forked from moreo/QuaPy
adding documentation for ensembles
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@ -1394,6 +1394,11 @@ validation data, or as an integer, indicating that the misclassification rates s
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<span class="sig-prename descclassname"><span class="pre">quapy.method.meta.</span></span><span class="sig-name descname"><span class="pre">EACC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">learner</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_mod_sel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.meta.EACC" title="Permalink to this definition">¶</a></dt>
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<dd><p>Implements an ensemble of <a class="reference internal" href="#quapy.method.aggregative.ACC" title="quapy.method.aggregative.ACC"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.ACC</span></code></a> quantifiers, as used by
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<a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S1566253517303652">Pérez-Gállego et al., 2019</a>.</p>
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<p>Equivalent to:</p>
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<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">ensembleFactory</span><span class="p">(</span><span class="n">learner</span><span class="p">,</span> <span class="n">ACC</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">optim</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
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</pre></div>
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</div>
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<p>See <a class="reference internal" href="#quapy.method.meta.ensembleFactory" title="quapy.method.meta.ensembleFactory"><code class="xref py py-meth docutils literal notranslate"><span class="pre">ensembleFactory()</span></code></a> for further details.</p>
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<dl class="field-list simple">
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<dt class="field-odd">Parameters</dt>
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<dd class="field-odd"><ul class="simple">
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@ -1416,6 +1421,11 @@ validation data, or as an integer, indicating that the misclassification rates s
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<span class="sig-prename descclassname"><span class="pre">quapy.method.meta.</span></span><span class="sig-name descname"><span class="pre">ECC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">learner</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_mod_sel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.meta.ECC" title="Permalink to this definition">¶</a></dt>
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<dd><p>Implements an ensemble of <a class="reference internal" href="#quapy.method.aggregative.CC" title="quapy.method.aggregative.CC"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.CC</span></code></a> quantifiers, as used by
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<a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S1566253517303652">Pérez-Gállego et al., 2019</a>.</p>
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<p>Equivalent to:</p>
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<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">ensembleFactory</span><span class="p">(</span><span class="n">learner</span><span class="p">,</span> <span class="n">CC</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">optim</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
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</pre></div>
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</div>
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<p>See <a class="reference internal" href="#quapy.method.meta.ensembleFactory" title="quapy.method.meta.ensembleFactory"><code class="xref py py-meth docutils literal notranslate"><span class="pre">ensembleFactory()</span></code></a> for further details.</p>
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<dl class="field-list simple">
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<dt class="field-odd">Parameters</dt>
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<dd class="field-odd"><ul class="simple">
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@ -1437,6 +1447,11 @@ validation data, or as an integer, indicating that the misclassification rates s
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<dt class="sig sig-object py" id="quapy.method.meta.EEMQ">
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<span class="sig-prename descclassname"><span class="pre">quapy.method.meta.</span></span><span class="sig-name descname"><span class="pre">EEMQ</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">learner</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_mod_sel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.meta.EEMQ" title="Permalink to this definition">¶</a></dt>
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<dd><p>Implements an ensemble of <a class="reference internal" href="#quapy.method.aggregative.EMQ" title="quapy.method.aggregative.EMQ"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.EMQ</span></code></a> quantifiers.</p>
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<p>Equivalent to:</p>
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<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">ensembleFactory</span><span class="p">(</span><span class="n">learner</span><span class="p">,</span> <span class="n">EMQ</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">optim</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
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</pre></div>
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</div>
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<p>See <a class="reference internal" href="#quapy.method.meta.ensembleFactory" title="quapy.method.meta.ensembleFactory"><code class="xref py py-meth docutils literal notranslate"><span class="pre">ensembleFactory()</span></code></a> for further details.</p>
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<dl class="field-list simple">
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<dt class="field-odd">Parameters</dt>
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<dd class="field-odd"><ul class="simple">
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@ -1459,6 +1474,11 @@ validation data, or as an integer, indicating that the misclassification rates s
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<span class="sig-prename descclassname"><span class="pre">quapy.method.meta.</span></span><span class="sig-name descname"><span class="pre">EHDy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">learner</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_mod_sel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.meta.EHDy" title="Permalink to this definition">¶</a></dt>
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<dd><p>Implements an ensemble of <a class="reference internal" href="#quapy.method.aggregative.HDy" title="quapy.method.aggregative.HDy"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.HDy</span></code></a> quantifiers, as used by
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<a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S1566253517303652">Pérez-Gállego et al., 2019</a>.</p>
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<p>Equivalent to:</p>
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<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">ensembleFactory</span><span class="p">(</span><span class="n">learner</span><span class="p">,</span> <span class="n">HDy</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">optim</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
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</pre></div>
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</div>
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<p>See <a class="reference internal" href="#quapy.method.meta.ensembleFactory" title="quapy.method.meta.ensembleFactory"><code class="xref py py-meth docutils literal notranslate"><span class="pre">ensembleFactory()</span></code></a> for further details.</p>
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<dl class="field-list simple">
