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refactoring and cleaning up

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Alejandro Moreo 2026-06-30 18:02:23 +02:00
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CHANGE_LOG.txt
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@ -8,9 +8,10 @@ Change Log 0.2.1
- Extended KDEy with Aitchison/ILR kernels, shrinkage, and improved numerical stability. - Extended KDEy with Aitchison/ILR kernels, shrinkage, and improved numerical stability.
- Added TemperatureScalingFromLogits for calibrating pretrained logits. - Added TemperatureScalingFromLogits for calibrating pretrained logits.
- Added DirichletProtocol for prevalence sampling from Dirichlet priors. - Added DirichletProtocol for prevalence sampling from Dirichlet priors.
- Added ReadMe method by Daniel Hopkins and Gary King - Added ReadMe method by Daniel Hopkins and Gary King.
- Internal index in LabelledCollection is now "lazy", and is only constructed if required. - Internal index in LabelledCollection is now "lazy", and is only constructed if required.
- Improved unit testing and separated integration tests - Improved unit testing and separated integration tests.
- Added RLLS (Regularized Learning for Domain Adaptation under Label Shifts) method.
Change Log 0.2.0 Change Log 0.2.0
----------------- -----------------

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<h1>All modules for which code is available</h1> <h1>All modules for which code is available</h1>
<ul><li><a href="quapy/classification/calibration.html">quapy.classification.calibration</a></li> <ul><li><a href="quapy/classification/calibration.html">quapy.classification.calibration</a></li>
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@ -82,43 +339,88 @@
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@ -1,23 +1,20 @@
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@ -71,12 +74,13 @@
<div itemprop="articleBody"> <div itemprop="articleBody">
<h1>Source code for quapy.classification.calibration</h1><div class="highlight"><pre> <h1>Source code for quapy.classification.calibration</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span> <span></span><span class="kn">from</span><span class="w"> </span><span class="nn">copy</span><span class="w"> </span><span class="kn">import</span> <span class="n">deepcopy</span>
<span class="kn">from</span> <span class="nn">abstention.calibration</span> <span class="kn">import</span> <span class="n">NoBiasVectorScaling</span><span class="p">,</span> <span class="n">TempScaling</span><span class="p">,</span> <span class="n">VectorScaling</span> <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">clone</span>
<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">clone</span> <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">cross_val_predict</span><span class="p">,</span> <span class="n">train_test_split</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">cross_val_predict</span><span class="p">,</span> <span class="n">train_test_split</span> <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <span class="n">LabelEncoder</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.utils.validation</span><span class="w"> </span><span class="kn">import</span> <span class="n">check_X_y</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="c1"># Wrappers of calibration defined by Alexandari et al. in paper &lt;http://proceedings.mlr.press/v119/alexandari20a.html&gt;</span> <span class="c1"># Wrappers of calibration defined by Alexandari et al. in paper &lt;http://proceedings.mlr.press/v119/alexandari20a.html&gt;</span>
@ -84,7 +88,20 @@
<span class="c1"># see https://github.com/kundajelab/abstention</span> <span class="c1"># see https://github.com/kundajelab/abstention</span>
<div class="viewcode-block" id="RecalibratedProbabilisticClassifier"><a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifier">[docs]</a><span class="k">class</span> <span class="nc">RecalibratedProbabilisticClassifier</span><span class="p">:</span> <span class="k">def</span><span class="w"> </span><span class="nf">_require_abstention_calibration</span><span class="p">():</span>
<span class="k">try</span><span class="p">:</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">abstention.calibration</span><span class="w"> </span><span class="kn">import</span> <span class="n">NoBiasVectorScaling</span><span class="p">,</span> <span class="n">TempScaling</span><span class="p">,</span> <span class="n">VectorScaling</span>
<span class="k">except</span> <span class="ne">ImportError</span> <span class="k">as</span> <span class="n">exc</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span>
<span class="s2">&quot;Calibration methods in quapy.classification.calibration require the optional &quot;</span>
<span class="s2">&quot;&#39;abstention&#39; package.&quot;</span>
<span class="p">)</span> <span class="kn">from</span><span class="w"> </span><span class="nn">exc</span>
<span class="k">return</span> <span class="n">NoBiasVectorScaling</span><span class="p">,</span> <span class="n">TempScaling</span><span class="p">,</span> <span class="n">VectorScaling</span>
<div class="viewcode-block" id="RecalibratedProbabilisticClassifier">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifier">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">RecalibratedProbabilisticClassifier</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Abstract class for (re)calibration method from `abstention.calibration`, as defined in</span> <span class="sd"> Abstract class for (re)calibration method from `abstention.calibration`, as defined in</span>
<span class="sd"> `Alexandari, A., Kundaje, A., &amp; Shrikumar, A. (2020, November). Maximum likelihood with bias-corrected calibration</span> <span class="sd"> `Alexandari, A., Kundaje, A., &amp; Shrikumar, A. (2020, November). Maximum likelihood with bias-corrected calibration</span>
@ -94,7 +111,10 @@
<span class="k">pass</span></div> <span class="k">pass</span></div>
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase"><a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase">[docs]</a><span class="k">class</span> <span class="nc">RecalibratedProbabilisticClassifierBase</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">RecalibratedProbabilisticClassifier</span><span class="p">):</span>
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">RecalibratedProbabilisticClassifierBase</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">RecalibratedProbabilisticClassifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Applies a (re)calibration method from `abstention.calibration`, as defined in</span> <span class="sd"> Applies a (re)calibration method from `abstention.calibration`, as defined in</span>
<span class="sd"> `Alexandari et al. paper &lt;http://proceedings.mlr.press/v119/alexandari20a.html&gt;`_.</span> <span class="sd"> `Alexandari et al. paper &lt;http://proceedings.mlr.press/v119/alexandari20a.html&gt;`_.</span>
@ -110,14 +130,16 @@
<span class="sd"> :param verbose: whether or not to display information in the standard output</span> <span class="sd"> :param verbose: whether or not to display information in the standard output</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">calibrator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">calibrator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
<span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span> <span class="o">=</span> <span class="n">calibrator</span> <span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span> <span class="o">=</span> <span class="n">calibrator</span>
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span> <span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span>
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.fit"><a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit">[docs]</a> <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span> <div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.fit">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Fits the calibration for the probabilistic classifier.</span> <span class="sd"> Fits the calibration for the probabilistic classifier.</span>
@ -135,7 +157,10 @@
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;wrong value for val_split: the proportion of validation documents must be in (0,1)&#39;</span><span class="p">)</span> <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;wrong value for val_split: the proportion of validation documents must be in (0,1)&#39;</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_tr_val</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span></div> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_tr_val</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span></div>
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.fit_cv"><a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_cv">[docs]</a> <span class="k">def</span> <span class="nf">fit_cv</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.fit_cv">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_cv">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">fit_cv</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Fits the calibration in a cross-validation manner, i.e., it generates posterior probabilities for all</span> <span class="sd"> Fits the calibration in a cross-validation manner, i.e., it generates posterior probabilities for all</span>
<span class="sd"> training instances via cross-validation, and then retrains the classifier on all training instances.</span> <span class="sd"> training instances via cross-validation, and then retrains the classifier on all training instances.</span>
@ -153,7 +178,10 @@
<span class="bp">self</span><span class="o">.</span><span class="n">calibration_function</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span><span class="p">(</span><span class="n">posteriors</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">nclasses</span><span class="p">)[</span><span class="n">y</span><span class="p">],</span> <span class="n">posterior_supplied</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">calibration_function</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span><span class="p">(</span><span class="n">posteriors</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">nclasses</span><span class="p">)[</span><span class="n">y</span><span class="p">],</span> <span class="n">posterior_supplied</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span></div> <span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.fit_tr_val"><a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_tr_val">[docs]</a> <span class="k">def</span> <span class="nf">fit_tr_val</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.fit_tr_val">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_tr_val">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">fit_tr_val</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Fits the calibration in a train/val-split manner, i.e.t, it partitions the training instances into a</span> <span class="sd"> Fits the calibration in a train/val-split manner, i.e.t, it partitions the training instances into a</span>
<span class="sd"> training and a validation set, and then uses the training samples to learn classifier which is then used</span> <span class="sd"> training and a validation set, and then uses the training samples to learn classifier which is then used</span>
@ -171,7 +199,10 @@
<span class="bp">self</span><span class="o">.</span><span class="n">calibration_function</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span><span class="p">(</span><span class="n">posteriors</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">nclasses</span><span class="p">)[</span><span class="n">yva</span><span class="p">],</span> <span class="n">posterior_supplied</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">calibration_function</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span><span class="p">(</span><span class="n">posteriors</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">nclasses</span><span class="p">)[</span><span class="n">yva</span><span class="p">],</span> <span class="n">posterior_supplied</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span></div> <span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.predict"><a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.predict">[docs]</a> <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.predict">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.predict">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Predicts class labels for the data instances in `X`</span> <span class="sd"> Predicts class labels for the data instances in `X`</span>
@ -180,7 +211,10 @@
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.predict_proba"><a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.predict_proba">[docs]</a> <span class="k">def</span> <span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.predict_proba">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.predict_proba">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Generates posterior probabilities for the data instances in `X`</span> <span class="sd"> Generates posterior probabilities for the data instances in `X`</span>
@ -190,8 +224,9 @@
<span class="n">posteriors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="n">posteriors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">calibration_function</span><span class="p">(</span><span class="n">posteriors</span><span class="p">)</span></div> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">calibration_function</span><span class="p">(</span><span class="n">posteriors</span><span class="p">)</span></div>
<span class="nd">@property</span> <span class="nd">@property</span>
<span class="k">def</span> <span class="nf">classes_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">classes_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the classes on which the classifier has been trained on</span> <span class="sd"> Returns the classes on which the classifier has been trained on</span>
@ -200,7 +235,10 @@
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">classes_</span></div> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">classes_</span></div>
<div class="viewcode-block" id="NBVSCalibration"><a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.NBVSCalibration">[docs]</a><span class="k">class</span> <span class="nc">NBVSCalibration</span><span class="p">(</span><span class="n">RecalibratedProbabilisticClassifierBase</span><span class="p">):</span>
<div class="viewcode-block" id="NBVSCalibration">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.NBVSCalibration">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">NBVSCalibration</span><span class="p">(</span><span class="n">RecalibratedProbabilisticClassifierBase</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Applies the No-Bias Vector Scaling (NBVS) calibration method from `abstention.calibration`, as defined in</span> <span class="sd"> Applies the No-Bias Vector Scaling (NBVS) calibration method from `abstention.calibration`, as defined in</span>
<span class="sd"> `Alexandari et al. paper &lt;http://proceedings.mlr.press/v119/alexandari20a.html&gt;`_:</span> <span class="sd"> `Alexandari et al. paper &lt;http://proceedings.mlr.press/v119/alexandari20a.html&gt;`_:</span>
@ -214,7 +252,8 @@
<span class="sd"> :param verbose: whether or not to display information in the standard output</span> <span class="sd"> :param verbose: whether or not to display information in the standard output</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="n">NoBiasVectorScaling</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_require_abstention_calibration</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
<span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span> <span class="o">=</span> <span class="n">NoBiasVectorScaling</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span> <span class="o">=</span> <span class="n">NoBiasVectorScaling</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span> <span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
@ -222,7 +261,10 @@
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span></div> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span></div>
<div class="viewcode-block" id="BCTSCalibration"><a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.BCTSCalibration">[docs]</a><span class="k">class</span> <span class="nc">BCTSCalibration</span><span class="p">(</span><span class="n">RecalibratedProbabilisticClassifierBase</span><span class="p">):</span>
<div class="viewcode-block" id="BCTSCalibration">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.BCTSCalibration">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">BCTSCalibration</span><span class="p">(</span><span class="n">RecalibratedProbabilisticClassifierBase</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Applies the Bias-Corrected Temperature Scaling (BCTS) calibration method from `abstention.calibration`, as defined in</span> <span class="sd"> Applies the Bias-Corrected Temperature Scaling (BCTS) calibration method from `abstention.calibration`, as defined in</span>
<span class="sd"> `Alexandari et al. paper &lt;http://proceedings.mlr.press/v119/alexandari20a.html&gt;`_:</span> <span class="sd"> `Alexandari et al. paper &lt;http://proceedings.mlr.press/v119/alexandari20a.html&gt;`_:</span>
@ -236,7 +278,8 @@
<span class="sd"> :param verbose: whether or not to display information in the standard output</span> <span class="sd"> :param verbose: whether or not to display information in the standard output</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="n">_</span><span class="p">,</span> <span class="n">TempScaling</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_require_abstention_calibration</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
<span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span> <span class="o">=</span> <span class="n">TempScaling</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">,</span> <span class="n">bias_positions</span><span class="o">=</span><span class="s1">&#39;all&#39;</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span> <span class="o">=</span> <span class="n">TempScaling</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">,</span> <span class="n">bias_positions</span><span class="o">=</span><span class="s1">&#39;all&#39;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span> <span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
@ -244,7 +287,10 @@
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span></div> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span></div>
<div class="viewcode-block" id="TSCalibration"><a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.TSCalibration">[docs]</a><span class="k">class</span> <span class="nc">TSCalibration</span><span class="p">(</span><span class="n">RecalibratedProbabilisticClassifierBase</span><span class="p">):</span>
<div class="viewcode-block" id="TSCalibration">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.TSCalibration">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">TSCalibration</span><span class="p">(</span><span class="n">RecalibratedProbabilisticClassifierBase</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Applies the Temperature Scaling (TS) calibration method from `abstention.calibration`, as defined in</span> <span class="sd"> Applies the Temperature Scaling (TS) calibration method from `abstention.calibration`, as defined in</span>
<span class="sd"> `Alexandari et al. paper &lt;http://proceedings.mlr.press/v119/alexandari20a.html&gt;`_:</span> <span class="sd"> `Alexandari et al. paper &lt;http://proceedings.mlr.press/v119/alexandari20a.html&gt;`_:</span>
@ -258,7 +304,8 @@
<span class="sd"> :param verbose: whether or not to display information in the standard output</span> <span class="sd"> :param verbose: whether or not to display information in the standard output</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="n">_</span><span class="p">,</span> <span class="n">TempScaling</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_require_abstention_calibration</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
<span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span> <span class="o">=</span> <span class="n">TempScaling</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span> <span class="o">=</span> <span class="n">TempScaling</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span> <span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
@ -266,7 +313,10 @@
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span></div> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span></div>
<div class="viewcode-block" id="VSCalibration"><a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.VSCalibration">[docs]</a><span class="k">class</span> <span class="nc">VSCalibration</span><span class="p">(</span><span class="n">RecalibratedProbabilisticClassifierBase</span><span class="p">):</span>
<div class="viewcode-block" id="VSCalibration">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.VSCalibration">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">VSCalibration</span><span class="p">(</span><span class="n">RecalibratedProbabilisticClassifierBase</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Applies the Vector Scaling (VS) calibration method from `abstention.calibration`, as defined in</span> <span class="sd"> Applies the Vector Scaling (VS) calibration method from `abstention.calibration`, as defined in</span>
<span class="sd"> `Alexandari et al. paper &lt;http://proceedings.mlr.press/v119/alexandari20a.html&gt;`_:</span> <span class="sd"> `Alexandari et al. paper &lt;http://proceedings.mlr.press/v119/alexandari20a.html&gt;`_:</span>
@ -280,13 +330,101 @@
<span class="sd"> :param verbose: whether or not to display information in the standard output</span> <span class="sd"> :param verbose: whether or not to display information in the standard output</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">VectorScaling</span> <span class="o">=</span> <span class="n">_require_abstention_calibration</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
<span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span> <span class="o">=</span> <span class="n">VectorScaling</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span> <span class="o">=</span> <span class="n">VectorScaling</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span> <span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span></div> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span></div>
<div class="viewcode-block" id="TemperatureScalingFromLogits">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.TemperatureScalingFromLogits">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">TemperatureScalingFromLogits</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Calibrates a matrix of logits by learning a temperature-scaling mapping</span>
<span class="sd"> with the calibration methods from `abstention.calibration`.</span>
<span class="sd"> This estimator is useful when the inputs are already logits produced by a</span>
<span class="sd"> pretrained classifier, and the goal is to transform them directly into</span>
<span class="sd"> calibrated posterior probabilities without retraining the underlying model.</span>
<span class="sd"> :param bias_corrected: if True, uses Bias-Corrected Temperature Scaling</span>
<span class="sd"> (BCTS); otherwise, uses standard Temperature Scaling (TS)</span>
<span class="sd"> :param verbose: whether the underlying calibrator should display progress</span>
<span class="sd"> information</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">bias_corrected</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bias_corrected</span> <span class="o">=</span> <span class="n">bias_corrected</span>
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span>
<div class="viewcode-block" id="TemperatureScalingFromLogits.fit">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.TemperatureScalingFromLogits.fit">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Fits the logits calibrator.</span>
<span class="sd"> :param X: array-like of shape `(n_samples, n_classes)` containing</span>
<span class="sd"> logits</span>
<span class="sd"> :param y: array-like of shape `(n_samples,)` containing class labels</span>
<span class="sd"> :return: self</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">check_X_y</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">label_encoder_</span> <span class="o">=</span> <span class="n">LabelEncoder</span><span class="p">()</span>
<span class="n">y_enc</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_encoder_</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_encoder_</span><span class="o">.</span><span class="n">classes_</span>
<span class="n">n_classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
<span class="n">logits_dim</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="k">if</span> <span class="n">n_classes</span> <span class="o">!=</span> <span class="n">logits_dim</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="sa">f</span><span class="s1">&#39;mismatch between the number of classes (</span><span class="si">{</span><span class="n">n_classes</span><span class="si">}</span><span class="s1">) and the &#39;</span>
<span class="sa">f</span><span class="s1">&#39;dimensionality of the logits (</span><span class="si">{</span><span class="n">logits_dim</span><span class="si">}</span><span class="s1">)&#39;</span>
<span class="p">)</span>
<span class="n">_</span><span class="p">,</span> <span class="n">TempScaling</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_require_abstention_calibration</span><span class="p">()</span>
<span class="n">calibrator</span> <span class="o">=</span> <span class="n">TempScaling</span><span class="p">(</span>
<span class="n">verbose</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">,</span>
<span class="n">bias_positions</span><span class="o">=</span><span class="s1">&#39;all&#39;</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias_corrected</span> <span class="k">else</span> <span class="p">[],</span>
<span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">calibrator_</span> <span class="o">=</span> <span class="n">calibrator</span>
<span class="bp">self</span><span class="o">.</span><span class="n">calibration_function_</span> <span class="o">=</span> <span class="n">calibrator</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_classes</span><span class="p">)[</span><span class="n">y_enc</span><span class="p">])</span>
<span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="TemperatureScalingFromLogits.predict_proba">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.TemperatureScalingFromLogits.predict_proba">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Converts logits into calibrated posterior probabilities.</span>
<span class="sd"> :param X: array-like of shape `(n_samples, n_classes)` containing</span>
<span class="sd"> logits</span>
<span class="sd"> :return: array-like of shape `(n_samples, n_classes)` with calibrated</span>
<span class="sd"> posterior probabilities</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">calibration_function_</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
<div class="viewcode-block" id="TemperatureScalingFromLogits.predict">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.TemperatureScalingFromLogits.predict">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Predicts class labels after calibration.</span>
<span class="sd"> :param X: array-like of shape `(n_samples, n_classes)` containing</span>
<span class="sd"> logits</span>
<span class="sd"> :return: array-like of shape `(n_samples,)` with class label</span>
<span class="sd"> predictions</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">posteriors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_encoder_</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">posteriors</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span></div>
</div>
</pre></div> </pre></div>
</div> </div>

85
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@ -1,22 +1,20 @@
<!DOCTYPE html> <!DOCTYPE html>
<html class="writer-html5" lang="en" data-content_root="../../../"> <html class="writer-html5" lang="en" data-content_root="../../../">
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@ -70,14 +74,15 @@
<div itemprop="articleBody"> <div itemprop="articleBody">
<h1>Source code for quapy.classification.methods</h1><div class="highlight"><pre> <h1>Source code for quapy.classification.methods</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span> <span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <span class="n">TruncatedSVD</span> <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseEstimator</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span> <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.decomposition</span><span class="w"> </span><span class="kn">import</span> <span class="n">TruncatedSVD</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LogisticRegression</span>
<div class="viewcode-block" id="LowRankLogisticRegression"> <div class="viewcode-block" id="LowRankLogisticRegression">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression">[docs]</a> <a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression">[docs]</a>
<span class="k">class</span> <span class="nc">LowRankLogisticRegression</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">):</span> <span class="k">class</span><span class="w"> </span><span class="nc">LowRankLogisticRegression</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> An example of a classification method (i.e., an object that implements `fit`, `predict`, and `predict_proba`)</span> <span class="sd"> An example of a classification method (i.e., an object that implements `fit`, `predict`, and `predict_proba`)</span>
<span class="sd"> that also generates embedded inputs (i.e., that implements `transform`), as those required for</span> <span class="sd"> that also generates embedded inputs (i.e., that implements `transform`), as those required for</span>
@ -91,13 +96,13 @@
<span class="sd"> `Logistic Regression &lt;https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html&gt;`__ classifier</span> <span class="sd"> `Logistic Regression &lt;https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html&gt;`__ classifier</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_components</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_components</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">n_components</span> <span class="o">=</span> <span class="n">n_components</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_components</span> <span class="o">=</span> <span class="n">n_components</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<div class="viewcode-block" id="LowRankLogisticRegression.get_params"> <div class="viewcode-block" id="LowRankLogisticRegression.get_params">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.get_params">[docs]</a> <a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.get_params">[docs]</a>
<span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Get hyper-parameters for this estimator.</span> <span class="sd"> Get hyper-parameters for this estimator.</span>
@ -110,7 +115,7 @@
<div class="viewcode-block" id="LowRankLogisticRegression.set_params"> <div class="viewcode-block" id="LowRankLogisticRegression.set_params">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.set_params">[docs]</a> <a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.set_params">[docs]</a>
<span class="k">def</span> <span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">params</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">params</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Set the parameters of this estimator.</span> <span class="sd"> Set the parameters of this estimator.</span>
@ -127,7 +132,7 @@
<div class="viewcode-block" id="LowRankLogisticRegression.fit"> <div class="viewcode-block" id="LowRankLogisticRegression.fit">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.fit">[docs]</a> <a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.fit">[docs]</a>
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Fit the model according to the given training data. The fit consists of</span> <span class="sd"> Fit the model according to the given training data. The fit consists of</span>
<span class="sd"> fitting `TruncatedSVD` and then `LogisticRegression` on the low-rank representation.</span> <span class="sd"> fitting `TruncatedSVD` and then `LogisticRegression` on the low-rank representation.</span>
@ -148,7 +153,7 @@
<div class="viewcode-block" id="LowRankLogisticRegression.predict"> <div class="viewcode-block" id="LowRankLogisticRegression.predict">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.predict">[docs]</a> <a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.predict">[docs]</a>
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Predicts labels for the instances `X` embedded into the low-rank space.</span> <span class="sd"> Predicts labels for the instances `X` embedded into the low-rank space.</span>
@ -162,7 +167,7 @@
<div class="viewcode-block" id="LowRankLogisticRegression.predict_proba"> <div class="viewcode-block" id="LowRankLogisticRegression.predict_proba">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.predict_proba">[docs]</a> <a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.predict_proba">[docs]</a>
<span class="k">def</span> <span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Predicts posterior probabilities for the instances `X` embedded into the low-rank space.</span> <span class="sd"> Predicts posterior probabilities for the instances `X` embedded into the low-rank space.</span>
@ -175,7 +180,7 @@
<div class="viewcode-block" id="LowRankLogisticRegression.transform"> <div class="viewcode-block" id="LowRankLogisticRegression.transform">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.transform">[docs]</a> <a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.transform">[docs]</a>
<span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the low-rank approximation of `X` with `n_components` dimensions, or `X` unaltered if</span> <span class="sd"> Returns the low-rank approximation of `X` with `n_components` dimensions, or `X` unaltered if</span>
<span class="sd"> `n_components` &gt;= `X.shape[1]`.</span> <span class="sd"> `n_components` &gt;= `X.shape[1]`.</span>
@ -188,6 +193,38 @@
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
</div> </div>
<div class="viewcode-block" id="MockClassifierFromPosteriors">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.MockClassifierFromPosteriors">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">MockClassifierFromPosteriors</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Mock classifier that bypasses classifier training when the input instances</span>
<span class="sd"> are already posterior probabilities produced by a pretrained probabilistic</span>
<span class="sd"> classifier.</span>
<span class="sd"> :param X: arrays of shape `(n_samples, n_classes)` are interpreted as posterior probabilities</span>
<span class="sd"> &quot;&quot;&quot;</span>
<div class="viewcode-block" id="MockClassifierFromPosteriors.fit">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.MockClassifierFromPosteriors.fit">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
<span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="MockClassifierFromPosteriors.predict">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.MockClassifierFromPosteriors.predict">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span></div>
<div class="viewcode-block" id="MockClassifierFromPosteriors.predict_proba">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.MockClassifierFromPosteriors.predict_proba">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">return</span> <span class="n">X</span></div>
</div>
</pre></div> </pre></div>
</div> </div>

81
docs/build/html/_modules/quapy/classification/svmperf.html vendored
View File

@ -1,22 +1,20 @@
<!DOCTYPE html> <!DOCTYPE html>
<html class="writer-html5" lang="en" data-content_root="../../../"> <html class="writer-html5" lang="en" data-content_root="../../../">
<head> <head>
<meta charset="utf-8" /> <meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" /> <meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>quapy.classification.svmperf &mdash; QuaPy: A Python-based open-source framework for quantification 0.1.8 documentation</title> <title>quapy.classification.svmperf &mdash; QuaPy: A Python-based open-source framework for quantification 0.2.1 documentation</title>
<link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=92fd9be5" /> <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=b86133f3" />
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@ -42,7 +40,13 @@
</div> </div>
</div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu"> </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
<ul> <ul>
<li class="toctree-l1"><a class="reference internal" href="../../../modules.html">quapy</a></li> <li class="toctree-l1"><a class="reference internal" href="../../../index.html">Quickstart</a></li>
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@ -70,21 +74,21 @@
<div itemprop="articleBody"> <div itemprop="articleBody">
<h1>Source code for quapy.classification.svmperf</h1><div class="highlight"><pre> <h1>Source code for quapy.classification.svmperf</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">random</span> <span></span><span class="kn">import</span><span class="w"> </span><span class="nn">random</span>
<span class="kn">import</span> <span class="nn">shutil</span> <span class="kn">import</span><span class="w"> </span><span class="nn">shutil</span>
<span class="kn">import</span> <span class="nn">subprocess</span> <span class="kn">import</span><span class="w"> </span><span class="nn">subprocess</span>
<span class="kn">import</span> <span class="nn">tempfile</span> <span class="kn">import</span><span class="w"> </span><span class="nn">tempfile</span>
<span class="kn">from</span> <span class="nn">os</span> <span class="kn">import</span> <span class="n">remove</span><span class="p">,</span> <span class="n">makedirs</span> <span class="kn">from</span><span class="w"> </span><span class="nn">os</span><span class="w"> </span><span class="kn">import</span> <span class="n">remove</span><span class="p">,</span> <span class="n">makedirs</span>
<span class="kn">from</span> <span class="nn">os.path</span> <span class="kn">import</span> <span class="n">join</span><span class="p">,</span> <span class="n">exists</span> <span class="kn">from</span><span class="w"> </span><span class="nn">os.path</span><span class="w"> </span><span class="kn">import</span> <span class="n">join</span><span class="p">,</span> <span class="n">exists</span>
<span class="kn">from</span> <span class="nn">subprocess</span> <span class="kn">import</span> <span class="n">PIPE</span><span class="p">,</span> <span class="n">STDOUT</span> <span class="kn">from</span><span class="w"> </span><span class="nn">subprocess</span><span class="w"> </span><span class="kn">import</span> <span class="n">PIPE</span><span class="p">,</span> <span class="n">STDOUT</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">ClassifierMixin</span> <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">ClassifierMixin</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">dump_svmlight_file</span> <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <span class="n">dump_svmlight_file</span>
