapy.method.aggregative.HDy"title="quapy.method.aggregative.HDy"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.method.aggregative.HDy</span></code></a> quantifiers, as used by
<aclass="reference external"href="https://www.sciencedirect.com/science/article/pii/S1566253517303652">Pérez-Gállego et al., 2019</a>.</p>
<li><p><strong>kwargs</strong>– kwargs for the class <aclass="reference internal"href="#quapy.method.meta.Ensemble"title="quapy.method.meta.Ensemble"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">Ensemble</span></code></a></p></li>
<li><p><strong>kwargs</strong>– kwargs for the class <aclass="reference internal"href="#quapy.method.meta.Ensemble"title="quapy.method.meta.Ensemble"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">Ensemble</span></code></a></p></li>
<spanclass="sig-name descname"><spanclass="pre">VALID_POLICIES</span></span><emclass="property"><spanclass="w"></span><spanclass="p"><spanclass="pre">=</span></span><spanclass="w"></span><spanclass="pre">{'ave',</span><spanclass="pre">'ds',</span><spanclass="pre">'mae',</span><spanclass="pre">'mkld',</span><spanclass="pre">'mnae',</span><spanclass="pre">'mnkld',</span><spanclass="pre">'mnrae',</span><spanclass="pre">'mrae',</span><spanclass="pre">'mse',</span><spanclass="pre">'ptr'}</span></em><aclass="headerlink"href="#quapy.method.meta.Ensemble.VALID_POLICIES"title="Permalink to this definition"></a></dt>
<li><p><strong>quantifier</strong>– base quantification member of the ensemble</p></li>
<li><p><strong>size</strong>– number of members</p></li>
<li><p><strong>red_size</strong>– number of members to retain after selection (depending on the policy)</p></li>
<li><p><strong>min_pos</strong>– minimum number of positive instances to consider a sample as valid</p></li>
<li><p><strong>policy</strong>– the selection policy; available policies include: <cite>ave</cite> (default), <cite>ptr</cite>, <cite>ds</cite>, and accuracy
(which is instantiated via a valid error name, e.g., <cite>mae</cite>)</p></li>
<li><p><strong>max_sample_size</strong>– maximum number of instances to consider in the samples (set to None
to indicate no limit, default)</p></li>
<li><p><strong>val_split</strong>– a float in range (0,1) indicating the proportion of data to be used as a stratified held-out
validation split, or a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> (the split itself).</p></li>
<li><p><strong>n_jobs</strong>– number of parallel workers (default 1)</p></li>
<li><p><strong>verbose</strong>– set to True (default is False) to get some information in standard output</p></li>
<emclass="property"><spanclass="pre">property</span><spanclass="w"></span></em><spanclass="sig-name descname"><spanclass="pre">aggregative</span></span><aclass="headerlink"href="#quapy.method.meta.Ensemble.aggregative"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">fit</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">data</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">val_split</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><spanclass="pre">Optional</span><spanclass="p"><spanclass="pre">[</span></span><spanclass="pre">Union</span><spanclass="p"><spanclass="pre">[</span></span><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a><spanclass="p"><spanclass="pre">,</span></span><spanclass="w"></span><spanclass="pre">float</span><spanclass="p"><spanclass="pre">]</span></span><spanclass="p"><spanclass="pre">]</span></span></span><spanclass="w"></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="w"></span><spanclass="default_value"><spanclass="pre">None</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/meta.html#Ensemble.fit"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.meta.Ensemble.fit"title="Permalink to this definition"></a></dt>
<ddclass="field-odd"><p><strong>data</strong>– a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p>
<spanclass="sig-name descname"><spanclass="pre">get_params</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">deep</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">True</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/meta.html#Ensemble.get_params"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.meta.Ensemble.get_params"title="Permalink to this definition"></a></dt>
<dd><p>This function should not be used within <aclass="reference internal"href="quapy.html#quapy.model_selection.GridSearchQ"title="quapy.model_selection.GridSearchQ"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.model_selection.GridSearchQ</span></code></a> (is here for compatibility
with the abstract class).