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<dt class="field-odd">Parameters</dt>
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<dd class="field-odd"><ul class="simple">
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@ -1480,6 +1500,11 @@ validation data, or as an integer, indicating that the misclassification rates s
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<dt class="sig sig-object py" id="quapy.method.meta.EPACC">
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<span class="sig-prename descclassname"><span class="pre">quapy.method.meta.</span></span><span class="sig-name descname"><span class="pre">EPACC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">learner</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_grid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_mod_sel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#quapy.method.meta.EPACC" title="Permalink to this definition">¶</a></dt>
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<dd><p>Implements an ensemble of <a class="reference internal" href="#quapy.method.aggregative.PACC" title="quapy.method.aggregative.PACC"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.PACC</span></code></a> quantifiers.</p>
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<p>Equivalent to:</p>
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<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">ensembleFactory</span><span class="p">(</span><span class="n">learner</span><span class="p">,</span> <span class="n">PACC</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">optim</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
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</pre></div>
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</div>
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<p>See <a class="reference internal" href="#quapy.method.meta.ensembleFactory" title="quapy.method.meta.ensembleFactory"><code class="xref py py-meth docutils literal notranslate"><span class="pre">ensembleFactory()</span></code></a> for further details.</p>
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<dl class="field-list simple">
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<dt class="field-odd">Parameters</dt>
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<dd class="field-odd"><ul class="simple">
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@ -1665,6 +1690,30 @@ selection for quantification) for a given evaluation metric using <a class="refe
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If the evaluation metric is classification-oriented
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(instead of quantification-oriented), then the optimization will be carried out via sklearn’s
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<a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html">GridSearchCV</a>.</p>
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<p>Example to instantiate an <a class="reference internal" href="#quapy.method.meta.Ensemble" title="quapy.method.meta.Ensemble"><code class="xref py py-class docutils literal notranslate"><span class="pre">Ensemble</span></code></a> based on <a class="reference internal" href="#quapy.method.aggregative.PACC" title="quapy.method.aggregative.PACC"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.method.aggregative.PACC</span></code></a>
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in which the base members are optimized for <a class="reference internal" href="quapy.html#quapy.error.mae" title="quapy.error.mae"><code class="xref py py-meth docutils literal notranslate"><span class="pre">quapy.error.mae()</span></code></a> via
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<a class="reference internal" href="quapy.html#quapy.model_selection.GridSearchQ" title="quapy.model_selection.GridSearchQ"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.model_selection.GridSearchQ</span></code></a>. The ensemble follows the policy <cite>Accuracy</cite> based
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on <a class="reference internal" href="quapy.html#quapy.error.mae" title="quapy.error.mae"><code class="xref py py-meth docutils literal notranslate"><span class="pre">quapy.error.mae()</span></code></a> (the same measure being optimized),
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meaning that a static selection of members of the ensemble is made based on their performance
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in terms of this error.</p>
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<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">param_grid</span> <span class="o">=</span> <span class="p">{</span>
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<span class="gp">>>> </span> <span class="s1">'C'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">7</span><span class="p">),</span>
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<span class="gp">>>> </span> <span class="s1">'class_weight'</span><span class="p">:</span> <span class="p">[</span><span class="s1">'balanced'</span><span class="p">,</span> <span class="kc">None</span><span class="p">]</span>
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<span class="gp">>>> </span><span class="p">}</span>
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<span class="gp">>>> </span><span class="n">param_mod_sel</span> <span class="o">=</span> <span class="p">{</span>
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<span class="gp">>>> </span> <span class="s1">'sample_size'</span><span class="p">:</span> <span class="mi">500</span><span class="p">,</span>
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<span class="gp">>>> </span> <span class="s1">'protocol'</span><span class="p">:</span> <span class="s1">'app'</span>
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<span class="gp">>>> </span><span class="p">}</span>
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<span class="gp">>>> </span><span class="n">common</span><span class="o">=</span><span class="p">{</span>
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<span class="gp">>>> </span> <span class="s1">'max_sample_size'</span><span class="p">:</span> <span class="mi">1000</span><span class="p">,</span>
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<span class="gp">>>> </span> <span class="s1">'n_jobs'</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span>
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<span class="gp">>>> </span> <span class="s1">'param_grid'</span><span class="p">:</span> <span class="n">param_grid</span><span class="p">,</span>
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<span class="gp">>>> </span> <span class="s1">'param_mod_sel'</span><span class="p">:</span> <span class="n">param_mod_sel</span><span class="p">,</span>
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<span class="gp">>>> </span><span class="p">}</span>
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<span class="go">>>></span>
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<span class="gp">>>> </span><span class="n">ensembleFactory</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">(),</span> <span class="n">PACC</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="s1">'mae'</span><span class="p">,</span> <span class="n">policy</span><span class="o">=</span><span class="s1">'mae'</span><span class="p">,</span> <span class="o">**</span><span class="n">common</span><span class="p">)</span>
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</pre></div>
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</div>
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<dl class="field-list simple">
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<dt class="field-odd">Parameters</dt>
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<dd class="field-odd"><ul class="simple">
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File diff suppressed because one or more lines are too long
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@ -379,6 +379,29 @@ def ensembleFactory(learner, base_quantifier_class, param_grid=None, optim=None,
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(instead of quantification-oriented), then the optimization will be carried out via sklearn's
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`GridSearchCV <https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html>`_.