<div class="viewcode-block" id="SVMperf"> <div class="viewcode-block" id="SVMperf">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.svmperf.SVMperf">[docs]</a> <a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.svmperf.SVMperf">[docs]</a>
<span class="k">class</span> <span class="nc">SVMperf</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">ClassifierMixin</span><span class="p">):</span> <span class="k">class</span><span class="w"> </span><span class="nc">SVMperf</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">ClassifierMixin</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;A wrapper for the `SVM-perf package &lt;https://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html&gt;`__ by Thorsten Joachims.</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;A wrapper for the `SVM-perf package &lt;https://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html&gt;`__ by Thorsten Joachims.</span>
<span class="sd"> When using losses for quantification, the source code has to be patched. See</span> <span class="sd"> When using losses for quantification, the source code has to be patched. See</span>
<span class="sd"> the `installation documentation &lt;https://hlt-isti.github.io/QuaPy/build/html/Installation.html#svm-perf-with-quantification-oriented-losses&gt;`__</span> <span class="sd"> the `installation documentation &lt;https://hlt-isti.github.io/QuaPy/build/html/Installation.html#svm-perf-with-quantification-oriented-losses&gt;`__</span>
@ -106,31 +110,20 @@
<span class="c1"># losses with their respective codes in svm_perf implementation</span> <span class="c1"># losses with their respective codes in svm_perf implementation</span>
<span class="n">valid_losses</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;01&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span> <span class="s1">&#39;f1&#39;</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;kld&#39;</span><span class="p">:</span><span class="mi">12</span><span class="p">,</span> <span class="s1">&#39;nkld&#39;</span><span class="p">:</span><span class="mi">13</span><span class="p">,</span> <span class="s1">&#39;q&#39;</span><span class="p">:</span><span class="mi">22</span><span class="p">,</span> <span class="s1">&#39;qacc&#39;</span><span class="p">:</span><span class="mi">23</span><span class="p">,</span> <span class="s1">&#39;qf1&#39;</span><span class="p">:</span><span class="mi">24</span><span class="p">,</span> <span class="s1">&#39;qgm&#39;</span><span class="p">:</span><span class="mi">25</span><span class="p">,</span> <span class="s1">&#39;mae&#39;</span><span class="p">:</span><span class="mi">26</span><span class="p">,</span> <span class="s1">&#39;mrae&#39;</span><span class="p">:</span><span class="mi">27</span><span class="p">}</span> <span class="n">valid_losses</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;01&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span> <span class="s1">&#39;f1&#39;</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;kld&#39;</span><span class="p">:</span><span class="mi">12</span><span class="p">,</span> <span class="s1">&#39;nkld&#39;</span><span class="p">:</span><span class="mi">13</span><span class="p">,</span> <span class="s1">&#39;q&#39;</span><span class="p">:</span><span class="mi">22</span><span class="p">,</span> <span class="s1">&#39;qacc&#39;</span><span class="p">:</span><span class="mi">23</span><span class="p">,</span> <span class="s1">&#39;qf1&#39;</span><span class="p">:</span><span class="mi">24</span><span class="p">,</span> <span class="s1">&#39;qgm&#39;</span><span class="p">:</span><span class="mi">25</span><span class="p">,</span> <span class="s1">&#39;mae&#39;</span><span class="p">:</span><span class="mi">26</span><span class="p">,</span> <span class="s1">&#39;mrae&#39;</span><span class="p">:</span><span class="mi">27</span><span class="p">}</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">svmperf_base</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;01&#39;</span><span class="p">,</span> <span class="n">host_folder</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">svmperf_base</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;01&#39;</span><span class="p">,</span> <span class="n">host_folder</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">assert</span> <span class="n">exists</span><span class="p">(</span><span class="n">svmperf_base</span><span class="p">),</span> <span class="sa">f</span><span class="s1">&#39;path </span><span class="si">{</span><span class="n">svmperf_base</span><span class="si">}</span><span class="s1"> does not seem to point to a valid path&#39;</span> <span class="k">assert</span> <span class="n">exists</span><span class="p">(</span><span class="n">svmperf_base</span><span class="p">),</span> \
<span class="p">(</span><span class="sa">f</span><span class="s1">&#39;path </span><span class="si">{</span><span class="n">svmperf_base</span><span class="si">}</span><span class="s1"> does not seem to point to a valid path;&#39;</span>
<span class="sa">f</span><span class="s1">&#39;did you install svm-perf? &#39;</span>
<span class="sa">f</span><span class="s1">&#39;see instructions in https://hlt-isti.github.io/QuaPy/manuals/explicit-loss-minimization.html&#39;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">svmperf_base</span> <span class="o">=</span> <span class="n">svmperf_base</span> <span class="bp">self</span><span class="o">.</span><span class="n">svmperf_base</span> <span class="o">=</span> <span class="n">svmperf_base</span>
<span class="bp">self</span><span class="o">.</span><span class="n">C</span> <span class="o">=</span> <span class="n">C</span> <span class="bp">self</span><span class="o">.</span><span class="n">C</span> <span class="o">=</span> <span class="n">C</span>
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span>
<span class="bp">self</span><span class="o">.</span><span class="n">loss</span> <span class="o">=</span> <span class="n">loss</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss</span> <span class="o">=</span> <span class="n">loss</span>
<span class="bp">self</span><span class="o">.</span><span class="n">host_folder</span> <span class="o">=</span> <span class="n">host_folder</span> <span class="bp">self</span><span class="o">.</span><span class="n">host_folder</span> <span class="o">=</span> <span class="n">host_folder</span>
<span class="c1"># def set_params(self, **parameters):</span>
<span class="c1"># &quot;&quot;&quot;</span>
<span class="c1"># Set the hyper-parameters for svm-perf. Currently, only the `C` and `loss` parameters are supported</span>
<span class="c1">#</span>
<span class="c1"># :param parameters: a `**kwargs` dictionary `{&#39;C&#39;: &lt;float&gt;}`</span>
<span class="c1"># &quot;&quot;&quot;</span>
<span class="c1"># assert sorted(list(parameters.keys())) == [&#39;C&#39;, &#39;loss&#39;], \</span>
<span class="c1"># &#39;currently, only the C and loss parameters are supported&#39;</span>
<span class="c1"># self.C = parameters.get(&#39;C&#39;, self.C)</span>
<span class="c1"># self.loss = parameters.get(&#39;loss&#39;, self.loss)</span>
<span class="c1">#</span>
<span class="c1"># def get_params(self, deep=True):</span>
<span class="c1"># return {&#39;C&#39;: self.C, &#39;loss&#39;: self.loss}</span>
<div class="viewcode-block" id="SVMperf.fit"> <div class="viewcode-block" id="SVMperf.fit">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.svmperf.SVMperf.fit">[docs]</a> <a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.svmperf.SVMperf.fit">[docs]</a>
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Trains the SVM for the multivariate performance loss</span> <span class="sd"> Trains the SVM for the multivariate performance loss</span>
@ -180,7 +173,7 @@
<div class="viewcode-block" id="SVMperf.predict"> <div class="viewcode-block" id="SVMperf.predict">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.svmperf.SVMperf.predict">[docs]</a> <a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.svmperf.SVMperf.predict">[docs]</a>
<span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Predicts labels for the instances `X`</span> <span class="sd"> Predicts labels for the instances `X`</span>
@ -195,7 +188,7 @@
<div class="viewcode-block" id="SVMperf.decision_function"> <div class="viewcode-block" id="SVMperf.decision_function">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.svmperf.SVMperf.decision_function">[docs]</a> <a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.svmperf.SVMperf.decision_function">[docs]</a>
<span class="k">def</span> <span class="nf">decision_function</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">decision_function</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Evaluate the decision function for the samples in `X`.</span> <span class="sd"> Evaluate the decision function for the samples in `X`.</span>
@ -231,7 +224,7 @@
<span class="k">return</span> <span class="n">scores</span></div> <span class="k">return</span> <span class="n">scores</span></div>
<span class="k">def</span> <span class="fm">__del__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__del__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;tmpdir&#39;</span><span class="p">):</span> <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;tmpdir&#39;</span><span class="p">):</span>
<span class="n">shutil</span><span class="o">.</span><span class="n">rmtree</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span><span class="p">,</span> <span class="n">ignore_errors</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div> <span class="n">shutil</span><span class="o">.</span><span class="n">rmtree</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span><span class="p">,</span> <span class="n">ignore_errors</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>

201
docs/build/html/_modules/quapy/data/base.html vendored
View File

@ -1,22 +1,20 @@
<!DOCTYPE html> <!DOCTYPE html>
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<title>quapy.data.base &mdash; QuaPy: A Python-based open-source framework for quantification 0.1.8 documentation</title> <title>quapy.data.base &mdash; QuaPy: A Python-based open-source framework for quantification 0.2.1 documentation</title>
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@ -42,7 +40,13 @@
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@ -70,22 +74,23 @@
<div itemprop="articleBody"> <div itemprop="articleBody">
<h1>Source code for quapy.data.base</h1><div class="highlight"><pre> <h1>Source code for quapy.data.base</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">itertools</span> <span></span><span class="kn">import</span><span class="w"> </span><span class="nn">itertools</span>
<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">cached_property</span> <span class="kn">from</span><span class="w"> </span><span class="nn">functools</span><span class="w"> </span><span class="kn">import</span> <span class="n">cached_property</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Iterable</span> <span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Iterable</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">from</span> <span class="nn">scipy.sparse</span> <span class="kn">import</span> <span class="n">issparse</span> <span class="kn">from</span><span class="w"> </span><span class="nn">scipy.sparse</span><span class="w"> </span><span class="kn">import</span> <span class="n">issparse</span>
<span class="kn">from</span> <span class="nn">scipy.sparse</span> <span class="kn">import</span> <span class="n">vstack</span> <span class="kn">from</span><span class="w"> </span><span class="nn">scipy.sparse</span><span class="w"> </span><span class="kn">import</span> <span class="n">vstack</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span><span class="p">,</span> <span class="n">RepeatedStratifiedKFold</span> <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">train_test_split</span><span class="p">,</span> <span class="n">RepeatedStratifiedKFold</span>
<span class="kn">from</span> <span class="nn">numpy.random</span> <span class="kn">import</span> <span class="n">RandomState</span> <span class="kn">from</span><span class="w"> </span><span class="nn">numpy.random</span><span class="w"> </span><span class="kn">import</span> <span class="n">RandomState</span>
<span class="kn">from</span> <span class="nn">quapy.functional</span> <span class="kn">import</span> <span class="n">strprev</span> <span class="kn">from</span><span class="w"> </span><span class="nn">quapy.functional</span><span class="w"> </span><span class="kn">import</span> <span class="n">strprev</span>
<span class="kn">from</span> <span class="nn">quapy.util</span> <span class="kn">import</span> <span class="n">temp_seed</span> <span class="kn">from</span><span class="w"> </span><span class="nn">quapy.util</span><span class="w"> </span><span class="kn">import</span> <span class="n">temp_seed</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy.functional</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">F</span>
<div class="viewcode-block" id="LabelledCollection"> <div class="viewcode-block" id="LabelledCollection">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection">[docs]</a>
<span class="k">class</span> <span class="nc">LabelledCollection</span><span class="p">:</span> <span class="k">class</span><span class="w"> </span><span class="nc">LabelledCollection</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> A LabelledCollection is a set of objects each with a label attached to each of them. </span> <span class="sd"> A LabelledCollection is a set of objects each with a label attached to each of them. </span>
<span class="sd"> This class implements several sampling routines and other utilities.</span> <span class="sd"> This class implements several sampling routines and other utilities.</span>
@ -97,7 +102,7 @@
<span class="sd"> (i.e., a prevalence of 0)</span> <span class="sd"> (i.e., a prevalence of 0)</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">if</span> <span class="n">issparse</span><span class="p">(</span><span class="n">instances</span><span class="p">):</span> <span class="k">if</span> <span class="n">issparse</span><span class="p">(</span><span class="n">instances</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">instances</span> <span class="o">=</span> <span class="n">instances</span> <span class="bp">self</span><span class="o">.</span><span class="n">instances</span> <span class="o">=</span> <span class="n">instances</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">instances</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">instances</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">str</span><span class="p">):</span> <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">instances</span><span class="p">,</span> <span class="nb">list</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">instances</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">str</span><span class="p">):</span>
@ -106,21 +111,25 @@
<span class="k">else</span><span class="p">:</span> <span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">instances</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">instances</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span>
<span class="n">n_docs</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
<span class="k">if</span> <span class="n">classes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="k">if</span> <span class="n">classes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">classes_from_labels</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
<span class="k">else</span><span class="p">:</span> <span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">classes</span><span class="p">))</span> <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">classes</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span> <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">)</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">classes</span><span class="p">)))</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">)</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">classes</span><span class="p">)))</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;labels (</span><span class="si">{</span><span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">)</span><span class="si">}</span><span class="s1">) contain values not included in classes_ (</span><span class="si">{</span><span class="nb">set</span><span class="p">(</span><span class="n">classes</span><span class="p">)</span><span class="si">}</span><span class="s1">)&#39;</span><span class="p">)</span> <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;labels (</span><span class="si">{</span><span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">)</span><span class="si">}</span><span class="s1">) contain values not included in classes_ (</span><span class="si">{</span><span class="nb">set</span><span class="p">(</span><span class="n">classes</span><span class="p">)</span><span class="si">}</span><span class="s1">)&#39;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="p">{</span><span class="n">class_</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">n_docs</span><span class="p">)[</span><span class="bp">self</span><span class="o">.</span><span class="n">labels</span> <span class="o">==</span> <span class="n">class_</span><span class="p">]</span> <span class="k">for</span> <span class="n">class_</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">}</span> <span class="bp">self</span><span class="o">.</span><span class="n">_index</span> <span class="o">=</span> <span class="kc">None</span>
<span class="nd">@property</span>
<span class="k">def</span><span class="w"> </span><span class="nf">index</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;_index&#39;</span><span class="p">)</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_index</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_index</span> <span class="o">=</span> <span class="p">{</span><span class="n">class_</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">))[</span><span class="bp">self</span><span class="o">.</span><span class="n">labels</span> <span class="o">==</span> <span class="n">class_</span><span class="p">]</span> <span class="k">for</span> <span class="n">class_</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">}</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_index</span>
<div class="viewcode-block" id="LabelledCollection.load"> <div class="viewcode-block" id="LabelledCollection.load">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.load">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.load">[docs]</a>
<span class="nd">@classmethod</span> <span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">loader_func</span><span class="p">:</span> <span class="n">callable</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">loader_kwargs</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">load</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">loader_func</span><span class="p">:</span> <span class="nb">callable</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">loader_kwargs</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Loads a labelled set of data and convert it into a :class:`LabelledCollection` instance. The function in charge</span> <span class="sd"> Loads a labelled set of data and convert it into a :class:`LabelledCollection` instance. The function in charge</span>
<span class="sd"> of reading the instances must be specified. This function can be a custom one, or any of the reading functions</span> <span class="sd"> of reading the instances must be specified. This function can be a custom one, or any of the reading functions</span>
@ -137,7 +146,7 @@
<span class="k">return</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="o">*</span><span class="n">loader_func</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="o">**</span><span class="n">loader_kwargs</span><span class="p">),</span> <span class="n">classes</span><span class="p">)</span></div> <span class="k">return</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="o">*</span><span class="n">loader_func</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="o">**</span><span class="n">loader_kwargs</span><span class="p">),</span> <span class="n">classes</span><span class="p">)</span></div>
<span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the length of this collection (number of labelled instances)</span> <span class="sd"> Returns the length of this collection (number of labelled instances)</span>
@ -147,7 +156,7 @@
<div class="viewcode-block" id="LabelledCollection.prevalence"> <div class="viewcode-block" id="LabelledCollection.prevalence">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.prevalence">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.prevalence">[docs]</a>
<span class="k">def</span> <span class="nf">prevalence</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">prevalence</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the prevalence, or relative frequency, of the classes in the codeframe.</span> <span class="sd"> Returns the prevalence, or relative frequency, of the classes in the codeframe.</span>
@ -159,7 +168,7 @@
<div class="viewcode-block" id="LabelledCollection.counts"> <div class="viewcode-block" id="LabelledCollection.counts">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.counts">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.counts">[docs]</a>
<span class="k">def</span> <span class="nf">counts</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">counts</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the number of instances for each of the classes in the codeframe.</span> <span class="sd"> Returns the number of instances for each of the classes in the codeframe.</span>
@ -170,7 +179,7 @@
<span class="nd">@property</span> <span class="nd">@property</span>
<span class="k">def</span> <span class="nf">n_classes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">n_classes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> The number of classes</span> <span class="sd"> The number of classes</span>
@ -179,7 +188,16 @@
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span> <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
<span class="nd">@property</span> <span class="nd">@property</span>
<span class="k">def</span> <span class="nf">binary</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">n_instances</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> The number of instances</span>
<span class="sd"> :return: integer</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span><span class="w"> </span><span class="nf">binary</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns True if the number of classes is 2</span> <span class="sd"> Returns True if the number of classes is 2</span>
@ -189,12 +207,11 @@
<div class="viewcode-block" id="LabelledCollection.sampling_index"> <div class="viewcode-block" id="LabelledCollection.sampling_index">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.sampling_index">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.sampling_index">[docs]</a>
<span class="k">def</span> <span class="nf">sampling_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="o">*</span><span class="n">prevs</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">sampling_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="o">*</span><span class="n">prevs</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns an index to be used to extract a random sample of desired size and desired prevalence values. If the</span> <span class="sd"> Returns an index to be used to extract a random sample of desired size and desired prevalence values. If the</span>
<span class="sd"> prevalence values are not specified, then returns the index of a uniform sampling.</span> <span class="sd"> prevalence values are not specified, then returns the index of a uniform sampling.</span>
<span class="sd"> For each class, the sampling is drawn with replacement if the requested prevalence is larger than</span> <span class="sd"> For each class, the sampling is drawn with replacement.</span>
<span class="sd"> the actual prevalence of the class, or without replacement otherwise.</span>
<span class="sd"> :param size: integer, the requested size</span> <span class="sd"> :param size: integer, the requested size</span>
<span class="sd"> :param prevs: the prevalence for each class; the prevalence value for the last class can be lead empty since</span> <span class="sd"> :param prevs: the prevalence for each class; the prevalence value for the last class can be lead empty since</span>
@ -209,7 +226,7 @@
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">prevs</span><span class="p">)</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">prevs</span><span class="p">)</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">prevs</span> <span class="o">=</span> <span class="n">prevs</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="nb">sum</span><span class="p">(</span><span class="n">prevs</span><span class="p">),)</span> <span class="n">prevs</span> <span class="o">=</span> <span class="n">prevs</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="nb">sum</span><span class="p">(</span><span class="n">prevs</span><span class="p">),)</span>
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">prevs</span><span class="p">)</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span><span class="p">,</span> <span class="s1">&#39;unexpected number of prevalences&#39;</span> <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">prevs</span><span class="p">)</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span><span class="p">,</span> <span class="s1">&#39;unexpected number of prevalences&#39;</span>
<span class="k">assert</span> <span class="nb">sum</span><span class="p">(</span><span class="n">prevs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;prevalences (</span><span class="si">{</span><span class="n">prevs</span><span class="si">}</span><span class="s1">) wrong range (sum=</span><span class="si">{</span><span class="nb">sum</span><span class="p">(</span><span class="n">prevs</span><span class="p">)</span><span class="si">}</span><span class="s1">)&#39;</span> <span class="k">assert</span> <span class="n">np</span><span class="o">.</span><span class="n">isclose</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">prevs</span><span class="p">),</span> <span class="mi">1</span><span class="p">),</span> <span class="sa">f</span><span class="s1">&#39;prevalences (</span><span class="si">{</span><span class="n">prevs</span><span class="si">}</span><span class="s1">) wrong range (sum=</span><span class="si">{</span><span class="nb">sum</span><span class="p">(</span><span class="n">prevs</span><span class="p">)</span><span class="si">}</span><span class="s1">)&#39;</span>
<span class="c1"># Decide how many instances should be taken for each class in order to satisfy the requested prevalence</span> <span class="c1"># Decide how many instances should be taken for each class in order to satisfy the requested prevalence</span>
<span class="c1"># accurately, and the number of instances in the sample (exactly). If int(size * prevs[i]) (which is</span> <span class="c1"># accurately, and the number of instances in the sample (exactly). If int(size * prevs[i]) (which is</span>
@ -238,7 +255,7 @@
<span class="k">for</span> <span class="n">class_</span><span class="p">,</span> <span class="n">n_requested</span> <span class="ow">in</span> <span class="n">n_requests</span><span class="o">.</span><span class="n">items</span><span class="p">():</span> <span class="k">for</span> <span class="n">class_</span><span class="p">,</span> <span class="n">n_requested</span> <span class="ow">in</span> <span class="n">n_requests</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">n_candidates</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">class_</span><span class="p">])</span> <span class="n">n_candidates</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">class_</span><span class="p">])</span>
<span class="n">index_sample</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">class_</span><span class="p">][</span> <span class="n">index_sample</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">class_</span><span class="p">][</span>
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">n_candidates</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">n_requested</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="p">(</span><span class="n">n_requested</span> <span class="o">&gt;</span> <span class="n">n_candidates</span><span class="p">))</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">n_candidates</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">n_requested</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="p">]</span> <span class="k">if</span> <span class="n">n_requested</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="p">[]</span> <span class="p">]</span> <span class="k">if</span> <span class="n">n_requested</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="p">[]</span>
<span class="n">indexes_sample</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">index_sample</span><span class="p">)</span> <span class="n">indexes_sample</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">index_sample</span><span class="p">)</span>
@ -253,11 +270,10 @@
<div class="viewcode-block" id="LabelledCollection.uniform_sampling_index"> <div class="viewcode-block" id="LabelledCollection.uniform_sampling_index">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.uniform_sampling_index">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.uniform_sampling_index">[docs]</a>
<span class="k">def</span> <span class="nf">uniform_sampling_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">uniform_sampling_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns an index to be used to extract a uniform sample of desired size. The sampling is drawn</span> <span class="sd"> Returns an index to be used to extract a uniform sample of desired size. The sampling is drawn</span>
<span class="sd"> with replacement if the requested size is greater than the number of instances, or without replacement</span> <span class="sd"> with replacement.</span>
<span class="sd"> otherwise.</span>
<span class="sd"> :param size: integer, the size of the uniform sample</span> <span class="sd"> :param size: integer, the size of the uniform sample</span>
<span class="sd"> :param random_state: if specified, guarantees reproducibility of the split.</span> <span class="sd"> :param random_state: if specified, guarantees reproducibility of the split.</span>
@ -267,16 +283,15 @@
<span class="n">ng</span> <span class="o">=</span> <span class="n">RandomState</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="n">random_state</span><span class="p">)</span> <span class="n">ng</span> <span class="o">=</span> <span class="n">RandomState</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="n">random_state</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span> <span class="k">else</span><span class="p">:</span>
<span class="n">ng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span> <span class="n">ng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span>
<span class="k">return</span> <span class="n">ng</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">),</span> <span class="n">size</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="n">size</span> <span class="o">&gt;</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">))</span></div> <span class="k">return</span> <span class="n">ng</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">),</span> <span class="n">size</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>
<div class="viewcode-block" id="LabelledCollection.sampling"> <div class="viewcode-block" id="LabelledCollection.sampling">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.sampling">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.sampling">[docs]</a>
<span class="k">def</span> <span class="nf">sampling</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="o">*</span><span class="n">prevs</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">sampling</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="o">*</span><span class="n">prevs</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Return a random sample (an instance of :class:`LabelledCollection`) of desired size and desired prevalence</span> <span class="sd"> Return a random sample (an instance of :class:`LabelledCollection`) of desired size and desired prevalence</span>
<span class="sd"> values. For each class, the sampling is drawn without replacement if the requested prevalence is larger than</span> <span class="sd"> values. For each class, the sampling is drawn with replacement.</span>
<span class="sd"> the actual prevalence of the class, or with replacement otherwise.</span>
<span class="sd"> :param size: integer, the requested size</span> <span class="sd"> :param size: integer, the requested size</span>
<span class="sd"> :param prevs: the prevalence for each class; the prevalence value for the last class can be lead empty since</span> <span class="sd"> :param prevs: the prevalence for each class; the prevalence value for the last class can be lead empty since</span>
@ -293,11 +308,10 @@
<div class="viewcode-block" id="LabelledCollection.uniform_sampling"> <div class="viewcode-block" id="LabelledCollection.uniform_sampling">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.uniform_sampling">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.uniform_sampling">[docs]</a>
<span class="k">def</span> <span class="nf">uniform_sampling</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">uniform_sampling</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns a uniform sample (an instance of :class:`LabelledCollection`) of desired size. The sampling is drawn</span> <span class="sd"> Returns a uniform sample (an instance of :class:`LabelledCollection`) of desired size. The sampling is drawn</span>
<span class="sd"> with replacement if the requested size is greater than the number of instances, or without replacement</span> <span class="sd"> with replacement.</span>
<span class="sd"> otherwise.</span>
<span class="sd"> :param size: integer, the requested size</span> <span class="sd"> :param size: integer, the requested size</span>
<span class="sd"> :param random_state: if specified, guarantees reproducibility of the split.</span> <span class="sd"> :param random_state: if specified, guarantees reproducibility of the split.</span>
@ -309,7 +323,7 @@
<div class="viewcode-block" id="LabelledCollection.sampling_from_index"> <div class="viewcode-block" id="LabelledCollection.sampling_from_index">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.sampling_from_index">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.sampling_from_index">[docs]</a>
<span class="k">def</span> <span class="nf">sampling_from_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">sampling_from_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns an instance of :class:`LabelledCollection` whose elements are sampled from this collection using the</span> <span class="sd"> Returns an instance of :class:`LabelledCollection` whose elements are sampled from this collection using the</span>
<span class="sd"> index.</span> <span class="sd"> index.</span>
@ -324,7 +338,7 @@
<div class="viewcode-block" id="LabelledCollection.split_stratified"> <div class="viewcode-block" id="LabelledCollection.split_stratified">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.split_stratified">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.split_stratified">[docs]</a>
<span class="k">def</span> <span class="nf">split_stratified</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">train_prop</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">split_stratified</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">train_prop</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns two instances of :class:`LabelledCollection` split with stratification from this collection, at desired</span> <span class="sd"> Returns two instances of :class:`LabelledCollection` split with stratification from this collection, at desired</span>
<span class="sd"> proportion.</span> <span class="sd"> proportion.</span>
@ -336,17 +350,17 @@
<span class="sd"> :return: two instances of :class:`LabelledCollection`, the first one with `train_prop` elements, and the</span> <span class="sd"> :return: two instances of :class:`LabelledCollection`, the first one with `train_prop` elements, and the</span>
<span class="sd"> second one with `1-train_prop` elements</span> <span class="sd"> second one with `1-train_prop` elements</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="n">tr_docs</span><span class="p">,</span> <span class="n">te_docs</span><span class="p">,</span> <span class="n">tr_labels</span><span class="p">,</span> <span class="n">te_labels</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span> <span class="n">tr_X</span><span class="p">,</span> <span class="n">te_X</span><span class="p">,</span> <span class="n">tr_y</span><span class="p">,</span> <span class="n">te_y</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">train_size</span><span class="o">=</span><span class="n">train_prop</span><span class="p">,</span> <span class="n">stratify</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span> <span class="bp">self</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">train_size</span><span class="o">=</span><span class="n">train_prop</span><span class="p">,</span> <span class="n">stratify</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span>
<span class="p">)</span> <span class="p">)</span>
<span class="n">training</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">tr_docs</span><span class="p">,</span> <span class="n">tr_labels</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span> <span class="n">training</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">tr_X</span><span class="p">,</span> <span class="n">tr_y</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
<span class="n">test</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">te_docs</span><span class="p">,</span> <span class="n">te_labels</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span> <span class="n">test</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">te_X</span><span class="p">,</span> <span class="n">te_y</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
<span class="k">return</span> <span class="n">training</span><span class="p">,</span> <span class="n">test</span></div> <span class="k">return</span> <span class="n">training</span><span class="p">,</span> <span class="n">test</span></div>
<div class="viewcode-block" id="LabelledCollection.split_random"> <div class="viewcode-block" id="LabelledCollection.split_random">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.split_random">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.split_random">[docs]</a>