Instead, use <cite>Ensemble(GridSearchQ(q),…)</cite>, with <cite>q</cite> a Quantifier (recommended), or
<cite>Ensemble(Q(GridSearchCV(l)))</cite> with <cite>Q</cite> a quantifier class that has a classifier <cite>l</cite> optimized for
<emclass="property"><spanclass="pre">property</span><spanclass="w"></span></em><spanclass="sig-name descname"><spanclass="pre">probabilistic</span></span><aclass="headerlink"href="#quapy.method.meta.Ensemble.probabilistic"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">quantify</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">instances</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/meta.html#Ensemble.quantify"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.meta.Ensemble.quantify"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">set_params</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="o"><spanclass="pre">**</span></span><spanclass="n"><spanclass="pre">parameters</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/meta.html#Ensemble.set_params"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.meta.Ensemble.set_params"title="Permalink to this definition"></a></dt>
<dd><p>This function should not be used within <aclass="reference internal"href="quapy.html#quapy.model_selection.GridSearchQ"title="quapy.model_selection.GridSearchQ"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.model_selection.GridSearchQ</span></code></a> (is here for compatibility
with the abstract class).
Instead, use <cite>Ensemble(GridSearchQ(q),…)</cite>, with <cite>q</cite> a Quantifier (recommended), or
<cite>Ensemble(Q(GridSearchCV(l)))</cite> with <cite>Q</cite> a quantifier class that has a classifier <cite>l</cite> optimized for
<spanclass="sig-name descname"><spanclass="pre">fit</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">training</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/meta.html#MedianEstimator.fit"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.meta.MedianEstimator.fit"title="Permalink to this definition"></a></dt>
<ddclass="field-odd"><p><strong>data</strong>– a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p>
<spanclass="sig-name descname"><spanclass="pre">get_params</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">deep</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">True</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/meta.html#MedianEstimator.get_params"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.meta.MedianEstimator.get_params"title="Permalink to this definition"></a></dt>
<ddclass="field-odd"><p><strong>deep</strong> (<em>bool</em><em>, </em><em>default=True</em>) – If True, will return the parameters for this estimator and
<spanclass="sig-name descname"><spanclass="pre">quantify</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">instances</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/meta.html#MedianEstimator.quantify"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.meta.MedianEstimator.quantify"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">set_params</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="o"><spanclass="pre">**</span></span><spanclass="n"><spanclass="pre">params</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/meta.html#MedianEstimator.set_params"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.meta.MedianEstimator.set_params"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">fit</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">training</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/meta.html#MedianEstimator2.fit"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.meta.MedianEstimator2.fit"title="Permalink to this definition"></a></dt>
<ddclass="field-odd"><p><strong>data</strong>– a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p>
<spanclass="sig-name descname"><spanclass="pre">get_params</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">deep</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">True</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/meta.html#MedianEstimator2.get_params"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.meta.MedianEstimator2.get_params"title="Permalink to this definition"></a></dt>
<ddclass="field-odd"><p><strong>deep</strong> (<em>bool</em><em>, </em><em>default=True</em>) – If True, will return the parameters for this estimator and
<spanclass="sig-name descname"><spanclass="pre">quantify</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">instances</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/meta.html#MedianEstimator2.quantify"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.meta.MedianEstimator2.quantify"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">set_params</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="o"><spanclass="pre">**</span></span><spanclass="n"><spanclass="pre">params</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/meta.html#MedianEstimator2.set_params"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.meta.MedianEstimator2.set_params"title="Permalink to this definition"></a></dt>
<dd><p>Ensemble factory. Provides a unified interface for instantiating ensembles that can be optimized (via model
selection for quantification) for a given evaluation metric using <aclass="reference internal"href="quapy.html#quapy.model_selection.GridSearchQ"title="quapy.model_selection.GridSearchQ"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.model_selection.GridSearchQ</span></code></a>.