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Example to instantiate an :class:`Ensemble` based on :class:`quapy.method.aggregative.PACC`
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in which the base members are optimized for :meth:`quapy.error.mae` via
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:class:`quapy.model_selection.GridSearchQ`. The ensemble follows the policy `Accuracy` based
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on :meth:`quapy.error.mae` (the same measure being optimized),
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meaning that a static selection of members of the ensemble is made based on their performance
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in terms of this error.
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>>> param_grid = {
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>>> 'C': np.logspace(-3,3,7),
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>>> 'class_weight': ['balanced', None]
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>>> }
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>>> param_mod_sel = {
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>>> 'sample_size': 500,
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>>> 'protocol': 'app'
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>>> }
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>>> common={
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>>> 'max_sample_size': 1000,
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>>> 'n_jobs': -1,
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>>> 'param_grid': param_grid,
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>>> 'param_mod_sel': param_mod_sel,
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>>> }
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>>>
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>>> ensembleFactory(LogisticRegression(), PACC, optim='mae', policy='mae', **common)
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:param learner: sklearn's Estimator that generates a classifier
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:param base_quantifier_class: a class of quantifiers
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@ -403,6 +426,12 @@ def ECC(learner, param_grid=None, optim=None, param_mod_sel=None, **kwargs):
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Implements an ensemble of :class:`quapy.method.aggregative.CC` quantifiers, as used by
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`Pérez-Gállego et al., 2019 <https://www.sciencedirect.com/science/article/pii/S1566253517303652>`_.
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Equivalent to:
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>>> ensembleFactory(learner, CC, param_grid, optim, param_mod_sel, **kwargs)
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See :meth:`ensembleFactory` for further details.
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:param learner: sklearn's Estimator that generates a classifier
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:param param_grid: a dictionary with the grid of parameters to optimize for
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:param optim: a valid quantification or classification error, or a string name of it
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@ -420,6 +449,12 @@ def EACC(learner, param_grid=None, optim=None, param_mod_sel=None, **kwargs):
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Implements an ensemble of :class:`quapy.method.aggregative.ACC` quantifiers, as used by
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`Pérez-Gállego et al., 2019 <https://www.sciencedirect.com/science/article/pii/S1566253517303652>`_.
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Equivalent to:
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>>> ensembleFactory(learner, ACC, param_grid, optim, param_mod_sel, **kwargs)
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See :meth:`ensembleFactory` for further details.
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:param learner: sklearn's Estimator that generates a classifier
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:param param_grid: a dictionary with the grid of parameters to optimize for
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:param optim: a valid quantification or classification error, or a string name of it
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@ -436,6 +471,12 @@ def EPACC(learner, param_grid=None, optim=None, param_mod_sel=None, **kwargs):
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"""
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Implements an ensemble of :class:`quapy.method.aggregative.PACC` quantifiers.
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Equivalent to:
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>>> ensembleFactory(learner, PACC, param_grid, optim, param_mod_sel, **kwargs)
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See :meth:`ensembleFactory` for further details.
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:param learner: sklearn's Estimator that generates a classifier
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:param param_grid: a dictionary with the grid of parameters to optimize for
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:param optim: a valid quantification or classification error, or a string name of it
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@ -453,6 +494,12 @@ def EHDy(learner, param_grid=None, optim=None, param_mod_sel=None, **kwargs):
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Implements an ensemble of :class:`quapy.method.aggregative.HDy` quantifiers, as used by
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`Pérez-Gállego et al., 2019 <https://www.sciencedirect.com/science/article/pii/S1566253517303652>`_.
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|
||||
Equivalent to:
|
||||
|
||||
>>> ensembleFactory(learner, HDy, param_grid, optim, param_mod_sel, **kwargs)
|
||||
|
||||
See :meth:`ensembleFactory` for further details.
|
||||
|
||||
:param learner: sklearn's Estimator that generates a classifier
|
||||
:param param_grid: a dictionary with the grid of parameters to optimize for
|
||||
:param optim: a valid quantification or classification error, or a string name of it
|
||||
|
@ -469,6 +516,12 @@ def EEMQ(learner, param_grid=None, optim=None, param_mod_sel=None, **kwargs):
|
|||
"""
|
||||
Implements an ensemble of :class:`quapy.method.aggregative.EMQ` quantifiers.
|
||||
|
||||
Equivalent to:
|
||||
|
||||
>>> ensembleFactory(learner, EMQ, param_grid, optim, param_mod_sel, **kwargs)
|
||||
|
||||
See :meth:`ensembleFactory` for further details.
|
||||
|
||||
:param learner: sklearn's Estimator that generates a classifier
|
||||
:param param_grid: a dictionary with the grid of parameters to optimize for
|
||||
:param optim: a valid quantification or classification error, or a string name of it
|
||||
|
|
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Reference in New Issue