<span class="k">def</span> <span class="nf">split_random</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">train_prop</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">split_random</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">train_prop</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns two instances of :class:`LabelledCollection` split randomly from this collection, at desired</span> <span class="sd"> Returns two instances of :class:`LabelledCollection` split randomly from this collection, at desired</span>
<span class="sd"> proportion.</span> <span class="sd"> proportion.</span>
@ -373,7 +387,7 @@
<span class="k">return</span> <span class="n">training</span><span class="p">,</span> <span class="n">test</span></div> <span class="k">return</span> <span class="n">training</span><span class="p">,</span> <span class="n">test</span></div>
<span class="k">def</span> <span class="fm">__add__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__add__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns a new :class:`LabelledCollection` as the union of this collection with another collection.</span> <span class="sd"> Returns a new :class:`LabelledCollection` as the union of this collection with another collection.</span>
<span class="sd"> Both labelled collections must have the same classes.</span> <span class="sd"> Both labelled collections must have the same classes.</span>
@ -389,7 +403,7 @@
<div class="viewcode-block" id="LabelledCollection.join"> <div class="viewcode-block" id="LabelledCollection.join">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.join">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.join">[docs]</a>
<span class="nd">@classmethod</span> <span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">join</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="s1">&#39;LabelledCollection&#39;</span><span class="p">]):</span> <span class="k">def</span><span class="w"> </span><span class="nf">join</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="s1">&#39;LabelledCollection&#39;</span><span class="p">]):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns a new :class:`LabelledCollection` as the union of the collections given in input.</span> <span class="sd"> Returns a new :class:`LabelledCollection` as the union of the collections given in input.</span>
@ -430,7 +444,16 @@
<span class="nd">@property</span> <span class="nd">@property</span>
<span class="k">def</span> <span class="nf">Xy</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">classes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Gets an array-like with the classes used in this collection</span>
<span class="sd"> :return: array-like</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span>
<span class="nd">@property</span>
<span class="k">def</span><span class="w"> </span><span class="nf">Xy</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Gets the instances and labels. This is useful when working with `sklearn` estimators, e.g.:</span> <span class="sd"> Gets the instances and labels. This is useful when working with `sklearn` estimators, e.g.:</span>
@ -441,7 +464,7 @@
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">labels</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">labels</span>
<span class="nd">@property</span> <span class="nd">@property</span>
<span class="k">def</span> <span class="nf">Xp</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">Xp</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Gets the instances and the true prevalence. This is useful when implementing evaluation protocols from</span> <span class="sd"> Gets the instances and the true prevalence. This is useful when implementing evaluation protocols from</span>
<span class="sd"> a :class:`LabelledCollection` object.</span> <span class="sd"> a :class:`LabelledCollection` object.</span>
@ -451,7 +474,7 @@
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span>
<span class="nd">@property</span> <span class="nd">@property</span>
<span class="k">def</span> <span class="nf">X</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">X</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> An alias to self.instances</span> <span class="sd"> An alias to self.instances</span>
@ -460,7 +483,7 @@
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">instances</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">instances</span>
<span class="nd">@property</span> <span class="nd">@property</span>
<span class="k">def</span> <span class="nf">y</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">y</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> An alias to self.labels</span> <span class="sd"> An alias to self.labels</span>
@ -469,7 +492,7 @@
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">labels</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">labels</span>
<span class="nd">@property</span> <span class="nd">@property</span>
<span class="k">def</span> <span class="nf">p</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">p</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> An alias to self.prevalence()</span> <span class="sd"> An alias to self.prevalence()</span>
@ -480,7 +503,7 @@
<div class="viewcode-block" id="LabelledCollection.stats"> <div class="viewcode-block" id="LabelledCollection.stats">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.stats">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.stats">[docs]</a>
<span class="k">def</span> <span class="nf">stats</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">show</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">stats</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">show</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns (and eventually prints) a dictionary with some stats of this collection. E.g.,:</span> <span class="sd"> Returns (and eventually prints) a dictionary with some stats of this collection. E.g.,:</span>
@ -515,7 +538,7 @@
<div class="viewcode-block" id="LabelledCollection.kFCV"> <div class="viewcode-block" id="LabelledCollection.kFCV">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.kFCV">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.kFCV">[docs]</a>
<span class="k">def</span> <span class="nf">kFCV</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nfolds</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">nrepeats</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">kFCV</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nfolds</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">nrepeats</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Generator of stratified folds to be used in k-fold cross validation.</span> <span class="sd"> Generator of stratified folds to be used in k-fold cross validation.</span>
@ -529,13 +552,18 @@
<span class="n">train</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">train_index</span><span class="p">)</span> <span class="n">train</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">train_index</span><span class="p">)</span>
<span class="n">test</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">test_index</span><span class="p">)</span> <span class="n">test</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">test_index</span><span class="p">)</span>
<span class="k">yield</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span></div> <span class="k">yield</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span></div>
</div>
<span class="k">def</span><span class="w"> </span><span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="nb">repr</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;&lt;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">n_instances</span><span class="si">}</span><span class="s1"> instances (dtype=</span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">instances</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="si">}</span><span class="s1">), &#39;</span>
<span class="nb">repr</span><span class="o">+=</span><span class="sa">f</span><span class="s1">&#39;n_classes=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span><span class="si">}</span><span class="s1"> </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="si">}</span><span class="s1">, prevalence=</span><span class="si">{</span><span class="n">F</span><span class="o">.</span><span class="n">strprev</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">prevalence</span><span class="p">())</span><span class="si">}</span><span class="s1">&gt;&#39;</span>
<span class="k">return</span> <span class="nb">repr</span></div>
<div class="viewcode-block" id="Dataset"> <div class="viewcode-block" id="Dataset">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.Dataset">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.Dataset">[docs]</a>
<span class="k">class</span> <span class="nc">Dataset</span><span class="p">:</span> <span class="k">class</span><span class="w"> </span><span class="nc">Dataset</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Abstraction of training and test :class:`LabelledCollection` objects.</span> <span class="sd"> Abstraction of training and test :class:`LabelledCollection` objects.</span>
@ -545,7 +573,7 @@
<span class="sd"> :param name: a string representing the name of the dataset</span> <span class="sd"> :param name: a string representing the name of the dataset</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">training</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">test</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">vocabulary</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">training</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">test</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">vocabulary</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">):</span>
<span class="k">assert</span> <span class="nb">set</span><span class="p">(</span><span class="n">training</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span> <span class="o">==</span> <span class="nb">set</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">classes_</span><span class="p">),</span> <span class="s1">&#39;incompatible labels in training and test collections&#39;</span> <span class="k">assert</span> <span class="nb">set</span><span class="p">(</span><span class="n">training</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span> <span class="o">==</span> <span class="nb">set</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">classes_</span><span class="p">),</span> <span class="s1">&#39;incompatible labels in training and test collections&#39;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">training</span> <span class="o">=</span> <span class="n">training</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span> <span class="o">=</span> <span class="n">training</span>
<span class="bp">self</span><span class="o">.</span><span class="n">test</span> <span class="o">=</span> <span class="n">test</span> <span class="bp">self</span><span class="o">.</span><span class="n">test</span> <span class="o">=</span> <span class="n">test</span>
@ -555,7 +583,7 @@
<div class="viewcode-block" id="Dataset.SplitStratified"> <div class="viewcode-block" id="Dataset.SplitStratified">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.Dataset.SplitStratified">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.Dataset.SplitStratified">[docs]</a>
<span class="nd">@classmethod</span> <span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">SplitStratified</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">collection</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">train_size</span><span class="o">=</span><span class="mf">0.6</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">SplitStratified</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">collection</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">train_size</span><span class="o">=</span><span class="mf">0.6</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Generates a :class:`Dataset` from a stratified split of a :class:`LabelledCollection` instance.</span> <span class="sd"> Generates a :class:`Dataset` from a stratified split of a :class:`LabelledCollection` instance.</span>
<span class="sd"> See :meth:`LabelledCollection.split_stratified`</span> <span class="sd"> See :meth:`LabelledCollection.split_stratified`</span>
@ -568,7 +596,7 @@
<span class="nd">@property</span> <span class="nd">@property</span>
<span class="k">def</span> <span class="nf">classes_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">classes_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> The classes according to which the training collection is labelled</span> <span class="sd"> The classes according to which the training collection is labelled</span>
@ -577,7 +605,7 @@
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">classes_</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">classes_</span>
<span class="nd">@property</span> <span class="nd">@property</span>
<span class="k">def</span> <span class="nf">n_classes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">n_classes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> The number of classes according to which the training collection is labelled</span> <span class="sd"> The number of classes according to which the training collection is labelled</span>
@ -586,7 +614,7 @@
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">n_classes</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">n_classes</span>
<span class="nd">@property</span> <span class="nd">@property</span>
<span class="k">def</span> <span class="nf">binary</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">binary</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns True if the training collection is labelled according to two classes</span> <span class="sd"> Returns True if the training collection is labelled according to two classes</span>
@ -597,7 +625,7 @@
<div class="viewcode-block" id="Dataset.load"> <div class="viewcode-block" id="Dataset.load">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.Dataset.load">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.Dataset.load">[docs]</a>
<span class="nd">@classmethod</span> <span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">train_path</span><span class="p">,</span> <span class="n">test_path</span><span class="p">,</span> <span class="n">loader_func</span><span class="p">:</span> <span class="n">callable</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">loader_kwargs</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">load</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">train_path</span><span class="p">,</span> <span class="n">test_path</span><span class="p">,</span> <span class="n">loader_func</span><span class="p">:</span> <span class="nb">callable</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">loader_kwargs</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Loads a training and a test labelled set of data and convert it into a :class:`Dataset` instance.</span> <span class="sd"> Loads a training and a test labelled set of data and convert it into a :class:`Dataset` instance.</span>
<span class="sd"> The function in charge of reading the instances must be specified. This function can be a custom one, or any of</span> <span class="sd"> The function in charge of reading the instances must be specified. This function can be a custom one, or any of</span>
@ -619,7 +647,7 @@
<span class="nd">@property</span> <span class="nd">@property</span>
<span class="k">def</span> <span class="nf">vocabulary_size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">vocabulary_size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> If the dataset is textual, and the vocabulary was indicated, returns the size of the vocabulary</span> <span class="sd"> If the dataset is textual, and the vocabulary was indicated, returns the size of the vocabulary</span>
@ -628,7 +656,7 @@
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">vocabulary</span><span class="p">)</span> <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">vocabulary</span><span class="p">)</span>
<span class="nd">@property</span> <span class="nd">@property</span>
<span class="k">def</span> <span class="nf">train_test</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">train_test</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Alias to `self.training` and `self.test`</span> <span class="sd"> Alias to `self.training` and `self.test`</span>
@ -639,7 +667,7 @@
<div class="viewcode-block" id="Dataset.stats"> <div class="viewcode-block" id="Dataset.stats">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.Dataset.stats">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.Dataset.stats">[docs]</a>
<span class="k">def</span> <span class="nf">stats</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">show</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">stats</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">show</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns (and eventually prints) a dictionary with some stats of this dataset. E.g.,:</span> <span class="sd"> Returns (and eventually prints) a dictionary with some stats of this dataset. E.g.,:</span>
@ -666,7 +694,7 @@
<div class="viewcode-block" id="Dataset.kFCV"> <div class="viewcode-block" id="Dataset.kFCV">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.Dataset.kFCV">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.Dataset.kFCV">[docs]</a>
<span class="nd">@classmethod</span> <span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">kFCV</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">nfolds</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">nrepeats</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">kFCV</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">nfolds</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">nrepeats</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Generator of stratified folds to be used in k-fold cross validation. This function is only a wrapper around</span> <span class="sd"> Generator of stratified folds to be used in k-fold cross validation. This function is only a wrapper around</span>
<span class="sd"> :meth:`LabelledCollection.kFCV` that returns :class:`Dataset` instances made of training and test folds.</span> <span class="sd"> :meth:`LabelledCollection.kFCV` that returns :class:`Dataset` instances made of training and test folds.</span>
@ -683,7 +711,7 @@
<div class="viewcode-block" id="Dataset.reduce"> <div class="viewcode-block" id="Dataset.reduce">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.Dataset.reduce">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.Dataset.reduce">[docs]</a>
<span class="k">def</span> <span class="nf">reduce</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_train</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">n_test</span><span class="o">=</span><span class="mi">100</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">reduce</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_train</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">n_test</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Reduce the number of instances in place for quick experiments. Preserves the prevalence of each set.</span> <span class="sd"> Reduce the number of instances in place for quick experiments. Preserves the prevalence of each set.</span>
@ -691,10 +719,21 @@
<span class="sd"> :param n_test: number of test documents to keep (default 100)</span> <span class="sd"> :param n_test: number of test documents to keep (default 100)</span>
<span class="sd"> :return: self</span> <span class="sd"> :return: self</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">training</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="n">n_train</span><span class="p">,</span> <span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">prevalence</span><span class="p">())</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">test</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="n">n_test</span><span class="p">,</span> <span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">prevalence</span><span class="p">())</span> <span class="n">n_train</span><span class="p">,</span>
<span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">prevalence</span><span class="p">(),</span>
<span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
<span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">test</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span>
<span class="n">n_test</span><span class="p">,</span>
<span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">prevalence</span><span class="p">(),</span>
<span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
<span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span></div> <span class="k">return</span> <span class="bp">self</span></div>
</div>
<span class="k">def</span><span class="w"> </span><span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="sa">f</span><span class="s1">&#39;training=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="si">}</span><span class="s1">; test=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">test</span><span class="si">}</span><span class="s1">&#39;</span></div>
</pre></div> </pre></div>

1074
docs/build/html/_modules/quapy/data/datasets.html vendored
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134
docs/build/html/_modules/quapy/data/preprocessing.html vendored
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@ -1,22 +1,20 @@
<!DOCTYPE html> <!DOCTYPE html>
<html class="writer-html5" lang="en" data-content_root="../../../"> <html class="writer-html5" lang="en" data-content_root="../../../">
<head> <head>
<meta charset="utf-8" /> <meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" /> <meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>quapy.data.preprocessing &mdash; QuaPy: A Python-based open-source framework for quantification 0.1.8 documentation</title> <title>quapy.data.preprocessing &mdash; QuaPy: A Python-based open-source framework for quantification 0.2.1 documentation</title>
<link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=92fd9be5" /> <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=b86133f3" />
<link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css?v=19f00094" /> <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css?v=9edc463e" />
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<![endif]--> <script src="../../../_static/documentation_options.js?v=37f418d5"></script>
<script src="../../../_static/doctools.js?v=fd6eb6e6"></script>
<script src="../../../_static/jquery.js?v=5d32c60e"></script> <script src="../../../_static/sphinx_highlight.js?v=6ffebe34"></script>
<script src="../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
<script src="../../../_static/documentation_options.js?v=22607128"></script>
<script src="../../../_static/doctools.js?v=9a2dae69"></script>
<script src="../../../_static/sphinx_highlight.js?v=dc90522c"></script>
<script src="../../../_static/js/theme.js"></script> <script src="../../../_static/js/theme.js"></script>
<link rel="index" title="Index" href="../../../genindex.html" /> <link rel="index" title="Index" href="../../../genindex.html" />
<link rel="search" title="Search" href="../../../search.html" /> <link rel="search" title="Search" href="../../../search.html" />
@ -42,7 +40,13 @@
</div> </div>
</div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu"> </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
<ul> <ul>
<li class="toctree-l1"><a class="reference internal" href="../../../modules.html">quapy</a></li> <li class="toctree-l1"><a class="reference internal" href="../../../index.html">Quickstart</a></li>
</ul>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../manuals.html">Manuals</a></li>
</ul>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../quapy.html">API</a></li>
</ul> </ul>
</div> </div>
@ -70,21 +74,55 @@
<div itemprop="articleBody"> <div itemprop="articleBody">
<h1>Source code for quapy.data.preprocessing</h1><div class="highlight"><pre> <h1>Source code for quapy.data.preprocessing</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">from</span> <span class="nn">scipy.sparse</span> <span class="kn">import</span> <span class="n">spmatrix</span> <span class="kn">from</span><span class="w"> </span><span class="nn">scipy.sparse</span><span class="w"> </span><span class="kn">import</span> <span class="n">spmatrix</span>
<span class="kn">from</span> <span class="nn">sklearn.feature_extraction.text</span> <span class="kn">import</span> <span class="n">TfidfVectorizer</span><span class="p">,</span> <span class="n">CountVectorizer</span> <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.feature_extraction.text</span><span class="w"> </span><span class="kn">import</span> <span class="n">TfidfVectorizer</span><span class="p">,</span> <span class="n">CountVectorizer</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span> <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <span class="n">StandardScaler</span>
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span> <span class="kn">from</span><span class="w"> </span><span class="nn">tqdm</span><span class="w"> </span><span class="kn">import</span> <span class="n">tqdm</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">Dataset</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.util</span><span class="w"> </span><span class="kn">import</span> <span class="n">map_parallel</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">LabelledCollection</span>
<div class="viewcode-block" id="instance_transformation">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.instance_transformation">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">instance_transformation</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span><span class="n">Dataset</span><span class="p">,</span> <span class="n">transformer</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Transforms a :class:`quapy.data.base.Dataset` applying the `fit_transform` and `transform` functions</span>
<span class="sd"> of a (sklearn&#39;s) transformer.</span>
<span class="sd"> :param dataset: a :class:`quapy.data.base.Dataset` where the instances of training and test collections are</span>
<span class="sd"> lists of str</span>
<span class="sd"> :param transformer: TransformerMixin implementing `fit_transform` and `transform` functions</span>
<span class="sd"> :param inplace: whether or not to apply the transformation inplace (True), or to a new copy (False, default)</span>
<span class="sd"> :return: a new :class:`quapy.data.base.Dataset` with transformed instances (if inplace=False) or a reference to the</span>
<span class="sd"> current Dataset (if inplace=True) where the instances have been transformed</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">training_transformed</span> <span class="o">=</span> <span class="n">transformer</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="o">*</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">Xy</span><span class="p">)</span>
<span class="n">test_transformed</span> <span class="o">=</span> <span class="n">transformer</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">X</span><span class="p">)</span>
<span class="n">orig_name</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">name</span>
<span class="k">if</span> <span class="n">inplace</span><span class="p">:</span>
<span class="n">dataset</span><span class="o">.</span><span class="n">training</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">training_transformed</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
<span class="n">dataset</span><span class="o">.</span><span class="n">test</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">test_transformed</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">transformer</span><span class="p">,</span> <span class="s1">&#39;vocabulary_&#39;</span><span class="p">):</span>
<span class="n">dataset</span><span class="o">.</span><span class="n">vocabulary</span> <span class="o">=</span> <span class="n">transformer</span><span class="o">.</span><span class="n">vocabulary_</span>
<span class="k">return</span> <span class="n">dataset</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">training</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">training_transformed</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
<span class="n">test</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">test_transformed</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
<span class="n">vocab</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">transformer</span><span class="p">,</span> <span class="s1">&#39;vocabulary_&#39;</span><span class="p">):</span>
<span class="n">vocab</span> <span class="o">=</span> <span class="n">transformer</span><span class="o">.</span><span class="n">vocabulary_</span>
<span class="k">return</span> <span class="n">Dataset</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="n">test</span><span class="p">,</span> <span class="n">vocabulary</span><span class="o">=</span><span class="n">vocab</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">orig_name</span><span class="p">)</span></div>
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
<span class="kn">from</span> <span class="nn">quapy.data.base</span> <span class="kn">import</span> <span class="n">Dataset</span>
<span class="kn">from</span> <span class="nn">quapy.util</span> <span class="kn">import</span> <span class="n">map_parallel</span>
<span class="kn">from</span> <span class="nn">.base</span> <span class="kn">import</span> <span class="n">LabelledCollection</span>
<div class="viewcode-block" id="text2tfidf"> <div class="viewcode-block" id="text2tfidf">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.text2tfidf">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.text2tfidf">[docs]</a>
<span class="k">def</span> <span class="nf">text2tfidf</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span><span class="n">Dataset</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">sublinear_tf</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">text2tfidf</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span><span class="n">Dataset</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">sublinear_tf</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Transforms a :class:`quapy.data.base.Dataset` of textual instances into a :class:`quapy.data.base.Dataset` of</span> <span class="sd"> Transforms a :class:`quapy.data.base.Dataset` of textual instances into a :class:`quapy.data.base.Dataset` of</span>
<span class="sd"> tfidf weighted sparse vectors</span> <span class="sd"> tfidf weighted sparse vectors</span>
@ -103,24 +141,13 @@
<span class="n">__check_type</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="n">__check_type</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
<span class="n">vectorizer</span> <span class="o">=</span> <span class="n">TfidfVectorizer</span><span class="p">(</span><span class="n">min_df</span><span class="o">=</span><span class="n">min_df</span><span class="p">,</span> <span class="n">sublinear_tf</span><span class="o">=</span><span class="n">sublinear_tf</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="n">vectorizer</span> <span class="o">=</span> <span class="n">TfidfVectorizer</span><span class="p">(</span><span class="n">min_df</span><span class="o">=</span><span class="n">min_df</span><span class="p">,</span> <span class="n">sublinear_tf</span><span class="o">=</span><span class="n">sublinear_tf</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="n">training_documents</span> <span class="o">=</span> <span class="n">vectorizer</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span> <span class="k">return</span> <span class="n">instance_transformation</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">vectorizer</span><span class="p">,</span> <span class="n">inplace</span><span class="p">)</span></div>
<span class="n">test_documents</span> <span class="o">=</span> <span class="n">vectorizer</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
<span class="k">if</span> <span class="n">inplace</span><span class="p">:</span>
<span class="n">dataset</span><span class="o">.</span><span class="n">training</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">training_documents</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
<span class="n">dataset</span><span class="o">.</span><span class="n">test</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">test_documents</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
<span class="n">dataset</span><span class="o">.</span><span class="n">vocabulary</span> <span class="o">=</span> <span class="n">vectorizer</span><span class="o">.</span><span class="n">vocabulary_</span>
<span class="k">return</span> <span class="n">dataset</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">training</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">training_documents</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
<span class="n">test</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">test_documents</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
<span class="k">return</span> <span class="n">Dataset</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="n">test</span><span class="p">,</span> <span class="n">vectorizer</span><span class="o">.</span><span class="n">vocabulary_</span><span class="p">)</span></div>
<div class="viewcode-block" id="reduce_columns"> <div class="viewcode-block" id="reduce_columns">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.reduce_columns">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.reduce_columns">[docs]</a>
<span class="k">def</span> <span class="nf">reduce_columns</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">reduce_columns</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Reduces the dimensionality of the instances, represented as a `csr_matrix` (or any subtype of</span> <span class="sd"> Reduces the dimensionality of the instances, represented as a `csr_matrix` (or any subtype of</span>
<span class="sd"> `scipy.sparse.spmatrix`), of training and test documents by removing the columns of words which are not present</span> <span class="sd"> `scipy.sparse.spmatrix`), of training and test documents by removing the columns of words which are not present</span>
@ -138,7 +165,7 @@
<span class="n">__check_type</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="n">spmatrix</span><span class="p">)</span> <span class="n">__check_type</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="n">spmatrix</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">instances</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s1">&#39;unaligned vector spaces&#39;</span> <span class="k">assert</span> <span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">instances</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s1">&#39;unaligned vector spaces&#39;</span>
<span class="k">def</span> <span class="nf">filter_by_occurrences</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">W</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">filter_by_occurrences</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">W</span><span class="p">):</span>
<span class="n">column_prevalence</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">((</span><span class="n">X</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span> <span class="n">column_prevalence</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">((</span><span class="n">X</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
<span class="n">take_columns</span> <span class="o">=</span> <span class="n">column_prevalence</span> <span class="o">&gt;=</span> <span class="n">min_df</span> <span class="n">take_columns</span> <span class="o">=</span> <span class="n">column_prevalence</span> <span class="o">&gt;=</span> <span class="n">min_df</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="n">take_columns</span><span class="p">]</span> <span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="n">take_columns</span><span class="p">]</span>
@ -159,7 +186,7 @@
<div class="viewcode-block" id="standardize"> <div class="viewcode-block" id="standardize">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.standardize">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.standardize">[docs]</a>
<span class="k">def</span> <span class="nf">standardize</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">standardize</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Standardizes the real-valued columns of a :class:`quapy.data.base.Dataset`.</span> <span class="sd"> Standardizes the real-valued columns of a :class:`quapy.data.base.Dataset`.</span>
<span class="sd"> Standardization, aka z-scoring, of a variable `X` comes down to subtracting the average and normalizing by the</span> <span class="sd"> Standardization, aka z-scoring, of a variable `X` comes down to subtracting the average and normalizing by the</span>
@ -170,19 +197,24 @@
<span class="sd"> :class:`quapy.data.base.Dataset` is to be returned</span> <span class="sd"> :class:`quapy.data.base.Dataset` is to be returned</span>
<span class="sd"> :return: an instance of :class:`quapy.data.base.Dataset`</span> <span class="sd"> :return: an instance of :class:`quapy.data.base.Dataset`</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="n">s</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">(</span><span class="n">copy</span><span class="o">=</span><span class="ow">not</span> <span class="n">inplace</span><span class="p">)</span> <span class="n">s</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
<span class="n">training</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">train_test</span>
<span class="n">test</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span> <span class="n">std_train_X</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">train</span><span class="o">.</span><span class="n">X</span><span class="p">)</span>
<span class="n">std_test_X</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">X</span><span class="p">)</span>
<span class="k">if</span> <span class="n">inplace</span><span class="p">:</span> <span class="k">if</span> <span class="n">inplace</span><span class="p">:</span>
<span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">instances</span> <span class="o">=</span> <span class="n">std_train_X</span>
<span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span> <span class="o">=</span> <span class="n">std_test_X</span>
<span class="k">return</span> <span class="n">dataset</span> <span class="k">return</span> <span class="n">dataset</span>
<span class="k">else</span><span class="p">:</span> <span class="k">else</span><span class="p">:</span>
<span class="n">training</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">std_train_X</span><span class="p">,</span> <span class="n">train</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="n">train</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
<span class="n">test</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">std_test_X</span><span class="p">,</span> <span class="n">test</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="n">test</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