If the evaluation metric is classification-oriented
(instead of quantification-oriented), then the optimization will be carried out via sklearn’s
<p>Example to instantiate an <aclass="reference internal"href="#quapy.method.meta.Ensemble"title="quapy.method.meta.Ensemble"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">Ensemble</span></code></a> based on <aclass="reference internal"href="#quapy.method.aggregative.PACC"title="quapy.method.aggregative.PACC"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.method.aggregative.PACC</span></code></a>
in which the base members are optimized for <aclass="reference internal"href="quapy.html#quapy.error.mae"title="quapy.error.mae"><codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">quapy.error.mae()</span></code></a> via
<aclass="reference internal"href="quapy.html#quapy.model_selection.GridSearchQ"title="quapy.model_selection.GridSearchQ"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.model_selection.GridSearchQ</span></code></a>. The ensemble follows the policy <cite>Accuracy</cite> based
on <aclass="reference internal"href="quapy.html#quapy.error.mae"title="quapy.error.mae"><codeclass="xref py py-meth docutils literal notranslate"><spanclass="pre">quapy.error.mae()</span></code></a> (the same measure being optimized),
meaning that a static selection of members of the ensemble is made based on their performance
<li><p><strong>kwargs</strong>– kwargs for the class <aclass="reference internal"href="#quapy.method.meta.Ensemble"title="quapy.method.meta.Ensemble"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">Ensemble</span></code></a></p></li>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.method.meta.</span></span><spanclass="sig-name descname"><spanclass="pre">get_probability_distribution</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">posterior_probabilities</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">bins</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">8</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/meta.html#get_probability_distribution"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.meta.get_probability_distribution"title="Permalink to this definition"></a></dt>
<spanid="quapy-method-non-aggregative-module"></span><h2>quapy.method.non_aggregative module<aclass="headerlink"href="#module-quapy.method.non_aggregative"title="Permalink to this heading"></a></h2>
<emclass="property"><spanclass="pre">classmethod</span><spanclass="w"></span></em><spanclass="sig-name descname"><spanclass="pre">HDx</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">n_jobs</span></span><spanclass="o"><spanclass="pre">=</span></span><spanclass="default_value"><spanclass="pre">None</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/non_aggregative.html#DMx.HDx"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.non_aggregative.DMx.HDx"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">fit</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">data</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/non_aggregative.html#DMx.fit"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.non_aggregative.DMx.fit"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">quantify</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">instances</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/non_aggregative.html#DMx.quantify"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.non_aggregative.DMx.quantify"title="Permalink to this definition"></a></dt>
<spanclass="sig-prename descclassname"><spanclass="pre">quapy.method.non_aggregative.</span></span><spanclass="sig-name descname"><spanclass="pre">DistributionMatchingX</span></span><aclass="headerlink"href="#quapy.method.non_aggregative.DistributionMatchingX"title="Permalink to this definition"></a></dt>
<emclass="property"><spanclass="pre">class</span><spanclass="w"></span></em><spanclass="sig-prename descclassname"><spanclass="pre">quapy.method.non_aggregative.</span></span><spanclass="sig-name descname"><spanclass="pre">MaximumLikelihoodPrevalenceEstimation</span></span><aclass="reference internal"href="_modules/quapy/method/non_aggregative.html#MaximumLikelihoodPrevalenceEstimation"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">fit</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">data</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/non_aggregative.html#MaximumLikelihoodPrevalenceEstimation.fit"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.fit"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">quantify</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">instances</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/non_aggregative.html#MaximumLikelihoodPrevalenceEstimation.quantify"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.quantify"title="Permalink to this definition"></a></dt>
<spanclass="sig-name descname"><spanclass="pre">fit</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">data</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><spanclass="pre">LabelledCollection</span></a></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/non_aggregative.html#ReadMe.fit"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.non_aggregative.ReadMe.fit"title="Permalink to this definition"></a></dt>
<ddclass="field-odd"><p><strong>data</strong>– a <aclass="reference internal"href="quapy.data.html#quapy.data.base.LabelledCollection"title="quapy.data.base.LabelledCollection"><codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p>
<spanclass="sig-name descname"><spanclass="pre">quantify</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">instances</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/non_aggregative.html#ReadMe.quantify"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.non_aggregative.ReadMe.quantify"title="Permalink to this definition"></a></dt>
<dd><p>Generate class prevalence estimates for the sample’s instances</p>
<spanclass="sig-name descname"><spanclass="pre">std_constrained_linear_ls</span></span><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n"><spanclass="pre">X</span></span></em>, <emclass="sig-param"><spanclass="n"><spanclass="pre">class_cond_X</span></span><spanclass="p"><spanclass="pre">:</span></span><spanclass="w"></span><spanclass="n"><spanclass="pre">dict</span></span></em><spanclass="sig-paren">)</span><aclass="reference internal"href="_modules/quapy/method/non_aggregative.html#ReadMe.std_constrained_linear_ls"><spanclass="viewcode-link"><spanclass="pre">[source]</span></span></a><aclass="headerlink"href="#quapy.method.non_aggregative.ReadMe.std_constrained_linear_ls"title="Permalink to this definition"></a></dt>