<span class="k">return</span> <span class="n">Dataset</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="n">test</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">vocabulary</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">name</span><span class="p">)</span></div> <span class="k">return</span> <span class="n">Dataset</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="n">test</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">vocabulary</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">name</span><span class="p">)</span></div>
<div class="viewcode-block" id="index"> <div class="viewcode-block" id="index">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.index">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.index">[docs]</a>
<span class="k">def</span> <span class="nf">index</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">index</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Indexes the tokens of a textual :class:`quapy.data.base.Dataset` of string documents.</span> <span class="sd"> Indexes the tokens of a textual :class:`quapy.data.base.Dataset` of string documents.</span>
<span class="sd"> To index a document means to replace each different token by a unique numerical index.</span> <span class="sd"> To index a document means to replace each different token by a unique numerical index.</span>
@ -219,7 +251,7 @@
<span class="k">def</span> <span class="nf">__check_type</span><span class="p">(</span><span class="n">container</span><span class="p">,</span> <span class="n">container_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">element_type</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">__check_type</span><span class="p">(</span><span class="n">container</span><span class="p">,</span> <span class="n">container_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">element_type</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">if</span> <span class="n">container_type</span><span class="p">:</span> <span class="k">if</span> <span class="n">container_type</span><span class="p">:</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">container</span><span class="p">,</span> <span class="n">container_type</span><span class="p">),</span> \ <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">container</span><span class="p">,</span> <span class="n">container_type</span><span class="p">),</span> \
<span class="sa">f</span><span class="s1">&#39;unexpected type of container (expected </span><span class="si">{</span><span class="n">container_type</span><span class="si">}</span><span class="s1">, found </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">container</span><span class="p">)</span><span class="si">}</span><span class="s1">)&#39;</span> <span class="sa">f</span><span class="s1">&#39;unexpected type of container (expected </span><span class="si">{</span><span class="n">container_type</span><span class="si">}</span><span class="s1">, found </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">container</span><span class="p">)</span><span class="si">}</span><span class="s1">)&#39;</span>
@ -230,7 +262,7 @@
<div class="viewcode-block" id="IndexTransformer"> <div class="viewcode-block" id="IndexTransformer">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.IndexTransformer">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.IndexTransformer">[docs]</a>
<span class="k">class</span> <span class="nc">IndexTransformer</span><span class="p">:</span> <span class="k">class</span><span class="w"> </span><span class="nc">IndexTransformer</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> This class implements a sklearn&#39;s-style transformer that indexes text as numerical ids for the tokens it</span> <span class="sd"> This class implements a sklearn&#39;s-style transformer that indexes text as numerical ids for the tokens it</span>
<span class="sd"> contains, and that would be generated by sklearn&#39;s</span> <span class="sd"> contains, and that would be generated by sklearn&#39;s</span>
@ -240,14 +272,14 @@
<span class="sd"> `CountVectorizer &lt;https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html&gt;`_</span> <span class="sd"> `CountVectorizer &lt;https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html&gt;`_</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">vect</span> <span class="o">=</span> <span class="n">CountVectorizer</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">vect</span> <span class="o">=</span> <span class="n">CountVectorizer</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">unk</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span> <span class="c1"># a valid index is assigned after fit</span> <span class="bp">self</span><span class="o">.</span><span class="n">unk</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span> <span class="c1"># a valid index is assigned after fit</span>
<span class="bp">self</span><span class="o">.</span><span class="n">pad</span> <span class="o">=</span> <span class="o">-</span><span class="mi">2</span> <span class="c1"># a valid index is assigned after fit</span> <span class="bp">self</span><span class="o">.</span><span class="n">pad</span> <span class="o">=</span> <span class="o">-</span><span class="mi">2</span> <span class="c1"># a valid index is assigned after fit</span>
<div class="viewcode-block" id="IndexTransformer.fit"> <div class="viewcode-block" id="IndexTransformer.fit">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.IndexTransformer.fit">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.IndexTransformer.fit">[docs]</a>
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Fits the transformer, i.e., decides on the vocabulary, given a list of strings.</span> <span class="sd"> Fits the transformer, i.e., decides on the vocabulary, given a list of strings.</span>
@ -264,7 +296,7 @@
<div class="viewcode-block" id="IndexTransformer.transform"> <div class="viewcode-block" id="IndexTransformer.transform">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.IndexTransformer.transform">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.IndexTransformer.transform">[docs]</a>
<span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Transforms the strings in `X` as lists of numerical ids</span> <span class="sd"> Transforms the strings in `X` as lists of numerical ids</span>
@ -279,13 +311,13 @@
<span class="k">def</span> <span class="nf">_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">documents</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">documents</span><span class="p">):</span>
<span class="n">vocab</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> <span class="n">vocab</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="k">return</span> <span class="p">[[</span><span class="n">vocab</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">word</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">unk</span><span class="p">)</span> <span class="k">for</span> <span class="n">word</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">analyzer</span><span class="p">(</span><span class="n">doc</span><span class="p">)]</span> <span class="k">for</span> <span class="n">doc</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">documents</span><span class="p">,</span> <span class="s1">&#39;indexing&#39;</span><span class="p">)]</span> <span class="k">return</span> <span class="p">[[</span><span class="n">vocab</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">word</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">unk</span><span class="p">)</span> <span class="k">for</span> <span class="n">word</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">analyzer</span><span class="p">(</span><span class="n">doc</span><span class="p">)]</span> <span class="k">for</span> <span class="n">doc</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">documents</span><span class="p">,</span> <span class="s1">&#39;indexing&#39;</span><span class="p">)]</span>
<div class="viewcode-block" id="IndexTransformer.fit_transform"> <div class="viewcode-block" id="IndexTransformer.fit_transform">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.IndexTransformer.fit_transform">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.IndexTransformer.fit_transform">[docs]</a>
<span class="k">def</span> <span class="nf">fit_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">fit_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Fits the transform on `X` and transforms it.</span> <span class="sd"> Fits the transform on `X` and transforms it.</span>
@ -298,7 +330,7 @@
<div class="viewcode-block" id="IndexTransformer.vocabulary_size"> <div class="viewcode-block" id="IndexTransformer.vocabulary_size">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.IndexTransformer.vocabulary_size">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.IndexTransformer.vocabulary_size">[docs]</a>
<span class="k">def</span> <span class="nf">vocabulary_size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">vocabulary_size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Gets the length of the vocabulary according to which the document tokens have been indexed</span> <span class="sd"> Gets the length of the vocabulary according to which the document tokens have been indexed</span>
@ -309,7 +341,7 @@
<div class="viewcode-block" id="IndexTransformer.add_word"> <div class="viewcode-block" id="IndexTransformer.add_word">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.IndexTransformer.add_word">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.IndexTransformer.add_word">[docs]</a>
<span class="k">def</span> <span class="nf">add_word</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">word</span><span class="p">,</span> <span class="nb">id</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nogaps</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">add_word</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">word</span><span class="p">,</span> <span class="nb">id</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nogaps</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Adds a new token (regardless of whether it has been found in the text or not), with dedicated id.</span> <span class="sd"> Adds a new token (regardless of whether it has been found in the text or not), with dedicated id.</span>
<span class="sd"> Useful to define special tokens for codifying unknown words, or padding tokens.</span> <span class="sd"> Useful to define special tokens for codifying unknown words, or padding tokens.</span>

48
docs/build/html/_modules/quapy/data/reader.html vendored
View File

@ -1,22 +1,20 @@
<!DOCTYPE html> <!DOCTYPE html>
<html class="writer-html5" lang="en" data-content_root="../../../"> <html class="writer-html5" lang="en" data-content_root="../../../">
<head> <head>
<meta charset="utf-8" /> <meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" /> <meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>quapy.data.reader &mdash; QuaPy: A Python-based open-source framework for quantification 0.1.8 documentation</title> <title>quapy.data.reader &mdash; QuaPy: A Python-based open-source framework for quantification 0.2.1 documentation</title>
<link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=92fd9be5" /> <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=b86133f3" />
<link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css?v=19f00094" /> <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css?v=9edc463e" />
<!--[if lt IE 9]> <script src="../../../_static/jquery.js?v=5d32c60e"></script>
<script src="../../../_static/js/html5shiv.min.js"></script> <script src="../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
<![endif]--> <script src="../../../_static/documentation_options.js?v=37f418d5"></script>
<script src="../../../_static/doctools.js?v=fd6eb6e6"></script>
<script src="../../../_static/jquery.js?v=5d32c60e"></script> <script src="../../../_static/sphinx_highlight.js?v=6ffebe34"></script>
<script src="../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
<script src="../../../_static/documentation_options.js?v=22607128"></script>
<script src="../../../_static/doctools.js?v=9a2dae69"></script>
<script src="../../../_static/sphinx_highlight.js?v=dc90522c"></script>
<script src="../../../_static/js/theme.js"></script> <script src="../../../_static/js/theme.js"></script>
<link rel="index" title="Index" href="../../../genindex.html" /> <link rel="index" title="Index" href="../../../genindex.html" />
<link rel="search" title="Search" href="../../../search.html" /> <link rel="search" title="Search" href="../../../search.html" />
@ -42,7 +40,13 @@
</div> </div>
</div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu"> </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
<ul> <ul>
<li class="toctree-l1"><a class="reference internal" href="../../../modules.html">quapy</a></li> <li class="toctree-l1"><a class="reference internal" href="../../../index.html">Quickstart</a></li>
</ul>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../manuals.html">Manuals</a></li>
</ul>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../quapy.html">API</a></li>
</ul> </ul>
</div> </div>
@ -70,14 +74,14 @@
<div itemprop="articleBody"> <div itemprop="articleBody">
<h1>Source code for quapy.data.reader</h1><div class="highlight"><pre> <h1>Source code for quapy.data.reader</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">from</span> <span class="nn">scipy.sparse</span> <span class="kn">import</span> <span class="n">dok_matrix</span> <span class="kn">from</span><span class="w"> </span><span class="nn">scipy.sparse</span><span class="w"> </span><span class="kn">import</span> <span class="n">dok_matrix</span>
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span> <span class="kn">from</span><span class="w"> </span><span class="nn">tqdm</span><span class="w"> </span><span class="kn">import</span> <span class="n">tqdm</span>
<div class="viewcode-block" id="from_text"> <div class="viewcode-block" id="from_text">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.reader.from_text">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.reader.from_text">[docs]</a>
<span class="k">def</span> <span class="nf">from_text</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">&#39;utf-8&#39;</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">class2int</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">from_text</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">&#39;utf-8&#39;</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">class2int</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Reads a labelled colletion of documents.</span> <span class="sd"> Reads a labelled colletion of documents.</span>
<span class="sd"> File fomart &lt;0 or 1&gt;\t&lt;document&gt;\n</span> <span class="sd"> File fomart &lt;0 or 1&gt;\t&lt;document&gt;\n</span>
@ -111,7 +115,7 @@
<div class="viewcode-block" id="from_sparse"> <div class="viewcode-block" id="from_sparse">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.reader.from_sparse">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.reader.from_sparse">[docs]</a>
<span class="k">def</span> <span class="nf">from_sparse</span><span class="p">(</span><span class="n">path</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">from_sparse</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Reads a labelled collection of real-valued instances expressed in sparse format</span> <span class="sd"> Reads a labelled collection of real-valued instances expressed in sparse format</span>
<span class="sd"> File format &lt;-1 or 0 or 1&gt;[\s col(int):val(float)]\n</span> <span class="sd"> File format &lt;-1 or 0 or 1&gt;[\s col(int):val(float)]\n</span>
@ -120,7 +124,7 @@
<span class="sd"> :return: a `csr_matrix` containing the instances (rows), and a ndarray containing the labels</span> <span class="sd"> :return: a `csr_matrix` containing the instances (rows), and a ndarray containing the labels</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">split_col_val</span><span class="p">(</span><span class="n">col_val</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">split_col_val</span><span class="p">(</span><span class="n">col_val</span><span class="p">):</span>
<span class="n">col</span><span class="p">,</span> <span class="n">val</span> <span class="o">=</span> <span class="n">col_val</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;:&#39;</span><span class="p">)</span> <span class="n">col</span><span class="p">,</span> <span class="n">val</span> <span class="o">=</span> <span class="n">col_val</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;:&#39;</span><span class="p">)</span>
<span class="n">col</span><span class="p">,</span> <span class="n">val</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">col</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="n">val</span><span class="p">)</span> <span class="n">col</span><span class="p">,</span> <span class="n">val</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">col</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
<span class="k">return</span> <span class="n">col</span><span class="p">,</span> <span class="n">val</span> <span class="k">return</span> <span class="n">col</span><span class="p">,</span> <span class="n">val</span>
@ -148,7 +152,7 @@
<div class="viewcode-block" id="from_csv"> <div class="viewcode-block" id="from_csv">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.reader.from_csv">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.reader.from_csv">[docs]</a>
<span class="k">def</span> <span class="nf">from_csv</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">&#39;utf-8&#39;</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">from_csv</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">&#39;utf-8&#39;</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Reads a csv file in which columns are separated by &#39;,&#39;.</span> <span class="sd"> Reads a csv file in which columns are separated by &#39;,&#39;.</span>
<span class="sd"> File format &lt;label&gt;,&lt;feat1&gt;,&lt;feat2&gt;,...,&lt;featn&gt;\n</span> <span class="sd"> File format &lt;label&gt;,&lt;feat1&gt;,&lt;feat2&gt;,...,&lt;featn&gt;\n</span>
@ -171,7 +175,7 @@
<div class="viewcode-block" id="reindex_labels"> <div class="viewcode-block" id="reindex_labels">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.reader.reindex_labels">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.reader.reindex_labels">[docs]</a>
<span class="k">def</span> <span class="nf">reindex_labels</span><span class="p">(</span><span class="n">y</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">reindex_labels</span><span class="p">(</span><span class="n">y</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Re-indexes a list of labels as a list of indexes, and returns the classnames corresponding to the indexes.</span> <span class="sd"> Re-indexes a list of labels as a list of indexes, and returns the classnames corresponding to the indexes.</span>
<span class="sd"> E.g.:</span> <span class="sd"> E.g.:</span>
@ -194,7 +198,7 @@
<div class="viewcode-block" id="binarize"> <div class="viewcode-block" id="binarize">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.reader.binarize">[docs]</a> <a class="viewcode-back" href="../../../quapy.data.html#quapy.data.reader.binarize">[docs]</a>
<span class="k">def</span> <span class="nf">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Binarizes a categorical array-like collection of labels towards the positive class `pos_class`. E.g.,:</span> <span class="sd"> Binarizes a categorical array-like collection of labels towards the positive class `pos_class`. E.g.,:</span>

377
docs/build/html/_modules/quapy/error.html vendored
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@ -1,23 +1,20 @@
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@ -43,7 +40,13 @@
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@ -73,12 +76,14 @@
<h1>Source code for quapy.error</h1><div class="highlight"><pre> <h1>Source code for quapy.error</h1><div class="highlight"><pre>
<span></span><span class="sd">&quot;&quot;&quot;Implementation of error measures used for quantification&quot;&quot;&quot;</span> <span></span><span class="sd">&quot;&quot;&quot;Implementation of error measures used for quantification&quot;&quot;&quot;</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">f1_score</span> <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">f1_score</span>
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span> <span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
<div class="viewcode-block" id="from_name"><a class="viewcode-back" href="../../quapy.html#quapy.error.from_name">[docs]</a><span class="k">def</span> <span class="nf">from_name</span><span class="p">(</span><span class="n">err_name</span><span class="p">):</span> <div class="viewcode-block" id="from_name">
<a class="viewcode-back" href="../../quapy.html#quapy.error.from_name">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">from_name</span><span class="p">(</span><span class="n">err_name</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Gets an error function from its name. E.g., `from_name(&quot;mae&quot;)`</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;Gets an error function from its name. E.g., `from_name(&quot;mae&quot;)`</span>
<span class="sd"> will return function :meth:`quapy.error.mae`</span> <span class="sd"> will return function :meth:`quapy.error.mae`</span>
@ -90,7 +95,10 @@
<span class="k">return</span> <span class="n">callable_error</span></div> <span class="k">return</span> <span class="n">callable_error</span></div>
<div class="viewcode-block" id="f1e"><a class="viewcode-back" href="../../quapy.html#quapy.error.f1e">[docs]</a><span class="k">def</span> <span class="nf">f1e</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">):</span>
<div class="viewcode-block" id="f1e">
<a class="viewcode-back" href="../../quapy.html#quapy.error.f1e">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">f1e</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;F1 error: simply computes the error in terms of macro :math:`F_1`, i.e.,</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;F1 error: simply computes the error in terms of macro :math:`F_1`, i.e.,</span>
<span class="sd"> :math:`1-F_1^M`, where :math:`F_1` is the harmonic mean of precision and recall,</span> <span class="sd"> :math:`1-F_1^M`, where :math:`F_1` is the harmonic mean of precision and recall,</span>
<span class="sd"> defined as :math:`\\frac{2tp}{2tp+fp+fn}`, with `tp`, `fp`, and `fn` standing</span> <span class="sd"> defined as :math:`\\frac{2tp}{2tp+fp+fn}`, with `tp`, `fp`, and `fn` standing</span>
@ -105,7 +113,10 @@
<span class="k">return</span> <span class="mf">1.</span> <span class="o">-</span> <span class="n">f1_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;macro&#39;</span><span class="p">)</span></div> <span class="k">return</span> <span class="mf">1.</span> <span class="o">-</span> <span class="n">f1_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">&#39;macro&#39;</span><span class="p">)</span></div>
<div class="viewcode-block" id="acce"><a class="viewcode-back" href="../../quapy.html#quapy.error.acce">[docs]</a><span class="k">def</span> <span class="nf">acce</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">):</span>
<div class="viewcode-block" id="acce">
<a class="viewcode-back" href="../../quapy.html#quapy.error.acce">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">acce</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Computes the error in terms of 1-accuracy. The accuracy is computed as</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;Computes the error in terms of 1-accuracy. The accuracy is computed as</span>
<span class="sd"> :math:`\\frac{tp+tn}{tp+fp+fn+tn}`, with `tp`, `fp`, `fn`, and `tn` standing</span> <span class="sd"> :math:`\\frac{tp+tn}{tp+fp+fn+tn}`, with `tp`, `fp`, `fn`, and `tn` standing</span>
<span class="sd"> for true positives, false positives, false negatives, and true negatives,</span> <span class="sd"> for true positives, false positives, false negatives, and true negatives,</span>
@ -118,89 +129,207 @@
<span class="k">return</span> <span class="mf">1.</span> <span class="o">-</span> <span class="p">(</span><span class="n">y_true</span> <span class="o">==</span> <span class="n">y_pred</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div> <span class="k">return</span> <span class="mf">1.</span> <span class="o">-</span> <span class="p">(</span><span class="n">y_true</span> <span class="o">==</span> <span class="n">y_pred</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>
<div class="viewcode-block" id="mae"><a class="viewcode-back" href="../../quapy.html#quapy.error.mae">[docs]</a><span class="k">def</span> <span class="nf">mae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
<div class="viewcode-block" id="mae">
<a class="viewcode-back" href="../../quapy.html#quapy.error.mae">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">mae</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Computes the mean absolute error (see :meth:`quapy.error.ae`) across the sample pairs.</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;Computes the mean absolute error (see :meth:`quapy.error.ae`) across the sample pairs.</span>
<span class="sd"> :param prevs: array-like of shape `(n_samples, n_classes,)` with the true prevalence values</span> <span class="sd"> :param prevs_true: array-like of shape `(n_samples, n_classes,)` with the true prevalence values</span>
<span class="sd"> :param prevs_hat: array-like of shape `(n_samples, n_classes,)` with the predicted</span> <span class="sd"> :param prevs_hat: array-like of shape `(n_samples, n_classes,)` with the predicted</span>
<span class="sd"> prevalence values</span> <span class="sd"> prevalence values</span>
<span class="sd"> :return: mean absolute error</span> <span class="sd"> :return: mean absolute error</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">ae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div> <span class="k">return</span> <span class="n">ae</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>
<div class="viewcode-block" id="ae"><a class="viewcode-back" href="../../quapy.html#quapy.error.ae">[docs]</a><span class="k">def</span> <span class="nf">ae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
<div class="viewcode-block" id="ae">
<a class="viewcode-back" href="../../quapy.html#quapy.error.ae">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">ae</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Computes the absolute error between the two prevalence vectors.</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;Computes the absolute error between the two prevalence vectors.</span>
<span class="sd"> Absolute error between two prevalence vectors :math:`p` and :math:`\\hat{p}` is computed as</span> <span class="sd"> Absolute error between two prevalence vectors :math:`p` and :math:`\\hat{p}` is computed as</span>
<span class="sd"> :math:`AE(p,\\hat{p})=\\frac{1}{|\\mathcal{Y}|}\\sum_{y\\in \\mathcal{Y}}|\\hat{p}(y)-p(y)|`,</span> <span class="sd"> :math:`AE(p,\\hat{p})=\\frac{1}{|\\mathcal{Y}|}\\sum_{y\\in \\mathcal{Y}}|\\hat{p}(y)-p(y)|`,</span>
<span class="sd"> where :math:`\\mathcal{Y}` are the classes of interest.</span> <span class="sd"> where :math:`\\mathcal{Y}` are the classes of interest.</span>
<span class="sd"> :param prevs: array-like of shape `(n_classes,)` with the true prevalence values</span> <span class="sd"> :param prevs_true: array-like of shape `(n_classes,)` with the true prevalence values</span>
<span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` with the predicted prevalence values</span> <span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` with the predicted prevalence values</span>
<span class="sd"> :return: absolute error</span> <span class="sd"> :return: absolute error</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">assert</span> <span class="n">prevs</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">prevs_hat</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;wrong shape </span><span class="si">{</span><span class="n">prevs</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s1"> vs. </span><span class="si">{</span><span class="n">prevs_hat</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s1">&#39;</span> <span class="n">prevs_true</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">)</span>
<span class="k">return</span> <span class="nb">abs</span><span class="p">(</span><span class="n">prevs_hat</span> <span class="o">-</span> <span class="n">prevs</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></div> <span class="n">prevs_hat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevs_hat</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">prevs_true</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">prevs_hat</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;wrong shape </span><span class="si">{</span><span class="n">prevs_true</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s1"> vs. </span><span class="si">{</span><span class="n">prevs_hat</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s1">&#39;</span>
<span class="k">return</span> <span class="nb">abs</span><span class="p">(</span><span class="n">prevs_hat</span> <span class="o">-</span> <span class="n">prevs_true</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></div>
<div class="viewcode-block" id="nae"><a class="viewcode-back" href="../../quapy.html#quapy.error.nae">[docs]</a><span class="k">def</span> <span class="nf">nae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
<div class="viewcode-block" id="nae">
<a class="viewcode-back" href="../../quapy.html#quapy.error.nae">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">nae</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Computes the normalized absolute error between the two prevalence vectors.</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;Computes the normalized absolute error between the two prevalence vectors.</span>
<span class="sd"> Normalized absolute error between two prevalence vectors :math:`p` and :math:`\\hat{p}` is computed as</span> <span class="sd"> Normalized absolute error between two prevalence vectors :math:`p` and :math:`\\hat{p}` is computed as</span>
<span class="sd"> :math:`NAE(p,\\hat{p})=\\frac{AE(p,\\hat{p})}{z_{AE}}`,</span> <span class="sd"> :math:`NAE(p,\\hat{p})=\\frac{AE(p,\\hat{p})}{z_{AE}}`,</span>
<span class="sd"> where :math:`z_{AE}=\\frac{2(1-\\min_{y\\in \\mathcal{Y}} p(y))}{|\\mathcal{Y}|}`, and :math:`\\mathcal{Y}`</span> <span class="sd"> where :math:`z_{AE}=\\frac{2(1-\\min_{y\\in \\mathcal{Y}} p(y))}{|\\mathcal{Y}|}`, and :math:`\\mathcal{Y}`</span>
<span class="sd"> are the classes of interest.</span> <span class="sd"> are the classes of interest.</span>
<span class="sd"> :param prevs: array-like of shape `(n_classes,)` with the true prevalence values</span> <span class="sd"> :param prevs_true: array-like of shape `(n_classes,)` with the true prevalence values</span>
<span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` with the predicted prevalence values</span> <span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` with the predicted prevalence values</span>
<span class="sd"> :return: normalized absolute error</span> <span class="sd"> :return: normalized absolute error</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">assert</span> <span class="n">prevs</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">prevs_hat</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;wrong shape </span><span class="si">{</span><span class="n">prevs</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s1"> vs. </span><span class="si">{</span><span class="n">prevs_hat</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s1">&#39;</span> <span class="n">prevs_true</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">)</span>
<span class="k">return</span> <span class="nb">abs</span><span class="p">(</span><span class="n">prevs_hat</span> <span class="o">-</span> <span class="n">prevs</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">prevs</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)))</span></div> <span class="n">prevs_hat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevs_hat</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">prevs_true</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">prevs_hat</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;wrong shape </span><span class="si">{</span><span class="n">prevs_true</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s1"> vs. </span><span class="si">{</span><span class="n">prevs_hat</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s1">&#39;</span>
<span class="k">return</span> <span class="nb">abs</span><span class="p">(</span><span class="n">prevs_hat</span> <span class="o">-</span> <span class="n">prevs_true</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">prevs_true</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)))</span></div>
<div class="viewcode-block" id="mnae"><a class="viewcode-back" href="../../quapy.html#quapy.error.mnae">[docs]</a><span class="k">def</span> <span class="nf">mnae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
<div class="viewcode-block" id="mnae">
<a class="viewcode-back" href="../../quapy.html#quapy.error.mnae">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">mnae</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Computes the mean normalized absolute error (see :meth:`quapy.error.nae`) across the sample pairs.</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;Computes the mean normalized absolute error (see :meth:`quapy.error.nae`) across the sample pairs.</span>
<span class="sd"> :param prevs: array-like of shape `(n_samples, n_classes,)` with the true prevalence values</span> <span class="sd"> :param prevs_true: array-like of shape `(n_samples, n_classes,)` with the true prevalence values</span>
<span class="sd"> :param prevs_hat: array-like of shape `(n_samples, n_classes,)` with the predicted</span> <span class="sd"> :param prevs_hat: array-like of shape `(n_samples, n_classes,)` with the predicted</span>
<span class="sd"> prevalence values</span> <span class="sd"> prevalence values</span>
<span class="sd"> :return: mean normalized absolute error</span> <span class="sd"> :return: mean normalized absolute error</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">nae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div> <span class="k">return</span> <span class="n">nae</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>
<div class="viewcode-block" id="mse"><a class="viewcode-back" href="../../quapy.html#quapy.error.mse">[docs]</a><span class="k">def</span> <span class="nf">mse</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
<div class="viewcode-block" id="mse">
<a class="viewcode-back" href="../../quapy.html#quapy.error.mse">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">mse</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Computes the mean squared error (see :meth:`quapy.error.se`) across the sample pairs.</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;Computes the mean squared error (see :meth:`quapy.error.se`) across the sample pairs.</span>
<span class="sd"> :param prevs: array-like of shape `(n_samples, n_classes,)` with the</span> <span class="sd"> :param prevs_true: array-like of shape `(n_samples, n_classes,)` with the</span>
<span class="sd"> true prevalence values</span> <span class="sd"> true prevalence values</span>
<span class="sd"> :param prevs_hat: array-like of shape `(n_samples, n_classes,)` with the</span> <span class="sd"> :param prevs_hat: array-like of shape `(n_samples, n_classes,)` with the</span>
<span class="sd"> predicted prevalence values</span> <span class="sd"> predicted prevalence values</span>
<span class="sd"> :return: mean squared error</span> <span class="sd"> :return: mean squared error</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">se</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div> <span class="k">return</span> <span class="n">se</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>
<div class="viewcode-block" id="se"><a class="viewcode-back" href="../../quapy.html#quapy.error.se">[docs]</a><span class="k">def</span> <span class="nf">se</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
<div class="viewcode-block" id="se">
<a class="viewcode-back" href="../../quapy.html#quapy.error.se">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">se</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Computes the squared error between the two prevalence vectors.</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;Computes the squared error between the two prevalence vectors.</span>
<span class="sd"> Squared error between two prevalence vectors :math:`p` and :math:`\\hat{p}` is computed as</span> <span class="sd"> Squared error between two prevalence vectors :math:`p` and :math:`\\hat{p}` is computed as</span>
<span class="sd"> :math:`SE(p,\\hat{p})=\\frac{1}{|\\mathcal{Y}|}\\sum_{y\\in \\mathcal{Y}}(\\hat{p}(y)-p(y))^2`,</span> <span class="sd"> :math:`SE(p,\\hat{p})=\\frac{1}{|\\mathcal{Y}|}\\sum_{y\\in \\mathcal{Y}}(\\hat{p}(y)-p(y))^2`,</span>
<span class="sd"> where</span> <span class="sd"> where</span>
<span class="sd"> :math:`\\mathcal{Y}` are the classes of interest.</span> <span class="sd"> :math:`\\mathcal{Y}` are the classes of interest.</span>
<span class="sd"> :param prevs: array-like of shape `(n_classes,)` with the true prevalence values</span> <span class="sd"> :param prevs_true: array-like of shape `(n_classes,)` with the true prevalence values</span>
<span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` with the predicted prevalence values</span> <span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` with the predicted prevalence values</span>
<span class="sd"> :return: absolute error</span> <span class="sd"> :return: absolute error</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="p">((</span><span class="n">prevs_hat</span> <span class="o">-</span> <span class="n">prevs</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></div> <span class="n">prevs_true</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">)</span>
<span class="n">prevs_hat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevs_hat</span><span class="p">)</span>
<span class="k">return</span> <span class="p">((</span><span class="n">prevs_hat</span> <span class="o">-</span> <span class="n">prevs_true</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></div>
<div class="viewcode-block" id="mkld"><a class="viewcode-back" href="../../quapy.html#quapy.error.mkld">[docs]</a><span class="k">def</span> <span class="nf">mkld</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<div class="viewcode-block" id="sre">
<a class="viewcode-back" href="../../quapy.html#quapy.error.sre">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">sre</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">prevs_train</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">0.</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Computes the squared ratio error between two prevalence vectors.</span>
<span class="sd"> The squared ratio error between prevalence vectors :math:`p` and</span>
<span class="sd"> :math:`\\hat{p}` with training prevalence :math:`p^{tr}` is:</span>
<span class="sd"> :math:`SRE(p,\\hat{p},p^{tr})=\\frac{1}{|\\mathcal{Y}|}\\sum_{i \\in \\mathcal{Y}}(w_i-\\hat{w}_i)^2`,</span>
<span class="sd"> where :math:`w_i=\\frac{p_i}{p^{tr}_i}`.</span>
<span class="sd"> :param prevs_true: array-like with the true prevalence values</span>
<span class="sd"> :param prevs_hat: array-like with the predicted prevalence values</span>
<span class="sd"> :param prevs_train: array-like with the training prevalence values, or a single</span>
<span class="sd"> prevalence vector when all comparisons refer to the same training set</span>
<span class="sd"> :param eps: smoothing factor for the prevalence values (default 0, i.e., no smoothing)</span>
<span class="sd"> :return: squared ratio error</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">prevs_true</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">)</span>
<span class="n">prevs_hat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevs_hat</span><span class="p">)</span>
<span class="n">prevs_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevs_train</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">prevs_true</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">prevs_hat</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;wrong shape </span><span class="si">{</span><span class="n">prevs_true</span><span class="o">.</span><span class="n">shape</span><span class="si">=}</span><span class="s1"> vs </span><span class="si">{</span><span class="n">prevs_hat</span><span class="o">.</span><span class="n">shape</span><span class="si">=}</span><span class="s1">&#39;</span>
<span class="k">assert</span> <span class="n">prevs_true</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">prevs_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="s1">&#39;wrong shape for training prevalence&#39;</span>
<span class="k">if</span> <span class="n">prevs_true</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">2</span> <span class="ow">and</span> <span class="n">prevs_train</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">prevs_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">prevs_train</span><span class="p">,</span> <span class="n">reps</span><span class="o">=</span><span class="p">(</span><span class="n">prevs_true</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">))</span>
<span class="k">if</span> <span class="n">eps</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">prevs_true</span> <span class="o">=</span> <span class="n">smooth</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span>
<span class="n">prevs_hat</span> <span class="o">=</span> <span class="n">smooth</span><span class="p">(</span><span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span>
<span class="n">prevs_train</span> <span class="o">=</span> <span class="n">smooth</span><span class="p">(</span><span class="n">prevs_train</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span>
<span class="n">n_classes</span> <span class="o">=</span> <span class="n">prevs_true</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">w</span> <span class="o">=</span> <span class="n">prevs_true</span> <span class="o">/</span> <span class="n">prevs_train</span>
<span class="n">w_hat</span> <span class="o">=</span> <span class="n">prevs_hat</span> <span class="o">/</span> <span class="n">prevs_train</span>
<span class="k">return</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">/</span> <span class="n">n_classes</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">w</span> <span class="o">-</span> <span class="n">w_hat</span><span class="p">)</span> <span class="o">**</span> <span class="mf">2.</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></div>
<div class="viewcode-block" id="msre">
<a class="viewcode-back" href="../../quapy.html#quapy.error.msre">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">msre</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">prevs_train</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">0.</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Computes the mean squared ratio error (see :meth:`quapy.error.sre`) across the sample pairs.</span>
<span class="sd"> :param prevs_true: array-like of shape `(n_samples, n_classes,)` with the true prevalence values</span>
<span class="sd"> :param prevs_hat: array-like of shape equal to prevs_true with the predicted prevalence values</span>
<span class="sd"> :param prevs_train: array-like with the training prevalence values</span>
<span class="sd"> :param eps: smoothing factor (default 0, i.e., no smoothing)</span>
<span class="sd"> :return: mean squared ratio error</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">sre</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">prevs_train</span><span class="p">,</span> <span class="n">eps</span><span class="p">))</span></div>
<div class="viewcode-block" id="aitchisondist">
<a class="viewcode-back" href="../../quapy.html#quapy.error.aitchisondist">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">aitchisondist</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Computes the Aitchison distance between two prevalence vectors.</span>
<span class="sd"> The Aitchison distance between prevalence vectors :math:`p` and</span>
<span class="sd"> :math:`\\hat{p}` is computed as</span>
<span class="sd"> :math:`d_A(p,\\hat{p})=\\|\\mathrm{clr}(p)-\\mathrm{clr}(\\hat{p})\\|_2`,</span>
<span class="sd"> where :math:`\\mathrm{clr}(p)_i=\\log p_i-\\frac{1}{|\\mathcal{Y}|}</span>
<span class="sd"> \\sum_{j \\in \\mathcal{Y}} \\log p_j`.</span>
<span class="sd"> :param prevs_true: array-like with the true prevalence values</span>
<span class="sd"> :param prevs_hat: array-like with the predicted prevalence values</span>
<span class="sd"> :return: Aitchison distance</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.functional</span><span class="w"> </span><span class="kn">import</span> <span class="n">CLRtransformation</span>
<span class="n">clr</span> <span class="o">=</span> <span class="n">CLRtransformation</span><span class="p">()</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">clr</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">)</span> <span class="o">-</span> <span class="n">clr</span><span class="p">(</span><span class="n">prevs_hat</span><span class="p">),</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></div>
<div class="viewcode-block" id="maitchisondist">
<a class="viewcode-back" href="../../quapy.html#quapy.error.maitchisondist">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">maitchisondist</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Computes the mean Aitchison distance (see :meth:`quapy.error.aitchisondist`)</span>
<span class="sd"> across the sample pairs, i.e.,</span>
<span class="sd"> :math:`\\mathrm{mAitchisonDist}=\\frac{1}{n}\\sum_{i=1}^n</span>
<span class="sd"> d_A(p_i,\\hat{p}_i)`.</span>
<span class="sd"> :param prevs_true: array-like with the true prevalence values</span>
<span class="sd"> :param prevs_hat: array-like with the predicted prevalence values</span>
<span class="sd"> :return: mean Aitchison distance</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">aitchisondist</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">))</span></div>
<div class="viewcode-block" id="mkld">
<a class="viewcode-back" href="../../quapy.html#quapy.error.mkld">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">mkld</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Computes the mean Kullback-Leibler divergence (see :meth:`quapy.error.kld`) across the</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;Computes the mean Kullback-Leibler divergence (see :meth:`quapy.error.kld`) across the</span>
<span class="sd"> sample pairs. The distributions are smoothed using the `eps` factor</span> <span class="sd"> sample pairs. The distributions are smoothed using the `eps` factor</span>
<span class="sd"> (see :meth:`quapy.error.smooth`).</span> <span class="sd"> (see :meth:`quapy.error.smooth`).</span>
<span class="sd"> :param prevs: array-like of shape `(n_samples, n_classes,)` with the true</span> <span class="sd"> :param prevs_true: array-like of shape `(n_samples, n_classes,)` with the true</span>
<span class="sd"> prevalence values</span> <span class="sd"> prevalence values</span>
<span class="sd"> :param prevs_hat: array-like of shape `(n_samples, n_classes,)` with the predicted</span> <span class="sd"> :param prevs_hat: array-like of shape `(n_samples, n_classes,)` with the predicted</span>
<span class="sd"> prevalence values</span> <span class="sd"> prevalence values</span>
@ -210,10 +339,13 @@
<span class="sd"> (which has thus to be set beforehand).</span> <span class="sd"> (which has thus to be set beforehand).</span>
<span class="sd"> :return: mean Kullback-Leibler distribution</span> <span class="sd"> :return: mean Kullback-Leibler distribution</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">kld</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div> <span class="k">return</span> <span class="n">kld</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>
<div class="viewcode-block" id="kld"><a class="viewcode-back" href="../../quapy.html#quapy.error.kld">[docs]</a><span class="k">def</span> <span class="nf">kld</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<div class="viewcode-block" id="kld">
<a class="viewcode-back" href="../../quapy.html#quapy.error.kld">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">kld</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Computes the Kullback-Leibler divergence between the two prevalence distributions.</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;Computes the Kullback-Leibler divergence between the two prevalence distributions.</span>
<span class="sd"> Kullback-Leibler divergence between two prevalence distributions :math:`p` and :math:`\\hat{p}`</span> <span class="sd"> Kullback-Leibler divergence between two prevalence distributions :math:`p` and :math:`\\hat{p}`</span>
<span class="sd"> is computed as</span> <span class="sd"> is computed as</span>
@ -222,7 +354,7 @@
<span class="sd"> where :math:`\\mathcal{Y}` are the classes of interest.</span> <span class="sd"> where :math:`\\mathcal{Y}` are the classes of interest.</span>
<span class="sd"> The distributions are smoothed using the `eps` factor (see :meth:`quapy.error.smooth`).</span> <span class="sd"> The distributions are smoothed using the `eps` factor (see :meth:`quapy.error.smooth`).</span>
<span class="sd"> :param prevs: array-like of shape `(n_classes,)` with the true prevalence values</span> <span class="sd"> :param prevs_true: array-like of shape `(n_classes,)` with the true prevalence values</span>
<span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` with the predicted prevalence values</span> <span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` with the predicted prevalence values</span>
<span class="sd"> :param eps: smoothing factor. KLD is not defined in cases in which the distributions contain</span> <span class="sd"> :param eps: smoothing factor. KLD is not defined in cases in which the distributions contain</span>
<span class="sd"> zeros; `eps` is typically set to be :math:`\\frac{1}{2T}`, with :math:`T` the sample size.</span> <span class="sd"> zeros; `eps` is typically set to be :math:`\\frac{1}{2T}`, with :math:`T` the sample size.</span>
@ -231,17 +363,20 @@
<span class="sd"> :return: Kullback-Leibler divergence between the two distributions</span> <span class="sd"> :return: Kullback-Leibler divergence between the two distributions</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="n">eps</span> <span class="o">=</span> <span class="n">__check_eps</span><span class="p">(</span><span class="n">eps</span><span class="p">)</span> <span class="n">eps</span> <span class="o">=</span> <span class="n">__check_eps</span><span class="p">(</span><span class="n">eps</span><span class="p">)</span>
<span class="n">smooth_prevs</span> <span class="o">=</span> <span class="n">prevs</span> <span class="o">+</span> <span class="n">eps</span> <span class="n">smooth_prevs</span> <span class="o">=</span> <span class="n">smooth</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span>
<span class="n">smooth_prevs_hat</span> <span class="o">=</span> <span class="n">prevs_hat</span> <span class="o">+</span> <span class="n">eps</span> <span class="n">smooth_prevs_hat</span> <span class="o">=</span> <span class="n">smooth</span><span class="p">(</span><span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span>
<span class="k">return</span> <span class="p">(</span><span class="n">smooth_prevs</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">smooth_prevs</span><span class="o">/</span><span class="n">smooth_prevs_hat</span><span class="p">))</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></div> <span class="k">return</span> <span class="p">(</span><span class="n">smooth_prevs</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">smooth_prevs</span><span class="o">/</span><span class="n">smooth_prevs_hat</span><span class="p">))</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></div>
<div class="viewcode-block" id="mnkld"><a class="viewcode-back" href="../../quapy.html#quapy.error.mnkld">[docs]</a><span class="k">def</span> <span class="nf">mnkld</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<div class="viewcode-block" id="mnkld">
<a class="viewcode-back" href="../../quapy.html#quapy.error.mnkld">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">mnkld</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Computes the mean Normalized Kullback-Leibler divergence (see :meth:`quapy.error.nkld`)</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;Computes the mean Normalized Kullback-Leibler divergence (see :meth:`quapy.error.nkld`)</span>
<span class="sd"> across the sample pairs. The distributions are smoothed using the `eps` factor</span> <span class="sd"> across the sample pairs. The distributions are smoothed using the `eps` factor</span>
<span class="sd"> (see :meth:`quapy.error.smooth`).</span> <span class="sd"> (see :meth:`quapy.error.smooth`).</span>
<span class="sd"> :param prevs: array-like of shape `(n_samples, n_classes,)` with the true prevalence values</span> <span class="sd"> :param prevs_true: array-like of shape `(n_samples, n_classes,)` with the true prevalence values</span>
<span class="sd"> :param prevs_hat: array-like of shape `(n_samples, n_classes,)` with the predicted</span> <span class="sd"> :param prevs_hat: array-like of shape `(n_samples, n_classes,)` with the predicted</span>
<span class="sd"> prevalence values</span> <span class="sd"> prevalence values</span>
<span class="sd"> :param eps: smoothing factor. NKLD is not defined in cases in which the distributions contain</span> <span class="sd"> :param eps: smoothing factor. NKLD is not defined in cases in which the distributions contain</span>
@ -250,10 +385,13 @@
<span class="sd"> (which has thus to be set beforehand).</span> <span class="sd"> (which has thus to be set beforehand).</span>
<span class="sd"> :return: mean Normalized Kullback-Leibler distribution</span> <span class="sd"> :return: mean Normalized Kullback-Leibler distribution</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">nkld</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div> <span class="k">return</span> <span class="n">nkld</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>
<div class="viewcode-block" id="nkld"><a class="viewcode-back" href="../../quapy.html#quapy.error.nkld">[docs]</a><span class="k">def</span> <span class="nf">nkld</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<div class="viewcode-block" id="nkld">
<a class="viewcode-back" href="../../quapy.html#quapy.error.nkld">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">nkld</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Computes the Normalized Kullback-Leibler divergence between the two prevalence distributions.</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;Computes the Normalized Kullback-Leibler divergence between the two prevalence distributions.</span>
<span class="sd"> Normalized Kullback-Leibler divergence between two prevalence distributions :math:`p` and</span> <span class="sd"> Normalized Kullback-Leibler divergence between two prevalence distributions :math:`p` and</span>
<span class="sd"> :math:`\\hat{p}` is computed as</span> <span class="sd"> :math:`\\hat{p}` is computed as</span>
@ -262,7 +400,7 @@
<span class="sd"> :math:`\\mathcal{Y}` are the classes of interest.</span> <span class="sd"> :math:`\\mathcal{Y}` are the classes of interest.</span>
<span class="sd"> The distributions are smoothed using the `eps` factor (see :meth:`quapy.error.smooth`).</span> <span class="sd"> The distributions are smoothed using the `eps` factor (see :meth:`quapy.error.smooth`).</span>
<span class="sd"> :param prevs: array-like of shape `(n_classes,)` with the true prevalence values</span> <span class="sd"> :param prevs_true: array-like of shape `(n_classes,)` with the true prevalence values</span>
<span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` with the predicted prevalence values</span> <span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` with the predicted prevalence values</span>
<span class="sd"> :param eps: smoothing factor. NKLD is not defined in cases in which the distributions</span> <span class="sd"> :param eps: smoothing factor. NKLD is not defined in cases in which the distributions</span>
<span class="sd"> contain zeros; `eps` is typically set to be :math:`\\frac{1}{2T}`, with :math:`T` the sample</span> <span class="sd"> contain zeros; `eps` is typically set to be :math:`\\frac{1}{2T}`, with :math:`T` the sample</span>
@ -270,16 +408,19 @@
<span class="sd"> `SAMPLE_SIZE` (which has thus to be set beforehand).</span> <span class="sd"> `SAMPLE_SIZE` (which has thus to be set beforehand).</span>
<span class="sd"> :return: Normalized Kullback-Leibler divergence between the two distributions</span> <span class="sd"> :return: Normalized Kullback-Leibler divergence between the two distributions</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="n">ekld</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">kld</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">))</span> <span class="n">ekld</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">kld</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">))</span>
<span class="k">return</span> <span class="mf">2.</span> <span class="o">*</span> <span class="n">ekld</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">ekld</span><span class="p">)</span> <span class="o">-</span> <span class="mf">1.</span></div> <span class="k">return</span> <span class="mf">2.</span> <span class="o">*</span> <span class="n">ekld</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">ekld</span><span class="p">)</span> <span class="o">-</span> <span class="mf">1.</span></div>
<div class="viewcode-block" id="mrae"><a class="viewcode-back" href="../../quapy.html#quapy.error.mrae">[docs]</a><span class="k">def</span> <span class="nf">mrae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<div class="viewcode-block" id="mrae">
<a class="viewcode-back" href="../../quapy.html#quapy.error.mrae">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">mrae</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Computes the mean relative absolute error (see :meth:`quapy.error.rae`) across</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;Computes the mean relative absolute error (see :meth:`quapy.error.rae`) across</span>
<span class="sd"> the sample pairs. The distributions are smoothed using the `eps` factor (see</span> <span class="sd"> the sample pairs. The distributions are smoothed using the `eps` factor (see</span>
<span class="sd"> :meth:`quapy.error.smooth`).</span> <span class="sd"> :meth:`quapy.error.smooth`).</span>
<span class="sd"> :param prevs: array-like of shape `(n_samples, n_classes,)` with the true</span> <span class="sd"> :param prevs_true: array-like of shape `(n_samples, n_classes,)` with the true</span>
<span class="sd"> prevalence values</span> <span class="sd"> prevalence values</span>
<span class="sd"> :param prevs_hat: array-like of shape `(n_samples, n_classes,)` with the predicted</span> <span class="sd"> :param prevs_hat: array-like of shape `(n_samples, n_classes,)` with the predicted</span>
<span class="sd"> prevalence values</span> <span class="sd"> prevalence values</span>
@ -289,10 +430,13 @@
<span class="sd"> the environment variable `SAMPLE_SIZE` (which has thus to be set beforehand).</span> <span class="sd"> the environment variable `SAMPLE_SIZE` (which has thus to be set beforehand).</span>
<span class="sd"> :return: mean relative absolute error</span> <span class="sd"> :return: mean relative absolute error</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">rae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div> <span class="k">return</span> <span class="n">rae</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>
<div class="viewcode-block" id="rae"><a class="viewcode-back" href="../../quapy.html#quapy.error.rae">[docs]</a><span class="k">def</span> <span class="nf">rae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<div class="viewcode-block" id="rae">
<a class="viewcode-back" href="../../quapy.html#quapy.error.rae">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">rae</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Computes the absolute relative error between the two prevalence vectors.</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;Computes the absolute relative error between the two prevalence vectors.</span>
<span class="sd"> Relative absolute error between two prevalence vectors :math:`p` and :math:`\\hat{p}`</span> <span class="sd"> Relative absolute error between two prevalence vectors :math:`p` and :math:`\\hat{p}`</span>
<span class="sd"> is computed as</span> <span class="sd"> is computed as</span>
@ -301,7 +445,7 @@
<span class="sd"> where :math:`\\mathcal{Y}` are the classes of interest.</span> <span class="sd"> where :math:`\\mathcal{Y}` are the classes of interest.</span>
<span class="sd"> The distributions are smoothed using the `eps` factor (see :meth:`quapy.error.smooth`).</span> <span class="sd"> The distributions are smoothed using the `eps` factor (see :meth:`quapy.error.smooth`).</span>
<span class="sd"> :param prevs: array-like of shape `(n_classes,)` with the true prevalence values</span> <span class="sd"> :param prevs_true: array-like of shape `(n_classes,)` with the true prevalence values</span>
<span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` with the predicted prevalence values</span> <span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` with the predicted prevalence values</span>
<span class="sd"> :param eps: smoothing factor. `rae` is not defined in cases in which the true distribution</span> <span class="sd"> :param eps: smoothing factor. `rae` is not defined in cases in which the true distribution</span>
<span class="sd"> contains zeros; `eps` is typically set to be :math:`\\frac{1}{2T}`, with :math:`T` the</span> <span class="sd"> contains zeros; `eps` is typically set to be :math:`\\frac{1}{2T}`, with :math:`T` the</span>
@ -310,12 +454,15 @@
<span class="sd"> :return: relative absolute error</span> <span class="sd"> :return: relative absolute error</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="n">eps</span> <span class="o">=</span> <span class="n">__check_eps</span><span class="p">(</span><span class="n">eps</span><span class="p">)</span> <span class="n">eps</span> <span class="o">=</span> <span class="n">__check_eps</span><span class="p">(</span><span class="n">eps</span><span class="p">)</span>
<span class="n">prevs</span> <span class="o">=</span> <span class="n">smooth</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span> <span class="n">prevs_true</span> <span class="o">=</span> <span class="n">smooth</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span>
<span class="n">prevs_hat</span> <span class="o">=</span> <span class="n">smooth</span><span class="p">(</span><span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span> <span class="n">prevs_hat</span> <span class="o">=</span> <span class="n">smooth</span><span class="p">(</span><span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span>
<span class="k">return</span> <span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">prevs</span> <span class="o">-</span> <span class="n">prevs_hat</span><span class="p">)</span> <span class="o">/</span> <span class="n">prevs</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></div> <span class="k">return</span> <span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">prevs_true</span> <span class="o">-</span> <span class="n">prevs_hat</span><span class="p">)</span> <span class="o">/</span> <span class="n">prevs_true</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></div>
<div class="viewcode-block" id="nrae"><a class="viewcode-back" href="../../quapy.html#quapy.error.nrae">[docs]</a><span class="k">def</span> <span class="nf">nrae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<div class="viewcode-block" id="nrae">
<a class="viewcode-back" href="../../quapy.html#quapy.error.nrae">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">nrae</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Computes the normalized absolute relative error between the two prevalence vectors.</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;Computes the normalized absolute relative error between the two prevalence vectors.</span>
<span class="sd"> Relative absolute error between two prevalence vectors :math:`p` and :math:`\\hat{p}`</span> <span class="sd"> Relative absolute error between two prevalence vectors :math:`p` and :math:`\\hat{p}`</span>
<span class="sd"> is computed as</span> <span class="sd"> is computed as</span>
@ -325,7 +472,7 @@
<span class="sd"> and :math:`\\mathcal{Y}` are the classes of interest.</span> <span class="sd"> and :math:`\\mathcal{Y}` are the classes of interest.</span>
<span class="sd"> The distributions are smoothed using the `eps` factor (see :meth:`quapy.error.smooth`).</span> <span class="sd"> The distributions are smoothed using the `eps` factor (see :meth:`quapy.error.smooth`).</span>
<span class="sd"> :param prevs: array-like of shape `(n_classes,)` with the true prevalence values</span> <span class="sd"> :param prevs_true: array-like of shape `(n_classes,)` with the true prevalence values</span>
<span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` with the predicted prevalence values</span> <span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` with the predicted prevalence values</span>
<span class="sd"> :param eps: smoothing factor. `nrae` is not defined in cases in which the true distribution</span> <span class="sd"> :param eps: smoothing factor. `nrae` is not defined in cases in which the true distribution</span>
<span class="sd"> contains zeros; `eps` is typically set to be :math:`\\frac{1}{2T}`, with :math:`T` the</span> <span class="sd"> contains zeros; `eps` is typically set to be :math:`\\frac{1}{2T}`, with :math:`T` the</span>
@ -334,18 +481,21 @@
<span class="sd"> :return: normalized relative absolute error</span> <span class="sd"> :return: normalized relative absolute error</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="n">eps</span> <span class="o">=</span> <span class="n">__check_eps</span><span class="p">(</span><span class="n">eps</span><span class="p">)</span> <span class="n">eps</span> <span class="o">=</span> <span class="n">__check_eps</span><span class="p">(</span><span class="n">eps</span><span class="p">)</span>
<span class="n">prevs</span> <span class="o">=</span> <span class="n">smooth</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span> <span class="n">prevs_true</span> <span class="o">=</span> <span class="n">smooth</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span>
<span class="n">prevs_hat</span> <span class="o">=</span> <span class="n">smooth</span><span class="p">(</span><span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span> <span class="n">prevs_hat</span> <span class="o">=</span> <span class="n">smooth</span><span class="p">(</span><span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span>
<span class="n">min_p</span> <span class="o">=</span> <span class="n">prevs</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span> <span class="n">min_p</span> <span class="o">=</span> <span class="n">prevs_true</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
<span class="k">return</span> <span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">prevs</span> <span class="o">-</span> <span class="n">prevs_hat</span><span class="p">)</span> <span class="o">/</span> <span class="n">prevs</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">prevs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="mi">1</span><span class="o">+</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">min_p</span><span class="p">)</span><span class="o">/</span><span class="n">min_p</span><span class="p">)</span></div> <span class="k">return</span> <span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">prevs_true</span> <span class="o">-</span> <span class="n">prevs_hat</span><span class="p">)</span> <span class="o">/</span> <span class="n">prevs_true</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">prevs_true</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">min_p</span><span class="p">)</span> <span class="o">/</span> <span class="n">min_p</span><span class="p">)</span></div>
<div class="viewcode-block" id="mnrae"><a class="viewcode-back" href="../../quapy.html#quapy.error.mnrae">[docs]</a><span class="k">def</span> <span class="nf">mnrae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<div class="viewcode-block" id="mnrae">
<a class="viewcode-back" href="../../quapy.html#quapy.error.mnrae">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">mnrae</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Computes the mean normalized relative absolute error (see :meth:`quapy.error.nrae`) across</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;Computes the mean normalized relative absolute error (see :meth:`quapy.error.nrae`) across</span>
<span class="sd"> the sample pairs. The distributions are smoothed using the `eps` factor (see</span> <span class="sd"> the sample pairs. The distributions are smoothed using the `eps` factor (see</span>
<span class="sd"> :meth:`quapy.error.smooth`).</span> <span class="sd"> :meth:`quapy.error.smooth`).</span>
<span class="sd"> :param prevs: array-like of shape `(n_samples, n_classes,)` with the true</span> <span class="sd"> :param prevs_true: array-like of shape `(n_samples, n_classes,)` with the true</span>
<span class="sd"> prevalence values</span> <span class="sd"> prevalence values</span>
<span class="sd"> :param prevs_hat: array-like of shape `(n_samples, n_classes,)` with the predicted</span> <span class="sd"> :param prevs_hat: array-like of shape `(n_samples, n_classes,)` with the predicted</span>
<span class="sd"> prevalence values</span> <span class="sd"> prevalence values</span>
@ -355,10 +505,84 @@
<span class="sd"> the environment variable `SAMPLE_SIZE` (which has thus to be set beforehand).</span> <span class="sd"> the environment variable `SAMPLE_SIZE` (which has thus to be set beforehand).</span>
<span class="sd"> :return: mean normalized relative absolute error</span> <span class="sd"> :return: mean normalized relative absolute error</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">nrae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div> <span class="k">return</span> <span class="n">nrae</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>
<div class="viewcode-block" id="smooth"><a class="viewcode-back" href="../../quapy.html#quapy.error.smooth">[docs]</a><span class="k">def</span> <span class="nf">smooth</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">eps</span><span class="p">):</span>
<div class="viewcode-block" id="nmd">
<a class="viewcode-back" href="../../quapy.html#quapy.error.nmd">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">nmd</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Computes the Normalized Match Distance; which is the Normalized Distance multiplied by the factor</span>
<span class="sd"> `1/(n-1)` to guarantee the measure ranges between 0 (best prediction) and 1 (worst prediction).</span>
<span class="sd"> :param prevs_true: array-like of shape `(n_classes,)` or `(n_instances, n_classes)` with the true prevalence values</span>
<span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` or `(n_instances, n_classes)` with the predicted prevalence values</span>
<span class="sd"> :return: float in [0,1]</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">prevs_true</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">)</span>
<span class="n">prevs_hat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevs_hat</span><span class="p">)</span>
<span class="n">n</span> <span class="o">=</span> <span class="n">prevs_true</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="k">return</span> <span class="p">(</span><span class="mf">1.</span><span class="o">/</span><span class="p">(</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">match_distance</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">))</span></div>
<div class="viewcode-block" id="bias_binary">
<a class="viewcode-back" href="../../quapy.html#quapy.error.bias_binary">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">bias_binary</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Computes the (positive) bias in a binary problem. The bias is simply the difference between the</span>
<span class="sd"> predicted positive value and the true positive value, so that a positive such value indicates the</span>
<span class="sd"> prediction has positive bias (i.e., it tends to overestimate) the true value, and negative otherwise.</span>
<span class="sd"> :math:`bias(p,\\hat{p})=\\hat{p}_1-p_1`,</span>
<span class="sd"> :param prevs_true: array-like of shape `(n_samples, n_classes,)` with the true prevalence values</span>
<span class="sd"> :param prevs_hat: array-like of shape `(n_samples, n_classes,)` with the predicted</span>
<span class="sd"> prevalence values</span>
<span class="sd"> :return: binary bias</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">prevs_true</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">)</span>
<span class="n">prevs_hat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevs_hat</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">prevs_true</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span> <span class="ow">and</span> <span class="n">prevs_true</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;bias_binary can only be applied to binary problems&#39;</span>
<span class="k">return</span> <span class="n">prevs_hat</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">prevs_true</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span></div>
<div class="viewcode-block" id="mean_bias_binary">
<a class="viewcode-back" href="../../quapy.html#quapy.error.mean_bias_binary">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">mean_bias_binary</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Computes the mean of the (positive) bias in a binary problem.</span>
<span class="sd"> :param prevs_true: array-like of shape `(n_classes,)` with the true prevalence values</span>
<span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` with the predicted prevalence values</span>
<span class="sd"> :return: mean binary bias</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">bias_binary</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">))</span></div>
<div class="viewcode-block" id="md">
<a class="viewcode-back" href="../../quapy.html#quapy.error.md">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">md</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">ERROR_TOL</span><span class="o">=</span><span class="mf">1E-3</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Computes the Match Distance, under the assumption that the cost in mistaking class i with class i+1 is 1 in</span>
<span class="sd"> all cases.</span>
<span class="sd"> :param prevs_true: array-like of shape `(n_classes,)` or `(n_instances, n_classes)` with the true prevalence values</span>
<span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` or `(n_instances, n_classes)` with the predicted prevalence values</span>
<span class="sd"> :return: float</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">P</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
<span class="n">P_hat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">prevs_hat</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">isclose</span><span class="p">(</span><span class="n">P_hat</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="mf">1.0</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="n">ERROR_TOL</span><span class="p">)),</span> \
<span class="s1">&#39;arg error in match_distance: the array does not represent a valid distribution&#39;</span>
<span class="n">distances</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">P</span><span class="o">-</span><span class="n">P_hat</span><span class="p">)</span>
<span class="k">return</span> <span class="n">distances</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></div>
<div class="viewcode-block" id="smooth">
<a class="viewcode-back" href="../../quapy.html#quapy.error.smooth">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">smooth</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">eps</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot; Smooths a prevalence distribution with :math:`\\epsilon` (`eps`) as:</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot; Smooths a prevalence distribution with :math:`\\epsilon` (`eps`) as:</span>
<span class="sd"> :math:`\\underline{p}(y)=\\frac{\\epsilon+p(y)}{\\epsilon|\\mathcal{Y}|+</span> <span class="sd"> :math:`\\underline{p}(y)=\\frac{\\epsilon+p(y)}{\\epsilon|\\mathcal{Y}|+</span>
<span class="sd"> \\displaystyle\\sum_{y\\in \\mathcal{Y}}p(y)}`</span> <span class="sd"> \\displaystyle\\sum_{y\\in \\mathcal{Y}}p(y)}`</span>
@ -367,11 +591,13 @@
<span class="sd"> :param eps: smoothing factor</span> <span class="sd"> :param eps: smoothing factor</span>
<span class="sd"> :return: array-like of shape `(n_classes,)` with the smoothed distribution</span> <span class="sd"> :return: array-like of shape `(n_classes,)` with the smoothed distribution</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="n">prevs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevs</span><span class="p">)</span>
<span class="n">n_classes</span> <span class="o">=</span> <span class="n">prevs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="n">n_classes</span> <span class="o">=</span> <span class="n">prevs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="k">return</span> <span class="p">(</span><span class="n">prevs</span> <span class="o">+</span> <span class="n">eps</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">eps</span> <span class="o">*</span> <span class="n">n_classes</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span></div> <span class="k">return</span> <span class="p">(</span><span class="n">prevs</span> <span class="o">+</span> <span class="n">eps</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">eps</span> <span class="o">*</span> <span class="n">n_classes</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">__check_eps</span><span class="p">(</span><span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">__check_eps</span><span class="p">(</span><span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">if</span> <span class="n">eps</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="k">if</span> <span class="n">eps</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">sample_size</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;SAMPLE_SIZE&#39;</span><span class="p">]</span> <span class="n">sample_size</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;SAMPLE_SIZE&#39;</span><span class="p">]</span>
<span class="k">if</span> <span class="n">sample_size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="k">if</span> <span class="n">sample_size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
@ -381,8 +607,8 @@
<span class="n">CLASSIFICATION_ERROR</span> <span class="o">=</span> <span class="p">{</span><span class="n">f1e</span><span class="p">,</span> <span class="n">acce</span><span class="p">}</span> <span class="n">CLASSIFICATION_ERROR</span> <span class="o">=</span> <span class="p">{</span><span class="n">f1e</span><span class="p">,</span> <span class="n">acce</span><span class="p">}</span>
<span class="n">QUANTIFICATION_ERROR</span> <span class="o">=</span> <span class="p">{</span><span class="n">mae</span><span class="p">,</span> <span class="n">mnae</span><span class="p">,</span> <span class="n">mrae</span><span class="p">,</span> <span class="n">mnrae</span><span class="p">,</span> <span class="n">mse</span><span class="p">,</span> <span class="n">mkld</span><span class="p">,</span> <span class="n">mnkld</span><span class="p">}</span> <span class="n">QUANTIFICATION_ERROR</span> <span class="o">=</span> <span class="p">{</span><span class="n">mae</span><span class="p">,</span> <span class="n">mnae</span><span class="p">,</span> <span class="n">mrae</span><span class="p">,</span> <span class="n">mnrae</span><span class="p">,</span> <span class="n">mse</span><span class="p">,</span> <span class="n">mkld</span><span class="p">,</span> <span class="n">mnkld</span><span class="p">,</span> <span class="n">msre</span><span class="p">,</span> <span class="n">maitchisondist</span><span class="p">}</span>
<span class="n">QUANTIFICATION_ERROR_SINGLE</span> <span class="o">=</span> <span class="p">{</span><span class="n">ae</span><span class="p">,</span> <span class="n">nae</span><span class="p">,</span> <span class="n">rae</span><span class="p">,</span> <span class="n">nrae</span><span class="p">,</span> <span class="n">se</span><span class="p">,</span> <span class="n">kld</span><span class="p">,</span> <span class="n">nkld</span><span class="p">}</span> <span class="n">QUANTIFICATION_ERROR_SINGLE</span> <span class="o">=</span> <span class="p">{</span><span class="n">ae</span><span class="p">,</span> <span class="n">nae</span><span class="p">,</span> <span class="n">rae</span><span class="p">,</span> <span class="n">nrae</span><span class="p">,</span> <span class="n">se</span><span class="p">,</span> <span class="n">kld</span><span class="p">,</span> <span class="n">nkld</span><span class="p">,</span> <span class="n">sre</span><span class="p">,</span> <span class="n">aitchisondist</span><span class="p">}</span>
<span class="n">QUANTIFICATION_ERROR_SMOOTH</span> <span class="o">=</span> <span class="p">{</span><span class="n">kld</span><span class="p">,</span> <span class="n">nkld</span><span class="p">,</span> <span class="n">rae</span><span class="p">,</span> <span class="n">nrae</span><span class="p">,</span> <span class="n">mkld</span><span class="p">,</span> <span class="n">mnkld</span><span class="p">,</span> <span class="n">mrae</span><span class="p">}</span> <span class="n">QUANTIFICATION_ERROR_SMOOTH</span> <span class="o">=</span> <span class="p">{</span><span class="n">kld</span><span class="p">,</span> <span class="n">nkld</span><span class="p">,</span> <span class="n">rae</span><span class="p">,</span> <span class="n">nrae</span><span class="p">,</span> <span class="n">mkld</span><span class="p">,</span> <span class="n">mnkld</span><span class="p">,</span> <span class="n">mrae</span><span class="p">}</span>
<span class="n">CLASSIFICATION_ERROR_NAMES</span> <span class="o">=</span> <span class="p">{</span><span class="n">func</span><span class="o">.</span><span class="vm">__name__</span> <span class="k">for</span> <span class="n">func</span> <span class="ow">in</span> <span class="n">CLASSIFICATION_ERROR</span><span class="p">}</span> <span class="n">CLASSIFICATION_ERROR_NAMES</span> <span class="o">=</span> <span class="p">{</span><span class="n">func</span><span class="o">.</span><span class="vm">__name__</span> <span class="k">for</span> <span class="n">func</span> <span class="ow">in</span> <span class="n">CLASSIFICATION_ERROR</span><span class="p">}</span>
<span class="n">QUANTIFICATION_ERROR_NAMES</span> <span class="o">=</span> <span class="p">{</span><span class="n">func</span><span class="o">.</span><span class="vm">__name__</span> <span class="k">for</span> <span class="n">func</span> <span class="ow">in</span> <span class="n">QUANTIFICATION_ERROR</span><span class="p">}</span> <span class="n">QUANTIFICATION_ERROR_NAMES</span> <span class="o">=</span> <span class="p">{</span><span class="n">func</span><span class="o">.</span><span class="vm">__name__</span> <span class="k">for</span> <span class="n">func</span> <span class="ow">in</span> <span class="n">QUANTIFICATION_ERROR</span><span class="p">}</span>
@ -394,6 +620,9 @@
<span class="n">f1_error</span> <span class="o">=</span> <span class="n">f1e</span> <span class="n">f1_error</span> <span class="o">=</span> <span class="n">f1e</span>
<span class="n">acc_error</span> <span class="o">=</span> <span class="n">acce</span> <span class="n">acc_error</span> <span class="o">=</span> <span class="n">acce</span>
<span class="n">mean_absolute_error</span> <span class="o">=</span> <span class="n">mae</span> <span class="n">mean_absolute_error</span> <span class="o">=</span> <span class="n">mae</span>
<span class="n">squared_ratio_error</span> <span class="o">=</span> <span class="n">sre</span>
<span class="n">dist_aitchison</span> <span class="o">=</span> <span class="n">aitchisondist</span>
<span class="n">mean_dist_aitchison</span> <span class="o">=</span> <span class="n">maitchisondist</span>
<span class="n">absolute_error</span> <span class="o">=</span> <span class="n">ae</span> <span class="n">absolute_error</span> <span class="o">=</span> <span class="n">ae</span>
<span class="n">mean_relative_absolute_error</span> <span class="o">=</span> <span class="n">mrae</span> <span class="n">mean_relative_absolute_error</span> <span class="o">=</span> <span class="n">mrae</span>
<span class="n">relative_absolute_error</span> <span class="o">=</span> <span class="n">rae</span> <span class="n">relative_absolute_error</span> <span class="o">=</span> <span class="n">rae</span>
@ -401,6 +630,8 @@
<span class="n">normalized_relative_absolute_error</span> <span class="o">=</span> <span class="n">nrae</span> <span class="n">normalized_relative_absolute_error</span> <span class="o">=</span> <span class="n">nrae</span>
<span class="n">mean_normalized_absolute_error</span> <span class="o">=</span> <span class="n">mnae</span> <span class="n">mean_normalized_absolute_error</span> <span class="o">=</span> <span class="n">mnae</span>
<span class="n">mean_normalized_relative_absolute_error</span> <span class="o">=</span> <span class="n">mnrae</span> <span class="n">mean_normalized_relative_absolute_error</span> <span class="o">=</span> <span class="n">mnrae</span>
<span class="n">normalized_match_distance</span> <span class="o">=</span> <span class="n">nmd</span>
<span class="n">match_distance</span> <span class="o">=</span> <span class="n">md</span>
</pre></div> </pre></div>
</div> </div>

75
docs/build/html/_modules/quapy/evaluation.html vendored
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@ -1,23 +1,20 @@
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@ -43,7 +40,13 @@
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@ -71,16 +74,18 @@
<div itemprop="articleBody"> <div itemprop="articleBody">
<h1>Source code for quapy.evaluation</h1><div class="highlight"><pre> <h1>Source code for quapy.evaluation</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Iterable</span> <span></span><span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Iterable</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span> <span class="kn">from</span><span class="w"> </span><span class="nn">tqdm</span><span class="w"> </span><span class="kn">import</span> <span class="n">tqdm</span>
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span> <span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
<span class="kn">from</span> <span class="nn">quapy.protocol</span> <span class="kn">import</span> <span class="n">AbstractProtocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span><span class="p">,</span> <span class="n">IterateProtocol</span> <span class="kn">from</span><span class="w"> </span><span class="nn">quapy.protocol</span><span class="w"> </span><span class="kn">import</span> <span class="n">AbstractProtocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span><span class="p">,</span> <span class="n">IterateProtocol</span>
<span class="kn">from</span> <span class="nn">quapy.method.base</span> <span class="kn">import</span> <span class="n">BaseQuantifier</span> <span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseQuantifier</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> <span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
<div class="viewcode-block" id="prediction"><a class="viewcode-back" href="../../quapy.html#quapy.evaluation.prediction">[docs]</a><span class="k">def</span> <span class="nf">prediction</span><span class="p">(</span> <div class="viewcode-block" id="prediction">
<a class="viewcode-back" href="../../quapy.html#quapy.evaluation.prediction">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">prediction</span><span class="p">(</span>
<span class="n">model</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">model</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span>
<span class="n">protocol</span><span class="p">:</span> <span class="n">AbstractProtocol</span><span class="p">,</span> <span class="n">protocol</span><span class="p">:</span> <span class="n">AbstractProtocol</span><span class="p">,</span>
<span class="n">aggr_speedup</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;auto&#39;</span><span class="p">,</span> <span class="n">aggr_speedup</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;auto&#39;</span><span class="p">,</span>
@ -118,7 +123,7 @@
<span class="c1"># checks whether the prediction can be made more efficiently; this check consists in verifying if the model is</span> <span class="c1"># checks whether the prediction can be made more efficiently; this check consists in verifying if the model is</span>
<span class="c1"># of type aggregative, if the protocol is based on LabelledCollection, and if the total number of documents to</span> <span class="c1"># of type aggregative, if the protocol is based on LabelledCollection, and if the total number of documents to</span>
<span class="c1"># classify using the protocol would exceed the number of test documents in the original collection</span> <span class="c1"># classify using the protocol would exceed the number of test documents in the original collection</span>
<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="n">AggregativeQuantifier</span> <span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.aggregative</span><span class="w"> </span><span class="kn">import</span> <span class="n">AggregativeQuantifier</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">AggregativeQuantifier</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">protocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">AggregativeQuantifier</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">protocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
<span class="k">if</span> <span class="n">aggr_speedup</span> <span class="o">==</span> <span class="s1">&#39;force&#39;</span><span class="p">:</span> <span class="k">if</span> <span class="n">aggr_speedup</span> <span class="o">==</span> <span class="s1">&#39;force&#39;</span><span class="p">:</span>
<span class="n">apply_optimization</span> <span class="o">=</span> <span class="kc">True</span> <span class="n">apply_optimization</span> <span class="o">=</span> <span class="kc">True</span>
@ -136,10 +141,11 @@
<span class="n">protocol_with_predictions</span> <span class="o">=</span> <span class="n">protocol</span><span class="o">.</span><span class="n">on_preclassified_instances</span><span class="p">(</span><span class="n">pre_classified</span><span class="p">)</span> <span class="n">protocol_with_predictions</span> <span class="o">=</span> <span class="n">protocol</span><span class="o">.</span><span class="n">on_preclassified_instances</span><span class="p">(</span><span class="n">pre_classified</span><span class="p">)</span>
<span class="k">return</span> <span class="n">__prediction_helper</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">aggregate</span><span class="p">,</span> <span class="n">protocol_with_predictions</span><span class="p">,</span> <span class="n">verbose</span><span class="p">)</span> <span class="k">return</span> <span class="n">__prediction_helper</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">aggregate</span><span class="p">,</span> <span class="n">protocol_with_predictions</span><span class="p">,</span> <span class="n">verbose</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span> <span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">__prediction_helper</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">,</span> <span class="n">protocol</span><span class="p">,</span> <span class="n">verbose</span><span class="p">)</span></div> <span class="k">return</span> <span class="n">__prediction_helper</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">,</span> <span class="n">protocol</span><span class="p">,</span> <span class="n">verbose</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">__prediction_helper</span><span class="p">(</span><span class="n">quantification_fn</span><span class="p">,</span> <span class="n">protocol</span><span class="p">:</span> <span class="n">AbstractProtocol</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">__prediction_helper</span><span class="p">(</span><span class="n">quantification_fn</span><span class="p">,</span> <span class="n">protocol</span><span class="p">:</span> <span class="n">AbstractProtocol</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">sample_instances</span><span class="p">,</span> <span class="n">sample_prev</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">protocol</span><span class="p">(),</span> <span class="n">total</span><span class="o">=</span><span class="n">protocol</span><span class="o">.</span><span class="n">total</span><span class="p">(),</span> <span class="n">desc</span><span class="o">=</span><span class="s1">&#39;predicting&#39;</span><span class="p">)</span> <span class="k">if</span> <span class="n">verbose</span> <span class="k">else</span> <span class="n">protocol</span><span class="p">():</span> <span class="k">for</span> <span class="n">sample_instances</span><span class="p">,</span> <span class="n">sample_prev</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">protocol</span><span class="p">(),</span> <span class="n">total</span><span class="o">=</span><span class="n">protocol</span><span class="o">.</span><span class="n">total</span><span class="p">(),</span> <span class="n">desc</span><span class="o">=</span><span class="s1">&#39;predicting&#39;</span><span class="p">)</span> <span class="k">if</span> <span class="n">verbose</span> <span class="k">else</span> <span class="n">protocol</span><span class="p">():</span>
<span class="n">estim_prevs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">quantification_fn</span><span class="p">(</span><span class="n">sample_instances</span><span class="p">))</span> <span class="n">estim_prevs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">quantification_fn</span><span class="p">(</span><span class="n">sample_instances</span><span class="p">))</span>
@ -151,7 +157,9 @@
<span class="k">return</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span> <span class="k">return</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span>
<div class="viewcode-block" id="evaluation_report"><a class="viewcode-back" href="../../quapy.html#quapy.evaluation.evaluation_report">[docs]</a><span class="k">def</span> <span class="nf">evaluation_report</span><span class="p">(</span><span class="n">model</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <div class="viewcode-block" id="evaluation_report">
<a class="viewcode-back" href="../../quapy.html#quapy.evaluation.evaluation_report">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">evaluation_report</span><span class="p">(</span><span class="n">model</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span>
<span class="n">protocol</span><span class="p">:</span> <span class="n">AbstractProtocol</span><span class="p">,</span> <span class="n">protocol</span><span class="p">:</span> <span class="n">AbstractProtocol</span><span class="p">,</span>
<span class="n">error_metrics</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span><span class="n">Callable</span><span class="p">]]</span> <span class="o">=</span> <span class="s1">&#39;mae&#39;</span><span class="p">,</span> <span class="n">error_metrics</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span><span class="n">Callable</span><span class="p">]]</span> <span class="o">=</span> <span class="s1">&#39;mae&#39;</span><span class="p">,</span>
<span class="n">aggr_speedup</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;auto&#39;</span><span class="p">,</span> <span class="n">aggr_speedup</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;auto&#39;</span><span class="p">,</span>
@ -182,7 +190,8 @@
<span class="k">return</span> <span class="n">_prevalence_report</span><span class="p">(</span><span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">error_metrics</span><span class="p">)</span></div> <span class="k">return</span> <span class="n">_prevalence_report</span><span class="p">(</span><span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">error_metrics</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">_prevalence_report</span><span class="p">(</span><span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">error_metrics</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">]]</span> <span class="o">=</span> <span class="s1">&#39;mae&#39;</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_prevalence_report</span><span class="p">(</span><span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">error_metrics</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">]]</span> <span class="o">=</span> <span class="s1">&#39;mae&#39;</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">error_metrics</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">error_metrics</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
<span class="n">error_metrics</span> <span class="o">=</span> <span class="p">[</span><span class="n">error_metrics</span><span class="p">]</span> <span class="n">error_metrics</span> <span class="o">=</span> <span class="p">[</span><span class="n">error_metrics</span><span class="p">]</span>
@ -203,7 +212,9 @@
<span class="k">return</span> <span class="n">df</span> <span class="k">return</span> <span class="n">df</span>
<div class="viewcode-block" id="evaluate"><a class="viewcode-back" href="../../quapy.html#quapy.evaluation.evaluate">[docs]</a><span class="k">def</span> <span class="nf">evaluate</span><span class="p">(</span> <div class="viewcode-block" id="evaluate">
<a class="viewcode-back" href="../../quapy.html#quapy.evaluation.evaluate">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">evaluate</span><span class="p">(</span>
<span class="n">model</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">model</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span>
<span class="n">protocol</span><span class="p">:</span> <span class="n">AbstractProtocol</span><span class="p">,</span> <span class="n">protocol</span><span class="p">:</span> <span class="n">AbstractProtocol</span><span class="p">,</span>
<span class="n">error_metric</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">],</span> <span class="n">error_metric</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">],</span>
@ -235,7 +246,10 @@
<span class="k">return</span> <span class="n">error_metric</span><span class="p">(</span><span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">)</span></div> <span class="k">return</span> <span class="n">error_metric</span><span class="p">(</span><span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">)</span></div>
<div class="viewcode-block" id="evaluate_on_samples"><a class="viewcode-back" href="../../quapy.html#quapy.evaluation.evaluate_on_samples">[docs]</a><span class="k">def</span> <span class="nf">evaluate_on_samples</span><span class="p">(</span>
<div class="viewcode-block" id="evaluate_on_samples">
<a class="viewcode-back" href="../../quapy.html#quapy.evaluation.evaluate_on_samples">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">evaluate_on_samples</span><span class="p">(</span>
<span class="n">model</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">model</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span>
<span class="n">samples</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="p">],</span> <span class="n">samples</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="p">],</span>
<span class="n">error_metric</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">],</span> <span class="n">error_metric</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">],</span>
@ -259,6 +273,7 @@
</pre></div> </pre></div>
</div> </div>

951
docs/build/html/_modules/quapy/functional.html vendored
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342
docs/build/html/_modules/quapy/method/_kdey.html vendored
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@ -1,23 +1,20 @@
<!DOCTYPE html> <!DOCTYPE html>
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<title>quapy.method._kdey &mdash; QuaPy: A Python-based open-source framework for quantification 0.1.8 documentation</title> <title>quapy.method._kdey &mdash; QuaPy: A Python-based open-source framework for quantification 0.2.1 documentation</title>
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@ -43,7 +40,13 @@
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@ -71,61 +74,85 @@
<div itemprop="articleBody"> <div itemprop="articleBody">
<h1>Source code for quapy.method._kdey</h1><div class="highlight"><pre> <h1>Source code for quapy.method._kdey</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span> <span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span class="kn">from</span><span class="w"> </span><span class="nn">numbers</span><span class="w"> </span><span class="kn">import</span> <span class="n">Real</span>
<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span> <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseEstimator</span>
<span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">KernelDensity</span> <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.neighbors</span><span class="w"> </span><span class="kn">import</span> <span class="n">KernelDensity</span>
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span> <span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
<span class="kn">from</span> <span class="nn">quapy.data</span> <span class="kn">import</span> <span class="n">LabelledCollection</span> <span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.aggregative</span><span class="w"> </span><span class="kn">import</span> <span class="n">AggregativeSoftQuantifier</span>
<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="n">AggregativeSoftQuantifier</span> <span class="kn">import</span><span class="w"> </span><span class="nn">quapy.functional</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">F</span>
<span class="kn">import</span> <span class="nn">quapy.functional</span> <span class="k">as</span> <span class="nn">F</span> <span class="kn">from</span><span class="w"> </span><span class="nn">scipy.special</span><span class="w"> </span><span class="kn">import</span> <span class="n">logsumexp</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics.pairwise</span><span class="w"> </span><span class="kn">import</span> <span class="n">rbf_kernel</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics.pairwise</span> <span class="kn">import</span> <span class="n">rbf_kernel</span>
<div class="viewcode-block" id="KDEBase"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase">[docs]</a><span class="k">class</span> <span class="nc">KDEBase</span><span class="p">:</span> <div class="viewcode-block" id="KDEBase">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">KDEBase</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Common ancestor for KDE-based methods. Implements some common routines.</span> <span class="sd"> Common ancestor for KDE-based methods. Implements some common routines.</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="n">BANDWIDTH_METHOD</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;scott&#39;</span><span class="p">,</span> <span class="s1">&#39;silverman&#39;</span><span class="p">]</span> <span class="n">BANDWIDTH_METHOD</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;scott&#39;</span><span class="p">,</span> <span class="s1">&#39;silverman&#39;</span><span class="p">]</span>
<span class="n">KERNELS</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;gaussian&#39;</span><span class="p">,</span> <span class="s1">&#39;aitchison&#39;</span><span class="p">,</span> <span class="s1">&#39;ilr&#39;</span><span class="p">]</span>
<span class="nd">@classmethod</span> <span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">_check_bandwidth</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">bandwidth</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">_check_bandwidth</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">bandwidth</span><span class="p">,</span> <span class="n">kernel</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Checks that the bandwidth parameter is correct</span> <span class="sd"> Checks that the bandwidth parameter is correct</span>
<span class="sd"> :param bandwidth: either a string (see BANDWIDTH_METHOD) or a float</span> <span class="sd"> :param bandwidth: either a string (see BANDWIDTH_METHOD) or a float</span>
<span class="sd"> :return: nothing, but raises an exception for invalid values</span> <span class="sd"> :return: the bandwidth if the check is passed, or raises an exception for invalid values</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">assert</span> <span class="n">bandwidth</span> <span class="ow">in</span> <span class="n">KDEBase</span><span class="o">.</span><span class="n">BANDWIDTH_METHOD</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">,</span> <span class="nb">float</span><span class="p">),</span> \ <span class="k">assert</span> <span class="n">bandwidth</span> <span class="ow">in</span> <span class="n">KDEBase</span><span class="o">.</span><span class="n">BANDWIDTH_METHOD</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">,</span> <span class="n">Real</span><span class="p">),</span> \
<span class="sa">f</span><span class="s1">&#39;invalid bandwidth, valid ones are </span><span class="si">{</span><span class="n">KDEBase</span><span class="o">.</span><span class="n">BANDWIDTH_METHOD</span><span class="si">}</span><span class="s1"> or float values&#39;</span> <span class="sa">f</span><span class="s1">&#39;invalid bandwidth, valid ones are </span><span class="si">{</span><span class="n">KDEBase</span><span class="o">.</span><span class="n">BANDWIDTH_METHOD</span><span class="si">}</span><span class="s1"> or float values&#39;</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">,</span> <span class="nb">float</span><span class="p">):</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">,</span> <span class="n">Real</span><span class="p">):</span>
<span class="k">assert</span> <span class="mi">0</span> <span class="o">&lt;</span> <span class="n">bandwidth</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;the bandwith for KDEy should be in (0,1), since this method models the unit simplex&quot;</span> <span class="n">bandwidth</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">)</span>
<span class="k">return</span> <span class="n">bandwidth</span>
<div class="viewcode-block" id="KDEBase.get_kde_function"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.get_kde_function">[docs]</a> <span class="k">def</span> <span class="nf">get_kde_function</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">bandwidth</span><span class="p">):</span> <span class="nd">@classmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_check_kernel</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">kernel</span><span class="p">):</span>
<span class="k">assert</span> <span class="n">kernel</span> <span class="ow">in</span> <span class="n">KDEBase</span><span class="o">.</span><span class="n">KERNELS</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;unknown </span><span class="si">{</span><span class="n">kernel</span><span class="si">=}</span><span class="s1">&#39;</span>
<span class="k">return</span> <span class="n">kernel</span>
<div class="viewcode-block" id="KDEBase.get_kde_function">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.get_kde_function">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">get_kde_function</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">bandwidth</span><span class="p">,</span> <span class="n">kernel</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Wraps the KDE function from scikit-learn.</span> <span class="sd"> Wraps the KDE function from scikit-learn.</span>
<span class="sd"> :param X: data for which the density function is to be estimated</span> <span class="sd"> :param X: data for which the density function is to be estimated</span>
<span class="sd"> :param bandwidth: the bandwidth of the kernel</span> <span class="sd"> :param bandwidth: the bandwidth of the kernel</span>
<span class="sd"> :param kernel: the kernel family</span>
<span class="sd"> :return: a scikit-learn&#39;s KernelDensity object</span> <span class="sd"> :return: a scikit-learn&#39;s KernelDensity object</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform_posteriors</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">kernel</span><span class="p">)</span>
<span class="n">bandwidth</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">effective_bandwidth</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">,</span> <span class="n">kernel</span><span class="p">)</span>
<span class="k">return</span> <span class="n">KernelDensity</span><span class="p">(</span><span class="n">bandwidth</span><span class="o">=</span><span class="n">bandwidth</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div> <span class="k">return</span> <span class="n">KernelDensity</span><span class="p">(</span><span class="n">bandwidth</span><span class="o">=</span><span class="n">bandwidth</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
<div class="viewcode-block" id="KDEBase.pdf"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.pdf">[docs]</a> <span class="k">def</span> <span class="nf">pdf</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">kde</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<div class="viewcode-block" id="KDEBase.pdf">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.pdf">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">pdf</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">kde</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">kernel</span><span class="p">,</span> <span class="n">log_densities</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Wraps the density evalution of scikit-learn&#39;s KDE. Scikit-learn returns log-scores (s), so this</span> <span class="sd"> Wraps the density evalution of scikit-learn&#39;s KDE. Scikit-learn returns log-scores (s), so this</span>
<span class="sd"> function returns :math:`e^{s}`</span> <span class="sd"> function returns :math:`e^{s}`</span>
<span class="sd"> :param kde: a previously fit KDE function</span> <span class="sd"> :param kde: a previously fit KDE function</span>
<span class="sd"> :param X: the data for which the density is to be estimated</span> <span class="sd"> :param X: the data for which the density is to be estimated</span>
<span class="sd"> :param kernel: the kernel family</span>
<span class="sd"> :return: np.ndarray with the densities</span> <span class="sd"> :return: np.ndarray with the densities</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">kde</span><span class="o">.</span><span class="n">score_samples</span><span class="p">(</span><span class="n">X</span><span class="p">))</span></div> <span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform_posteriors</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">kernel</span><span class="p">)</span>
<span class="n">log_density</span> <span class="o">=</span> <span class="n">kde</span><span class="o">.</span><span class="n">score_samples</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="k">if</span> <span class="n">log_densities</span><span class="p">:</span>
<span class="k">return</span> <span class="n">log_density</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">log_density</span><span class="p">)</span></div>
<div class="viewcode-block" id="KDEBase.get_mixture_components"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.get_mixture_components">[docs]</a> <span class="k">def</span> <span class="nf">get_mixture_components</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">bandwidth</span><span class="p">):</span>
<div class="viewcode-block" id="KDEBase.get_mixture_components">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.get_mixture_components">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">get_mixture_components</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">classes</span><span class="p">,</span> <span class="n">bandwidth</span><span class="p">,</span> <span class="n">kernel</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns an array containing the mixture components, i.e., the KDE functions for each class.</span> <span class="sd"> Returns an array containing the mixture components, i.e., the KDE functions for each class.</span>
@ -133,13 +160,72 @@
<span class="sd"> :param y: the class labels</span> <span class="sd"> :param y: the class labels</span>
<span class="sd"> :param n_classes: integer, the number of classes</span> <span class="sd"> :param n_classes: integer, the number of classes</span>
<span class="sd"> :param bandwidth: float, the bandwidth of the kernel</span> <span class="sd"> :param bandwidth: float, the bandwidth of the kernel</span>
<span class="sd"> :param kernel: the kernel family</span>
<span class="sd"> :return: a list of KernelDensity objects, each fitted with the corresponding class-specific covariates</span> <span class="sd"> :return: a list of KernelDensity objects, each fitted with the corresponding class-specific covariates</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">get_kde_function</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">y</span> <span class="o">==</span> <span class="n">cat</span><span class="p">],</span> <span class="n">bandwidth</span><span class="p">)</span> <span class="k">for</span> <span class="n">cat</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_classes</span><span class="p">)]</span></div></div> <span class="n">class_cond_X</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">cat</span> <span class="ow">in</span> <span class="n">classes</span><span class="p">:</span>
<span class="n">selX</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">y</span><span class="o">==</span><span class="n">cat</span><span class="p">]</span>
<span class="k">if</span> <span class="n">selX</span><span class="o">.</span><span class="n">size</span><span class="o">==</span><span class="mi">0</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;empty class </span><span class="si">{</span><span class="n">cat</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="n">class_cond_X</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">selX</span><span class="p">)</span>
<span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">get_kde_function</span><span class="p">(</span><span class="n">X_cond_yi</span><span class="p">,</span> <span class="n">bandwidth</span><span class="p">,</span> <span class="n">kernel</span><span class="p">)</span> <span class="k">for</span> <span class="n">X_cond_yi</span> <span class="ow">in</span> <span class="n">class_cond_X</span><span class="p">]</span></div>
<div class="viewcode-block" id="KDEBase.transform_posteriors">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.transform_posteriors">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">transform_posteriors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">kernel</span><span class="p">):</span>
<span class="k">if</span> <span class="n">kernel</span> <span class="ow">in</span> <span class="p">{</span><span class="s1">&#39;aitchison&#39;</span><span class="p">,</span> <span class="s1">&#39;ilr&#39;</span><span class="p">}:</span>
<span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shrink_posteriors</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="k">if</span> <span class="n">kernel</span> <span class="o">==</span> <span class="s1">&#39;aitchison&#39;</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">clr_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="k">if</span> <span class="n">kernel</span> <span class="o">==</span> <span class="s1">&#39;ilr&#39;</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">ilr_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="k">return</span> <span class="n">X</span></div>
<div class="viewcode-block" id="KDEBase.shrink_posteriors">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.shrink_posteriors">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">shrink_posteriors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="n">shrinkage</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;shrinkage&#39;</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)</span>
<span class="k">if</span> <span class="n">shrinkage</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="n">X</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="n">n_classes</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">uniform</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">n_classes</span><span class="p">,</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">X</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="k">return</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">shrinkage</span><span class="p">)</span> <span class="o">*</span> <span class="n">X</span> <span class="o">+</span> <span class="n">shrinkage</span> <span class="o">*</span> <span class="n">uniform</span></div>
<div class="viewcode-block" id="KDEBase.effective_bandwidth">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.effective_bandwidth">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">effective_bandwidth</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">bandwidth</span><span class="p">,</span> <span class="n">kernel</span><span class="p">):</span>
<span class="n">shrinkage</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;shrinkage&#39;</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)</span>
<span class="k">if</span> <span class="n">shrinkage</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">kernel</span> <span class="ow">in</span> <span class="p">{</span><span class="s1">&#39;aitchison&#39;</span><span class="p">,</span> <span class="s1">&#39;ilr&#39;</span><span class="p">}</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">,</span> <span class="n">Real</span><span class="p">):</span>
<span class="k">return</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">shrinkage</span><span class="p">)</span> <span class="o">*</span> <span class="nb">float</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">)</span>
<span class="k">return</span> <span class="n">bandwidth</span></div>
<div class="viewcode-block" id="KDEBase.clr_transform">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.clr_transform">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">clr_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;clr&#39;</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">clr</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">CLRtransformation</span><span class="p">()</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">clr</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
<div class="viewcode-block" id="KDEBase.ilr_transform">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.ilr_transform">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">ilr_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;ilr&#39;</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">ilr</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">ILRtransformation</span><span class="p">()</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">ilr</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
</div>
<div class="viewcode-block" id="KDEyML"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyML">[docs]</a><span class="k">class</span> <span class="nc">KDEyML</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">,</span> <span class="n">KDEBase</span><span class="p">):</span> <div class="viewcode-block" id="KDEyML">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyML">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">KDEyML</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">,</span> <span class="n">KDEBase</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Kernel Density Estimation model for quantification (KDEy) relying on the Kullback-Leibler divergence (KLD) as</span> <span class="sd"> Kernel Density Estimation model for quantification (KDEy) relying on the Kullback-Leibler divergence (KLD) as</span>
<span class="sd"> the divergence measure to be minimized. This method was first proposed in the paper</span> <span class="sd"> the divergence measure to be minimized. This method was first proposed in the paper</span>
@ -165,32 +251,49 @@
<span class="sd"> which corresponds to the maximum likelihood estimate.</span> <span class="sd"> which corresponds to the maximum likelihood estimate.</span>
<span class="sd"> :param classifier: a sklearn&#39;s Estimator that generates a binary classifier.</span> <span class="sd"> :param classifier: a scikit-learn&#39;s BaseEstimator, or None, in which case the classifier is taken to be</span>
<span class="sd"> the one indicated in `qp.environ[&#39;DEFAULT_CLS&#39;]`</span>
<span class="sd"> :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
<span class="sd"> learner has been trained outside the quantifier.</span>
<span class="sd"> :param val_split: specifies the data used for generating classifier predictions. This specification</span> <span class="sd"> :param val_split: specifies the data used for generating classifier predictions. This specification</span>
<span class="sd"> can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span> <span class="sd"> can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span>
<span class="sd"> be extracted from the training set; or as an integer (default 5), indicating that the predictions</span> <span class="sd"> be extracted from the training set; or as an integer (default 5), indicating that the predictions</span>
<span class="sd"> are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span> <span class="sd"> are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span>
<span class="sd"> for `k`); or as a collection defining the specific set of data to use for validation.</span> <span class="sd"> for `k`); or as a tuple (X,y) defining the specific set of data to use for validation.</span>
<span class="sd"> Alternatively, this set can be specified at fit time by indicating the exact set of data</span>
<span class="sd"> on which the predictions are to be generated.</span>
<span class="sd"> :param bandwidth: float, the bandwidth of the Kernel</span> <span class="sd"> :param bandwidth: float, the bandwidth of the Kernel</span>
<span class="sd"> :param n_jobs: number of parallel workers</span> <span class="sd"> :param kernel: kernel of KDE, valid ones are in KDEBase.KERNELS</span>
<span class="sd"> :param shrinkage: amount of shrinkage towards the uniform distribution to apply before</span>
<span class="sd"> Aitchison/ILR transformations. Must be in ``[0,1)``.</span>
<span class="sd"> :param random_state: a seed to be set before fitting any base quantifier (default None)</span> <span class="sd"> :param random_state: a seed to be set before fitting any base quantifier (default None)</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">bandwidth</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">bandwidth</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_check_bandwidth</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">)</span> <span class="n">kernel</span><span class="o">=</span><span class="s1">&#39;gaussian&#39;</span><span class="p">,</span> <span class="n">shrinkage</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span> <span class="o">=</span> <span class="n">KDEBase</span><span class="o">.</span><span class="n">_check_bandwidth</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">,</span> <span class="n">kernel</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span> <span class="o">=</span> <span class="n">bandwidth</span> <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_kernel</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span> <span class="k">assert</span> <span class="mi">0</span> <span class="o">&lt;=</span> <span class="n">shrinkage</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;shrinkage must be in [0,1)&#39;</span>
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">!=</span> <span class="s1">&#39;gaussian&#39;</span> <span class="ow">or</span> <span class="n">shrinkage</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span> \
<span class="s1">&#39;shrinkage is only supported for Aitchison/ILR kernels&#39;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">shrinkage</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">shrinkage</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span>
<div class="viewcode-block" id="KDEyML.aggregation_fit"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyML.aggregation_fit">[docs]</a> <span class="k">def</span> <span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span> <div class="viewcode-block" id="KDEyML.aggregation_fit">
<span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_mixture_components</span><span class="p">(</span><span class="o">*</span><span class="n">classif_predictions</span><span class="o">.</span><span class="n">Xy</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">n_classes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span><span class="p">)</span> <a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyML.aggregation_fit">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_mixture_components</span><span class="p">(</span>
<span class="n">classif_predictions</span><span class="p">,</span>
<span class="n">labels</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span><span class="p">,</span>
<span class="bp">self</span><span class="o">.</span><span class="n">kernel</span><span class="p">,</span>
<span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span></div> <span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="KDEyML.aggregate"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyML.aggregate">[docs]</a> <span class="k">def</span> <span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
<div class="viewcode-block" id="KDEyML.aggregate">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyML.aggregate">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Searches for the mixture model parameter (the sought prevalence values) that maximizes the likelihood</span> <span class="sd"> Searches for the mixture model parameter (the sought prevalence values) that maximizes the likelihood</span>
<span class="sd"> of the data (i.e., that minimizes the negative log-likelihood)</span> <span class="sd"> of the data (i.e., that minimizes the negative log-likelihood)</span>
@ -198,20 +301,35 @@
<span class="sd"> :param posteriors: instances in the sample converted into posterior probabilities</span> <span class="sd"> :param posteriors: instances in the sample converted into posterior probabilities</span>
<span class="sd"> :return: a vector of class prevalence estimates</span> <span class="sd"> :return: a vector of class prevalence estimates</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">)</span> <span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">):</span>
<span class="n">epsilon</span> <span class="o">=</span> <span class="mf">1e-10</span> <span class="n">epsilon</span> <span class="o">=</span> <span class="mf">1e-12</span>
<span class="n">n_classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span><span class="p">)</span> <span class="n">n_classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span><span class="p">)</span>
<span class="n">test_densities</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">kde_i</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">)</span> <span class="k">for</span> <span class="n">kde_i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span><span class="p">]</span> <span class="k">if</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">!=</span> <span class="s1">&#39;gaussian&#39;</span> <span class="ow">and</span> <span class="n">n_classes</span> <span class="o">&gt;=</span> <span class="mi">20</span><span class="p">)</span> <span class="ow">or</span> <span class="n">n_classes</span> <span class="o">&gt;=</span> <span class="mi">30</span><span class="p">:</span>
<span class="n">test_log_densities</span> <span class="o">=</span> <span class="p">[</span>
<span class="bp">self</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">kde_i</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span><span class="p">,</span> <span class="n">log_densities</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">for</span> <span class="n">kde_i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span>
<span class="p">]</span>
<span class="k">def</span> <span class="nf">neg_loglikelihood</span><span class="p">(</span><span class="n">prev</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">neg_loglikelihood</span><span class="p">(</span><span class="n">prev</span><span class="p">):</span>
<span class="n">test_mixture_likelihood</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">prev_i</span> <span class="o">*</span> <span class="n">dens_i</span> <span class="k">for</span> <span class="n">prev_i</span><span class="p">,</span> <span class="n">dens_i</span> <span class="ow">in</span> <span class="nb">zip</span> <span class="p">(</span><span class="n">prev</span><span class="p">,</span> <span class="n">test_densities</span><span class="p">))</span> <span class="n">prev</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">smooth</span><span class="p">(</span><span class="n">prev</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="n">epsilon</span><span class="p">)</span>
<span class="n">test_loglikelihood</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">test_mixture_likelihood</span> <span class="o">+</span> <span class="n">epsilon</span><span class="p">)</span> <span class="n">test_loglikelihood</span> <span class="o">=</span> <span class="n">logsumexp</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">prev</span><span class="p">)[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">+</span> <span class="n">test_log_densities</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="k">return</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">test_loglikelihood</span><span class="p">)</span> <span class="k">return</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">test_loglikelihood</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">test_densities</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">kde_i</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span><span class="p">)</span> <span class="k">for</span> <span class="n">kde_i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span><span class="p">]</span>
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">optim_minimize</span><span class="p">(</span><span class="n">neg_loglikelihood</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span></div></div> <span class="k">def</span><span class="w"> </span><span class="nf">neg_loglikelihood</span><span class="p">(</span><span class="n">prev</span><span class="p">):</span>
<span class="n">test_mixture_likelihood</span> <span class="o">=</span> <span class="n">prev</span> <span class="o">@</span> <span class="n">test_densities</span>
<span class="n">test_loglikelihood</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">test_mixture_likelihood</span> <span class="o">+</span> <span class="n">epsilon</span><span class="p">)</span>
<span class="k">return</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">test_loglikelihood</span><span class="p">)</span>
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">optim_minimize</span><span class="p">(</span><span class="n">neg_loglikelihood</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span></div>
</div>
<div class="viewcode-block" id="KDEyHD"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyHD">[docs]</a><span class="k">class</span> <span class="nc">KDEyHD</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">,</span> <span class="n">KDEBase</span><span class="p">):</span>
<div class="viewcode-block" id="KDEyHD">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyHD">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">KDEyHD</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">,</span> <span class="n">KDEBase</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Kernel Density Estimation model for quantification (KDEy) relying on the squared Hellinger Disntace (HD) as</span> <span class="sd"> Kernel Density Estimation model for quantification (KDEy) relying on the squared Hellinger Disntace (HD) as</span>
<span class="sd"> the divergence measure to be minimized. This method was first proposed in the paper</span> <span class="sd"> the divergence measure to be minimized. This method was first proposed in the paper</span>
@ -242,53 +360,59 @@
<span class="sd"> where the datapoints (trials) :math:`x_1,\\ldots,x_t\\sim_{\\mathrm{iid}} r` with :math:`r` the</span> <span class="sd"> where the datapoints (trials) :math:`x_1,\\ldots,x_t\\sim_{\\mathrm{iid}} r` with :math:`r` the</span>
<span class="sd"> uniform distribution.</span> <span class="sd"> uniform distribution.</span>
<span class="sd"> :param classifier: a sklearn&#39;s Estimator that generates a binary classifier.</span> <span class="sd"> :param classifier: a scikit-learn&#39;s BaseEstimator, or None, in which case the classifier is taken to be</span>
<span class="sd"> the one indicated in `qp.environ[&#39;DEFAULT_CLS&#39;]`</span>
<span class="sd"> :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
<span class="sd"> learner has been trained outside the quantifier.</span>
<span class="sd"> :param val_split: specifies the data used for generating classifier predictions. This specification</span> <span class="sd"> :param val_split: specifies the data used for generating classifier predictions. This specification</span>
<span class="sd"> can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span> <span class="sd"> can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span>
<span class="sd"> be extracted from the training set; or as an integer (default 5), indicating that the predictions</span> <span class="sd"> be extracted from the training set; or as an integer (default 5), indicating that the predictions</span>
<span class="sd"> are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span> <span class="sd"> are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span>
<span class="sd"> for `k`); or as a collection defining the specific set of data to use for validation.</span> <span class="sd"> for `k`); or as a tuple (X,y) defining the specific set of data to use for validation.</span>
<span class="sd"> Alternatively, this set can be specified at fit time by indicating the exact set of data</span>
<span class="sd"> on which the predictions are to be generated.</span>
<span class="sd"> :param bandwidth: float, the bandwidth of the Kernel</span> <span class="sd"> :param bandwidth: float, the bandwidth of the Kernel</span>
<span class="sd"> :param n_jobs: number of parallel workers</span>
<span class="sd"> :param random_state: a seed to be set before fitting any base quantifier (default None)</span> <span class="sd"> :param random_state: a seed to be set before fitting any base quantifier (default None)</span>
<span class="sd"> :param montecarlo_trials: number of Monte Carlo trials (default 10000)</span> <span class="sd"> :param montecarlo_trials: number of Monte Carlo trials (default 10000)</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">divergence</span><span class="p">:</span> <span class="nb">str</span><span class="o">=</span><span class="s1">&#39;HD&#39;</span><span class="p">,</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">divergence</span><span class="p">:</span> <span class="nb">str</span><span class="o">=</span><span class="s1">&#39;HD&#39;</span><span class="p">,</span>
<span class="n">bandwidth</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">montecarlo_trials</span><span class="o">=</span><span class="mi">10000</span><span class="p">):</span> <span class="n">bandwidth</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">montecarlo_trials</span><span class="o">=</span><span class="mi">10000</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_check_bandwidth</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">)</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
<span class="bp">self</span><span class="o">.</span><span class="n">divergence</span> <span class="o">=</span> <span class="n">divergence</span> <span class="bp">self</span><span class="o">.</span><span class="n">divergence</span> <span class="o">=</span> <span class="n">divergence</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span> <span class="o">=</span> <span class="n">bandwidth</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span> <span class="o">=</span> <span class="n">KDEBase</span><span class="o">.</span><span class="n">_check_bandwidth</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">,</span> <span class="n">kernel</span><span class="o">=</span><span class="s1">&#39;gaussian&#39;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span>
<span class="bp">self</span><span class="o">.</span><span class="n">montecarlo_trials</span> <span class="o">=</span> <span class="n">montecarlo_trials</span> <span class="bp">self</span><span class="o">.</span><span class="n">montecarlo_trials</span> <span class="o">=</span> <span class="n">montecarlo_trials</span>
<div class="viewcode-block" id="KDEyHD.aggregation_fit"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyHD.aggregation_fit">[docs]</a> <span class="k">def</span> <span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span> <div class="viewcode-block" id="KDEyHD.aggregation_fit">
<span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_mixture_components</span><span class="p">(</span><span class="o">*</span><span class="n">classif_predictions</span><span class="o">.</span><span class="n">Xy</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">n_classes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span><span class="p">)</span> <a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyHD.aggregation_fit">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_mixture_components</span><span class="p">(</span>
<span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span><span class="p">,</span> <span class="s1">&#39;gaussian&#39;</span>
<span class="p">)</span>
<span class="n">N</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">montecarlo_trials</span> <span class="n">N</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">montecarlo_trials</span>
<span class="n">rs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="n">rs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span>
<span class="n">n</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">n_classes</span> <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">reference_samples</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span><span class="n">kde_i</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">N</span><span class="o">//</span><span class="n">n</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">rs</span><span class="p">)</span> <span class="k">for</span> <span class="n">kde_i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span><span class="p">])</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_samples</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span><span class="n">kde_i</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">N</span><span class="o">//</span><span class="n">n</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">rs</span><span class="p">)</span> <span class="k">for</span> <span class="n">kde_i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span><span class="p">])</span>
<span class="bp">self</span><span class="o">.</span><span class="n">reference_classwise_densities</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">kde_j</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_samples</span><span class="p">)</span> <span class="k">for</span> <span class="n">kde_j</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span><span class="p">])</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_classwise_densities</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span>
<span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">kde_j</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_samples</span><span class="p">,</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">)</span> <span class="k">for</span> <span class="n">kde_j</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span><span class="p">]</span>
<span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">reference_density</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">reference_classwise_densities</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="c1"># equiv. to (uniform @ self.reference_classwise_densities)</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_density</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">reference_classwise_densities</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="c1"># equiv. to (uniform @ self.reference_classwise_densities)</span>
<span class="k">return</span> <span class="bp">self</span></div> <span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="KDEyHD.aggregate"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyHD.aggregate">[docs]</a> <span class="k">def</span> <span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
<div class="viewcode-block" id="KDEyHD.aggregate">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyHD.aggregate">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
<span class="c1"># we retain all n*N examples (sampled from a mixture with uniform parameter), and then</span> <span class="c1"># we retain all n*N examples (sampled from a mixture with uniform parameter), and then</span>
<span class="c1"># apply importance sampling (IS). In this version we compute D(p_alpha||q) with IS</span> <span class="c1"># apply importance sampling (IS). In this version we compute D(p_alpha||q) with IS</span>
<span class="n">n_classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span><span class="p">)</span> <span class="n">n_classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span><span class="p">)</span>
<span class="n">test_kde</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_kde_function</span><span class="p">(</span><span class="n">posteriors</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span><span class="p">)</span> <span class="n">test_kde</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_kde_function</span><span class="p">(</span><span class="n">posteriors</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span><span class="p">,</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">)</span>
<span class="n">test_densities</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">test_kde</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_samples</span><span class="p">)</span> <span class="n">test_densities</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">test_kde</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_samples</span><span class="p">,</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">f_squared_hellinger</span><span class="p">(</span><span class="n">u</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">f_squared_hellinger</span><span class="p">(</span><span class="n">u</span><span class="p">):</span>
<span class="k">return</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">u</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span> <span class="k">return</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">u</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span>
<span class="c1"># todo: this will fail when self.divergence is a callable, and is not the right place to do it anyway</span> <span class="c1"># todo: this will fail when self.divergence is a callable, and is not the right place to do it anyway</span>
@ -304,15 +428,19 @@
<span class="n">p_class</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_classwise_densities</span> <span class="o">+</span> <span class="n">epsilon</span> <span class="n">p_class</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_classwise_densities</span> <span class="o">+</span> <span class="n">epsilon</span>
<span class="n">fracs</span> <span class="o">=</span> <span class="n">p_class</span><span class="o">/</span><span class="n">qs</span> <span class="n">fracs</span> <span class="o">=</span> <span class="n">p_class</span><span class="o">/</span><span class="n">qs</span>
<span class="k">def</span> <span class="nf">divergence</span><span class="p">(</span><span class="n">prev</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">divergence</span><span class="p">(</span><span class="n">prev</span><span class="p">):</span>
<span class="c1"># ps / qs = (prev @ p_class) / qs = prev @ (p_class / qs) = prev @ fracs</span> <span class="c1"># ps / qs = (prev @ p_class) / qs = prev @ (p_class / qs) = prev @ fracs</span>
<span class="n">ps_div_qs</span> <span class="o">=</span> <span class="n">prev</span> <span class="o">@</span> <span class="n">fracs</span> <span class="n">ps_div_qs</span> <span class="o">=</span> <span class="n">prev</span> <span class="o">@</span> <span class="n">fracs</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span> <span class="n">f</span><span class="p">(</span><span class="n">ps_div_qs</span><span class="p">)</span> <span class="o">*</span> <span class="n">iw</span> <span class="p">)</span> <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span> <span class="n">f</span><span class="p">(</span><span class="n">ps_div_qs</span><span class="p">)</span> <span class="o">*</span> <span class="n">iw</span> <span class="p">)</span>
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">optim_minimize</span><span class="p">(</span><span class="n">divergence</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span></div></div> <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">optim_minimize</span><span class="p">(</span><span class="n">divergence</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span></div>
</div>
<div class="viewcode-block" id="KDEyCS"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyCS">[docs]</a><span class="k">class</span> <span class="nc">KDEyCS</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">):</span>
<div class="viewcode-block" id="KDEyCS">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyCS">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">KDEyCS</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Kernel Density Estimation model for quantification (KDEy) relying on the Cauchy-Schwarz divergence (CS) as</span> <span class="sd"> Kernel Density Estimation model for quantification (KDEy) relying on the Cauchy-Schwarz divergence (CS) as</span>
<span class="sd"> the divergence measure to be minimized. This method was first proposed in the paper</span> <span class="sd"> the divergence measure to be minimized. This method was first proposed in the paper</span>
@ -337,26 +465,25 @@
<span class="sd"> The authors showed that this distribution matching admits a closed-form solution</span> <span class="sd"> The authors showed that this distribution matching admits a closed-form solution</span>
<span class="sd"> :param classifier: a sklearn&#39;s Estimator that generates a binary classifier.</span> <span class="sd"> :param classifier: a scikit-learn&#39;s BaseEstimator, or None, in which case the classifier is taken to be</span>
<span class="sd"> the one indicated in `qp.environ[&#39;DEFAULT_CLS&#39;]`</span>
<span class="sd"> :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
<span class="sd"> learner has been trained outside the quantifier.</span>
<span class="sd"> :param val_split: specifies the data used for generating classifier predictions. This specification</span> <span class="sd"> :param val_split: specifies the data used for generating classifier predictions. This specification</span>
<span class="sd"> can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span> <span class="sd"> can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span>
<span class="sd"> be extracted from the training set; or as an integer (default 5), indicating that the predictions</span> <span class="sd"> be extracted from the training set; or as an integer (default 5), indicating that the predictions</span>
<span class="sd"> are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span> <span class="sd"> are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span>
<span class="sd"> for `k`); or as a collection defining the specific set of data to use for validation.</span> <span class="sd"> for `k`); or as a tuple (X,y) defining the specific set of data to use for validation.</span>
<span class="sd"> Alternatively, this set can be specified at fit time by indicating the exact set of data</span>
<span class="sd"> on which the predictions are to be generated.</span>
<span class="sd"> :param bandwidth: float, the bandwidth of the Kernel</span> <span class="sd"> :param bandwidth: float, the bandwidth of the Kernel</span>
<span class="sd"> :param n_jobs: number of parallel workers</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">bandwidth</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">bandwidth</span><span class="o">=</span><span class="mf">0.1</span><span class="p">):</span>
<span class="n">KDEBase</span><span class="o">.</span><span class="n">_check_bandwidth</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">)</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span> <span class="o">=</span> <span class="n">KDEBase</span><span class="o">.</span><span class="n">_check_bandwidth</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">,</span> <span class="n">kernel</span><span class="o">=</span><span class="s1">&#39;gaussian&#39;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span> <span class="o">=</span> <span class="n">bandwidth</span>
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
<div class="viewcode-block" id="KDEyCS.gram_matrix_mix_sum"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyCS.gram_matrix_mix_sum">[docs]</a> <span class="k">def</span> <span class="nf">gram_matrix_mix_sum</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> <div class="viewcode-block" id="KDEyCS.gram_matrix_mix_sum">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyCS.gram_matrix_mix_sum">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">gram_matrix_mix_sum</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="c1"># this adapts the output of the rbf_kernel function (pairwise evaluations of Gaussian kernels k(x,y))</span> <span class="c1"># this adapts the output of the rbf_kernel function (pairwise evaluations of Gaussian kernels k(x,y))</span>
<span class="c1"># to contain pairwise evaluations of N(x|mu,Sigma1+Sigma2) with mu=y and Sigma1 and Sigma2 are </span> <span class="c1"># to contain pairwise evaluations of N(x|mu,Sigma1+Sigma2) with mu=y and Sigma1 and Sigma2 are </span>
<span class="c1"># two &quot;scalar matrices&quot; (h^2)*I each, so Sigma1+Sigma2 has scalar 2(h^2) (h is the bandwidth)</span> <span class="c1"># two &quot;scalar matrices&quot; (h^2)*I each, so Sigma1+Sigma2 has scalar 2(h^2) (h is the bandwidth)</span>
@ -368,17 +495,20 @@
<span class="n">gram</span> <span class="o">=</span> <span class="n">norm_factor</span> <span class="o">*</span> <span class="n">rbf_kernel</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="n">gamma</span><span class="p">)</span> <span class="n">gram</span> <span class="o">=</span> <span class="n">norm_factor</span> <span class="o">*</span> <span class="n">rbf_kernel</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="n">gamma</span><span class="p">)</span>
<span class="k">return</span> <span class="n">gram</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span></div> <span class="k">return</span> <span class="n">gram</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span></div>
<div class="viewcode-block" id="KDEyCS.aggregation_fit"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyCS.aggregation_fit">[docs]</a> <span class="k">def</span> <span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
<span class="n">P</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">classif_predictions</span><span class="o">.</span><span class="n">Xy</span> <div class="viewcode-block" id="KDEyCS.aggregation_fit">
<span class="n">n</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">n_classes</span> <a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyCS.aggregation_fit">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="n">P</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span>
<span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
<span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">))</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">n</span><span class="p">)),</span> \ <span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">))</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">n</span><span class="p">)),</span> \
<span class="s1">&#39;label name gaps not allowed in current implementation&#39;</span> <span class="s1">&#39;label name gaps not allowed in current implementation&#39;</span>
<span class="c1"># counts_inv keeps track of the relative weight of each datapoint within its class</span> <span class="c1"># counts_inv keeps track of the relative weight of each datapoint within its class</span>
<span class="c1"># (i.e., the weight in its KDE model)</span> <span class="c1"># (i.e., the weight in its KDE model)</span>
<span class="n">counts_inv</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">counts</span><span class="p">())</span> <span class="n">counts_inv</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">counts_from_labels</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">))</span>
<span class="c1"># tr_tr_sums corresponds to symbol \overline{B} in the paper</span> <span class="c1"># tr_tr_sums corresponds to symbol \overline{B} in the paper</span>
<span class="n">tr_tr_sums</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">n</span><span class="p">,</span><span class="n">n</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span> <span class="n">tr_tr_sums</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">n</span><span class="p">,</span><span class="n">n</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
@ -399,7 +529,10 @@
<span class="k">return</span> <span class="bp">self</span></div> <span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="KDEyCS.aggregate"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyCS.aggregate">[docs]</a> <span class="k">def</span> <span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
<div class="viewcode-block" id="KDEyCS.aggregate">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyCS.aggregate">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
<span class="n">Ptr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">Ptr</span> <span class="n">Ptr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">Ptr</span>
<span class="n">Pte</span> <span class="o">=</span> <span class="n">posteriors</span> <span class="n">Pte</span> <span class="o">=</span> <span class="n">posteriors</span>
<span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ytr</span> <span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ytr</span>
@ -419,16 +552,17 @@
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
<span class="n">tr_te_sums</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gram_matrix_mix_sum</span><span class="p">(</span><span class="n">Ptr</span><span class="p">[</span><span class="n">y</span><span class="o">==</span><span class="n">i</span><span class="p">],</span> <span class="n">Pte</span><span class="p">)</span> <span class="n">tr_te_sums</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gram_matrix_mix_sum</span><span class="p">(</span><span class="n">Ptr</span><span class="p">[</span><span class="n">y</span><span class="o">==</span><span class="n">i</span><span class="p">],</span> <span class="n">Pte</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">divergence</span><span class="p">(</span><span class="n">alpha</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">divergence</span><span class="p">(</span><span class="n">alpha</span><span class="p">):</span>
<span class="c1"># called \overline{r} in the paper</span> <span class="c1"># called \overline{r} in the paper</span>
<span class="n">alpha_ratio</span> <span class="o">=</span> <span class="n">alpha</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">counts_inv</span> <span class="n">alpha_ratio</span> <span class="o">=</span> <span class="n">alpha</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">counts_inv</span>
<span class="c1"># recal that tr_te_sums already accounts for the constant terms (1/Li)*(1/M)</span> <span class="c1"># recall that tr_te_sums already accounts for the constant terms (1/Li)*(1/M)</span>
<span class="n">partA</span> <span class="o">=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">((</span><span class="n">alpha_ratio</span> <span class="o">@</span> <span class="n">tr_te_sums</span><span class="p">)</span> <span class="o">*</span> <span class="n">Minv</span><span class="p">)</span> <span class="n">partA</span> <span class="o">=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">((</span><span class="n">alpha_ratio</span> <span class="o">@</span> <span class="n">tr_te_sums</span><span class="p">)</span> <span class="o">*</span> <span class="n">Minv</span><span class="p">)</span>
<span class="n">partB</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">alpha_ratio</span> <span class="o">@</span> <span class="n">tr_tr_sums</span> <span class="o">@</span> <span class="n">alpha_ratio</span><span class="p">)</span> <span class="n">partB</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">alpha_ratio</span> <span class="o">@</span> <span class="n">tr_tr_sums</span> <span class="o">@</span> <span class="n">alpha_ratio</span><span class="p">)</span>
<span class="k">return</span> <span class="n">partA</span> <span class="o">+</span> <span class="n">partB</span> <span class="c1">#+ partC</span> <span class="k">return</span> <span class="n">partA</span> <span class="o">+</span> <span class="n">partB</span> <span class="c1">#+ partC</span>
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">optim_minimize</span><span class="p">(</span><span class="n">divergence</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span></div></div> <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">optim_minimize</span><span class="p">(</span><span class="n">divergence</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span></div>
</div>
</pre></div> </pre></div>

295
docs/build/html/_modules/quapy/method/_threshold_optim.html vendored
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@ -1,23 +1,20 @@
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@ -43,7 +40,13 @@
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@ -71,17 +74,19 @@
<div itemprop="articleBody"> <div itemprop="articleBody">
<h1>Source code for quapy.method._threshold_optim</h1><div class="highlight"><pre> <h1>Source code for quapy.method._threshold_optim</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">abstractmethod</span> <span></span><span class="kn">from</span><span class="w"> </span><span class="nn">abc</span><span class="w"> </span><span class="kn">import</span> <span class="n">abstractmethod</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span> <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseEstimator</span>
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span> <span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
<span class="kn">import</span> <span class="nn">quapy.functional</span> <span class="k">as</span> <span class="nn">F</span> <span class="kn">import</span><span class="w"> </span><span class="nn">quapy.functional</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">F</span>
<span class="kn">from</span> <span class="nn">quapy.data</span> <span class="kn">import</span> <span class="n">LabelledCollection</span> <span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data</span><span class="w"> </span><span class="kn">import</span> <span class="n">LabelledCollection</span>
<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="n">BinaryAggregativeQuantifier</span> <span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.aggregative</span><span class="w"> </span><span class="kn">import</span> <span class="n">BinaryAggregativeQuantifier</span>
<div class="viewcode-block" id="ThresholdOptimization"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization">[docs]</a><span class="k">class</span> <span class="nc">ThresholdOptimization</span><span class="p">(</span><span class="n">BinaryAggregativeQuantifier</span><span class="p">):</span> <div class="viewcode-block" id="ThresholdOptimization">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">ThresholdOptimization</span><span class="p">(</span><span class="n">BinaryAggregativeQuantifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Abstract class of Threshold Optimization variants for :class:`ACC` as proposed by</span> <span class="sd"> Abstract class of Threshold Optimization variants for :class:`ACC` as proposed by</span>
<span class="sd"> `Forman 2006 &lt;https://dl.acm.org/doi/abs/10.1145/1150402.1150423&gt;`_ and</span> <span class="sd"> `Forman 2006 &lt;https://dl.acm.org/doi/abs/10.1145/1150402.1150423&gt;`_ and</span>
@ -91,22 +96,29 @@
<span class="sd"> that would allow for more true positives and many more false positives, on the grounds this</span> <span class="sd"> that would allow for more true positives and many more false positives, on the grounds this</span>
<span class="sd"> would deliver larger denominators.</span> <span class="sd"> would deliver larger denominators.</span>
<span class="sd"> :param classifier: a sklearn&#39;s Estimator that generates a classifier</span> <span class="sd"> :param classifier: a scikit-learn&#39;s BaseEstimator, or None, in which case the classifier is taken to be</span>
<span class="sd"> :param val_split: indicates the proportion of data to be used as a stratified held-out validation set in which the</span> <span class="sd"> the one indicated in `qp.environ[&#39;DEFAULT_CLS&#39;]`</span>
<span class="sd"> misclassification rates are to be estimated.</span>
<span class="sd"> This parameter can be indicated as a real value (between 0 and 1), representing a proportion of</span> <span class="sd"> :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
<span class="sd"> validation data, or as an integer, indicating that the misclassification rates should be estimated via</span> <span class="sd"> learner has been trained outside the quantifier.</span>
<span class="sd"> `k`-fold cross validation (this integer stands for the number of folds `k`, defaults 5), or as a</span>
<span class="sd"> :class:`quapy.data.base.LabelledCollection` (the split itself).</span> <span class="sd"> :param val_split: specifies the data used for generating classifier predictions. This specification</span>
<span class="sd"> can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span>
<span class="sd"> be extracted from the training set; or as an integer (default 5), indicating that the predictions</span>
<span class="sd"> are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span>
<span class="sd"> for `k`); or as a tuple (X,y) defining the specific set of data to use for validation.</span>
<span class="sd"> :param n_jobs: number of parallel workers</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_njobs</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_njobs</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)</span>
<div class="viewcode-block" id="ThresholdOptimization.condition"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.condition">[docs]</a> <span class="nd">@abstractmethod</span> <div class="viewcode-block" id="ThresholdOptimization.condition">
<span class="k">def</span> <span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span> <a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.condition">[docs]</a>
<span class="nd">@abstractmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Implements the criterion according to which the threshold should be selected.</span> <span class="sd"> Implements the criterion according to which the threshold should be selected.</span>
<span class="sd"> This function should return the (float) score to be minimized.</span> <span class="sd"> This function should return the (float) score to be minimized.</span>
@ -117,7 +129,10 @@
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="o">...</span></div> <span class="o">...</span></div>
<div class="viewcode-block" id="ThresholdOptimization.discard"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.discard">[docs]</a> <span class="k">def</span> <span class="nf">discard</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
<div class="viewcode-block" id="ThresholdOptimization.discard">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.discard">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">discard</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Indicates whether a combination of tpr and fpr should be discarded</span> <span class="sd"> Indicates whether a combination of tpr and fpr should be discarded</span>
@ -128,7 +143,8 @@
<span class="k">return</span> <span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span></div> <span class="k">return</span> <span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span></div>
<span class="k">def</span> <span class="nf">_eval_candidate_thresholds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_eval_candidate_thresholds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Seeks for the best `tpr` and `fpr` according to the score obtained at different</span> <span class="sd"> Seeks for the best `tpr` and `fpr` according to the score obtained at different</span>
<span class="sd"> decision thresholds. The scoring function is implemented in function `_condition`.</span> <span class="sd"> decision thresholds. The scoring function is implemented in function `_condition`.</span>
@ -163,7 +179,9 @@
<span class="k">return</span> <span class="n">candidates</span> <span class="k">return</span> <span class="n">candidates</span>
<div class="viewcode-block" id="ThresholdOptimization.aggregate_with_threshold"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.aggregate_with_threshold">[docs]</a> <span class="k">def</span> <span class="nf">aggregate_with_threshold</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">tprs</span><span class="p">,</span> <span class="n">fprs</span><span class="p">,</span> <span class="n">thresholds</span><span class="p">):</span> <div class="viewcode-block" id="ThresholdOptimization.aggregate_with_threshold">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.aggregate_with_threshold">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregate_with_threshold</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">tprs</span><span class="p">,</span> <span class="n">fprs</span><span class="p">,</span> <span class="n">thresholds</span><span class="p">):</span>
<span class="c1"># This function performs the adjusted count for given tpr, fpr, and threshold.</span> <span class="c1"># This function performs the adjusted count for given tpr, fpr, and threshold.</span>
<span class="c1"># Note that, due to broadcasting, tprs, fprs, and thresholds could be arrays of length &gt; 1</span> <span class="c1"># Note that, due to broadcasting, tprs, fprs, and thresholds could be arrays of length &gt; 1</span>
<span class="n">prevs_estims</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">classif_predictions</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">thresholds</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="n">prevs_estims</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">classif_predictions</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">thresholds</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
@ -171,35 +189,45 @@
<span class="n">prevs_estims</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">as_binary_prevalence</span><span class="p">(</span><span class="n">prevs_estims</span><span class="p">,</span> <span class="n">clip_if_necessary</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="n">prevs_estims</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">as_binary_prevalence</span><span class="p">(</span><span class="n">prevs_estims</span><span class="p">,</span> <span class="n">clip_if_necessary</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">return</span> <span class="n">prevs_estims</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span></div> <span class="k">return</span> <span class="n">prevs_estims</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span></div>
<span class="k">def</span> <span class="nf">_compute_table</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">y_</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_compute_table</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">y_</span><span class="p">):</span>
<span class="n">TP</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">y</span> <span class="o">==</span> <span class="n">y_</span><span class="p">,</span> <span class="n">y</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">pos_label</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="n">TP</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">y</span> <span class="o">==</span> <span class="n">y_</span><span class="p">,</span> <span class="n">y</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">pos_label</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">FP</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">y</span> <span class="o">!=</span> <span class="n">y_</span><span class="p">,</span> <span class="n">y</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">neg_label</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="n">FP</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">y</span> <span class="o">!=</span> <span class="n">y_</span><span class="p">,</span> <span class="n">y</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">neg_label</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">FN</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">y</span> <span class="o">!=</span> <span class="n">y_</span><span class="p">,</span> <span class="n">y</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">pos_label</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="n">FN</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">y</span> <span class="o">!=</span> <span class="n">y_</span><span class="p">,</span> <span class="n">y</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">pos_label</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">TN</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">y</span> <span class="o">==</span> <span class="n">y_</span><span class="p">,</span> <span class="n">y</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">neg_label</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="n">TN</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">y</span> <span class="o">==</span> <span class="n">y_</span><span class="p">,</span> <span class="n">y</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">neg_label</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="k">return</span> <span class="n">TP</span><span class="p">,</span> <span class="n">FP</span><span class="p">,</span> <span class="n">FN</span><span class="p">,</span> <span class="n">TN</span> <span class="k">return</span> <span class="n">TP</span><span class="p">,</span> <span class="n">FP</span><span class="p">,</span> <span class="n">FN</span><span class="p">,</span> <span class="n">TN</span>
<span class="k">def</span> <span class="nf">_compute_tpr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">TP</span><span class="p">,</span> <span class="n">FP</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">_compute_tpr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">TP</span><span class="p">,</span> <span class="n">FP</span><span class="p">):</span>
<span class="k">if</span> <span class="n">TP</span> <span class="o">+</span> <span class="n">FP</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> <span class="k">if</span> <span class="n">TP</span> <span class="o">+</span> <span class="n">FP</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="mi">1</span> <span class="k">return</span> <span class="mi">1</span>
<span class="k">return</span> <span class="n">TP</span> <span class="o">/</span> <span class="p">(</span><span class="n">TP</span> <span class="o">+</span> <span class="n">FP</span><span class="p">)</span> <span class="k">return</span> <span class="n">TP</span> <span class="o">/</span> <span class="p">(</span><span class="n">TP</span> <span class="o">+</span> <span class="n">FP</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_compute_fpr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">FP</span><span class="p">,</span> <span class="n">TN</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">_compute_fpr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">FP</span><span class="p">,</span> <span class="n">TN</span><span class="p">):</span>
<span class="k">if</span> <span class="n">FP</span> <span class="o">+</span> <span class="n">TN</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> <span class="k">if</span> <span class="n">FP</span> <span class="o">+</span> <span class="n">TN</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">return</span> <span class="mi">0</span> <span class="k">return</span> <span class="mi">0</span>
<span class="k">return</span> <span class="n">FP</span> <span class="o">/</span> <span class="p">(</span><span class="n">FP</span> <span class="o">+</span> <span class="n">TN</span><span class="p">)</span> <span class="k">return</span> <span class="n">FP</span> <span class="o">/</span> <span class="p">(</span><span class="n">FP</span> <span class="o">+</span> <span class="n">TN</span><span class="p">)</span>
<div class="viewcode-block" id="ThresholdOptimization.aggregation_fit"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.aggregation_fit">[docs]</a> <span class="k">def</span> <span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span> <div class="viewcode-block" id="ThresholdOptimization.aggregation_fit">
<span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">classif_predictions</span><span class="o">.</span><span class="n">Xy</span> <a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.aggregation_fit">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span>
<span class="c1"># the standard behavior is to keep the best threshold only</span> <span class="c1"># the standard behavior is to keep the best threshold only</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tpr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fpr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_eval_candidate_thresholds</span><span class="p">(</span><span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span> <span class="bp">self</span><span class="o">.</span><span class="n">tpr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fpr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_eval_candidate_thresholds</span><span class="p">(</span><span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">return</span> <span class="bp">self</span></div> <span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="ThresholdOptimization.aggregate"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.aggregate">[docs]</a> <span class="k">def</span> <span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
<div class="viewcode-block" id="ThresholdOptimization.aggregate">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.aggregate">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
<span class="c1"># the standard behavior is to compute the adjusted count using the best threshold found</span> <span class="c1"># the standard behavior is to compute the adjusted count using the best threshold found</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">aggregate_with_threshold</span><span class="p">(</span><span class="n">classif_predictions</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tpr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fpr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span><span class="p">)</span></div></div> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">aggregate_with_threshold</span><span class="p">(</span><span class="n">classif_predictions</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tpr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fpr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span><span class="p">)</span></div>
</div>
<div class="viewcode-block" id="T50"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.T50">[docs]</a><span class="k">class</span> <span class="nc">T50</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
<div class="viewcode-block" id="T50">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.T50">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">T50</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Threshold Optimization variant for :class:`ACC` as proposed by</span> <span class="sd"> Threshold Optimization variant for :class:`ACC` as proposed by</span>
<span class="sd"> `Forman 2006 &lt;https://dl.acm.org/doi/abs/10.1145/1150402.1150423&gt;`_ and</span> <span class="sd"> `Forman 2006 &lt;https://dl.acm.org/doi/abs/10.1145/1150402.1150423&gt;`_ and</span>
@ -207,23 +235,34 @@
<span class="sd"> for the threshold that makes `tpr` closest to 0.5.</span> <span class="sd"> for the threshold that makes `tpr` closest to 0.5.</span>
<span class="sd"> The goal is to bring improved stability to the denominator of the adjustment.</span> <span class="sd"> The goal is to bring improved stability to the denominator of the adjustment.</span>
<span class="sd"> :param classifier: a sklearn&#39;s Estimator that generates a classifier</span> <span class="sd"> :param classifier: a scikit-learn&#39;s BaseEstimator, or None, in which case the classifier is taken to be</span>
<span class="sd"> :param val_split: indicates the proportion of data to be used as a stratified held-out validation set in which the</span> <span class="sd"> the one indicated in `qp.environ[&#39;DEFAULT_CLS&#39;]`</span>
<span class="sd"> misclassification rates are to be estimated.</span>
<span class="sd"> This parameter can be indicated as a real value (between 0 and 1), representing a proportion of</span> <span class="sd"> :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
<span class="sd"> validation data, or as an integer, indicating that the misclassification rates should be estimated via</span> <span class="sd"> learner has been trained outside the quantifier.</span>
<span class="sd"> `k`-fold cross validation (this integer stands for the number of folds `k`, defaults 5), or as a</span>
<span class="sd"> :class:`quapy.data.base.LabelledCollection` (the split itself).</span> <span class="sd"> :param val_split: specifies the data used for generating classifier predictions. This specification</span>
<span class="sd"> can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span>
<span class="sd"> be extracted from the training set; or as an integer (default 5), indicating that the predictions</span>
<span class="sd"> are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span>
<span class="sd"> for `k`); or as a tuple (X,y) defining the specific set of data to use for validation.</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
<div class="viewcode-block" id="T50.condition"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.T50.condition">[docs]</a> <span class="k">def</span> <span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span> <div class="viewcode-block" id="T50.condition">
<span class="k">return</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="mf">0.5</span><span class="p">)</span></div></div> <a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.T50.condition">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
<span class="k">return</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="mf">0.5</span><span class="p">)</span></div>
</div>
<div class="viewcode-block" id="MAX"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MAX">[docs]</a><span class="k">class</span> <span class="nc">MAX</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
<div class="viewcode-block" id="MAX">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MAX">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">MAX</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Threshold Optimization variant for :class:`ACC` as proposed by</span> <span class="sd"> Threshold Optimization variant for :class:`ACC` as proposed by</span>
<span class="sd"> `Forman 2006 &lt;https://dl.acm.org/doi/abs/10.1145/1150402.1150423&gt;`_ and</span> <span class="sd"> `Forman 2006 &lt;https://dl.acm.org/doi/abs/10.1145/1150402.1150423&gt;`_ and</span>
@ -231,24 +270,33 @@
<span class="sd"> for the threshold that maximizes `tpr-fpr`.</span> <span class="sd"> for the threshold that maximizes `tpr-fpr`.</span>
<span class="sd"> The goal is to bring improved stability to the denominator of the adjustment.</span> <span class="sd"> The goal is to bring improved stability to the denominator of the adjustment.</span>
<span class="sd"> :param classifier: a sklearn&#39;s Estimator that generates a classifier</span> <span class="sd"> :param classifier: a scikit-learn&#39;s BaseEstimator, or None, in which case the classifier is taken to be</span>
<span class="sd"> :param val_split: indicates the proportion of data to be used as a stratified held-out validation set in which the</span> <span class="sd"> the one indicated in `qp.environ[&#39;DEFAULT_CLS&#39;]`</span>
<span class="sd"> misclassification rates are to be estimated.</span> <span class="sd"> :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
<span class="sd"> This parameter can be indicated as a real value (between 0 and 1), representing a proportion of</span> <span class="sd"> learner has been trained outside the quantifier.</span>
<span class="sd"> validation data, or as an integer, indicating that the misclassification rates should be estimated via</span> <span class="sd"> :param val_split: specifies the data used for generating classifier predictions. This specification</span>
<span class="sd"> `k`-fold cross validation (this integer stands for the number of folds `k`, defaults 5), or as a</span> <span class="sd"> can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span>
<span class="sd"> :class:`quapy.data.base.LabelledCollection` (the split itself).</span> <span class="sd"> be extracted from the training set; or as an integer (default 5), indicating that the predictions</span>
<span class="sd"> are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span>
<span class="sd"> for `k`); or as a tuple (X,y) defining the specific set of data to use for validation.</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
<div class="viewcode-block" id="MAX.condition"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MAX.condition">[docs]</a> <span class="k">def</span> <span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span> <div class="viewcode-block" id="MAX.condition">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MAX.condition">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
<span class="c1"># MAX strives to maximize (tpr - fpr), which is equivalent to minimize (fpr - tpr)</span> <span class="c1"># MAX strives to maximize (tpr - fpr), which is equivalent to minimize (fpr - tpr)</span>
<span class="k">return</span> <span class="p">(</span><span class="n">fpr</span> <span class="o">-</span> <span class="n">tpr</span><span class="p">)</span></div></div> <span class="k">return</span> <span class="p">(</span><span class="n">fpr</span> <span class="o">-</span> <span class="n">tpr</span><span class="p">)</span></div>
</div>
<div class="viewcode-block" id="X"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.X">[docs]</a><span class="k">class</span> <span class="nc">X</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
<div class="viewcode-block" id="X">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.X">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">X</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Threshold Optimization variant for :class:`ACC` as proposed by</span> <span class="sd"> Threshold Optimization variant for :class:`ACC` as proposed by</span>
<span class="sd"> `Forman 2006 &lt;https://dl.acm.org/doi/abs/10.1145/1150402.1150423&gt;`_ and</span> <span class="sd"> `Forman 2006 &lt;https://dl.acm.org/doi/abs/10.1145/1150402.1150423&gt;`_ and</span>
@ -256,23 +304,32 @@
<span class="sd"> for the threshold that yields `tpr=1-fpr`.</span> <span class="sd"> for the threshold that yields `tpr=1-fpr`.</span>
<span class="sd"> The goal is to bring improved stability to the denominator of the adjustment.</span> <span class="sd"> The goal is to bring improved stability to the denominator of the adjustment.</span>
<span class="sd"> :param classifier: a sklearn&#39;s Estimator that generates a classifier</span> <span class="sd"> :param classifier: a scikit-learn&#39;s BaseEstimator, or None, in which case the classifier is taken to be</span>
<span class="sd"> :param val_split: indicates the proportion of data to be used as a stratified held-out validation set in which the</span> <span class="sd"> the one indicated in `qp.environ[&#39;DEFAULT_CLS&#39;]`</span>
<span class="sd"> misclassification rates are to be estimated.</span> <span class="sd"> :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
<span class="sd"> This parameter can be indicated as a real value (between 0 and 1), representing a proportion of</span> <span class="sd"> learner has been trained outside the quantifier.</span>
<span class="sd"> validation data, or as an integer, indicating that the misclassification rates should be estimated via</span> <span class="sd"> :param val_split: specifies the data used for generating classifier predictions. This specification</span>
<span class="sd"> `k`-fold cross validation (this integer stands for the number of folds `k`, defaults 5), or as a</span> <span class="sd"> can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span>
<span class="sd"> :class:`quapy.data.base.LabelledCollection` (the split itself).</span> <span class="sd"> be extracted from the training set; or as an integer (default 5), indicating that the predictions</span>
<span class="sd"> are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span>
<span class="sd"> for `k`); or as a tuple (X,y) defining the specific set of data to use for validation.</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
<div class="viewcode-block" id="X.condition"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.X.condition">[docs]</a> <span class="k">def</span> <span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span> <div class="viewcode-block" id="X.condition">
<span class="k">return</span> <span class="nb">abs</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="p">(</span><span class="n">tpr</span> <span class="o">+</span> <span class="n">fpr</span><span class="p">))</span></div></div> <a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.X.condition">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
<span class="k">return</span> <span class="nb">abs</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="p">(</span><span class="n">tpr</span> <span class="o">+</span> <span class="n">fpr</span><span class="p">))</span></div>
</div>
<div class="viewcode-block" id="MS"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS">[docs]</a><span class="k">class</span> <span class="nc">MS</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
<div class="viewcode-block" id="MS">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">MS</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Median Sweep. Threshold Optimization variant for :class:`ACC` as proposed by</span> <span class="sd"> Median Sweep. Threshold Optimization variant for :class:`ACC` as proposed by</span>
<span class="sd"> `Forman 2006 &lt;https://dl.acm.org/doi/abs/10.1145/1150402.1150423&gt;`_ and</span> <span class="sd"> `Forman 2006 &lt;https://dl.acm.org/doi/abs/10.1145/1150402.1150423&gt;`_ and</span>
@ -280,22 +337,30 @@
<span class="sd"> class prevalence estimates for all decision thresholds and returns the median of them all.</span> <span class="sd"> class prevalence estimates for all decision thresholds and returns the median of them all.</span>
<span class="sd"> The goal is to bring improved stability to the denominator of the adjustment.</span> <span class="sd"> The goal is to bring improved stability to the denominator of the adjustment.</span>
<span class="sd"> :param classifier: a sklearn&#39;s Estimator that generates a classifier</span> <span class="sd"> :param classifier: a scikit-learn&#39;s BaseEstimator, or None, in which case the classifier is taken to be</span>
<span class="sd"> :param val_split: indicates the proportion of data to be used as a stratified held-out validation set in which the</span> <span class="sd"> the one indicated in `qp.environ[&#39;DEFAULT_CLS&#39;]`</span>
<span class="sd"> misclassification rates are to be estimated.</span> <span class="sd"> :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
<span class="sd"> This parameter can be indicated as a real value (between 0 and 1), representing a proportion of</span> <span class="sd"> learner has been trained outside the quantifier.</span>
<span class="sd"> validation data, or as an integer, indicating that the misclassification rates should be estimated via</span> <span class="sd"> :param val_split: specifies the data used for generating classifier predictions. This specification</span>
<span class="sd"> `k`-fold cross validation (this integer stands for the number of folds `k`, defaults 5), or as a</span> <span class="sd"> can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span>
<span class="sd"> :class:`quapy.data.base.LabelledCollection` (the split itself).</span> <span class="sd"> be extracted from the training set; or as an integer (default 5), indicating that the predictions</span>
<span class="sd"> are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span>
<span class="sd"> for `k`); or as a tuple (X,y) defining the specific set of data to use for validation.</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
<div class="viewcode-block" id="MS.condition"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS.condition">[docs]</a> <span class="k">def</span> <span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
<div class="viewcode-block" id="MS.condition">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS.condition">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
<span class="k">return</span> <span class="mi">1</span></div> <span class="k">return</span> <span class="mi">1</span></div>
<div class="viewcode-block" id="MS.aggregation_fit"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS.aggregation_fit">[docs]</a> <span class="k">def</span> <span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
<span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">classif_predictions</span><span class="o">.</span><span class="n">Xy</span> <div class="viewcode-block" id="MS.aggregation_fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS.aggregation_fit">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span>
<span class="c1"># keeps all candidates</span> <span class="c1"># keeps all candidates</span>
<span class="n">tprs_fprs_thresholds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_eval_candidate_thresholds</span><span class="p">(</span><span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="n">tprs_fprs_thresholds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_eval_candidate_thresholds</span><span class="p">(</span><span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tprs</span> <span class="o">=</span> <span class="n">tprs_fprs_thresholds</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="bp">self</span><span class="o">.</span><span class="n">tprs</span> <span class="o">=</span> <span class="n">tprs_fprs_thresholds</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span>
@ -303,14 +368,21 @@
<span class="bp">self</span><span class="o">.</span><span class="n">thresholds</span> <span class="o">=</span> <span class="n">tprs_fprs_thresholds</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span> <span class="bp">self</span><span class="o">.</span><span class="n">thresholds</span> <span class="o">=</span> <span class="n">tprs_fprs_thresholds</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span>
<span class="k">return</span> <span class="bp">self</span></div> <span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="MS.aggregate"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS.aggregate">[docs]</a> <span class="k">def</span> <span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
<div class="viewcode-block" id="MS.aggregate">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS.aggregate">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
<span class="n">prevalences</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aggregate_with_threshold</span><span class="p">(</span><span class="n">classif_predictions</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tprs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fprs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">thresholds</span><span class="p">)</span> <span class="n">prevalences</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aggregate_with_threshold</span><span class="p">(</span><span class="n">classif_predictions</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tprs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fprs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">thresholds</span><span class="p">)</span>
<span class="k">if</span> <span class="n">prevalences</span><span class="o">.</span><span class="n">ndim</span><span class="o">==</span><span class="mi">2</span><span class="p">:</span> <span class="k">if</span> <span class="n">prevalences</span><span class="o">.</span><span class="n">ndim</span><span class="o">==</span><span class="mi">2</span><span class="p">:</span>
<span class="n">prevalences</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">prevalences</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="n">prevalences</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">prevalences</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="k">return</span> <span class="n">prevalences</span></div></div> <span class="k">return</span> <span class="n">prevalences</span></div>
</div>
<div class="viewcode-block" id="MS2"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS2">[docs]</a><span class="k">class</span> <span class="nc">MS2</span><span class="p">(</span><span class="n">MS</span><span class="p">):</span>
<div class="viewcode-block" id="MS2">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS2">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">MS2</span><span class="p">(</span><span class="n">MS</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Median Sweep 2. Threshold Optimization variant for :class:`ACC` as proposed by</span> <span class="sd"> Median Sweep 2. Threshold Optimization variant for :class:`ACC` as proposed by</span>
<span class="sd"> `Forman 2006 &lt;https://dl.acm.org/doi/abs/10.1145/1150402.1150423&gt;`_ and</span> <span class="sd"> `Forman 2006 &lt;https://dl.acm.org/doi/abs/10.1145/1150402.1150423&gt;`_ and</span>
@ -319,19 +391,26 @@
<span class="sd"> which `tpr-fpr&gt;0.25`</span> <span class="sd"> which `tpr-fpr&gt;0.25`</span>
<span class="sd"> The goal is to bring improved stability to the denominator of the adjustment.</span> <span class="sd"> The goal is to bring improved stability to the denominator of the adjustment.</span>
<span class="sd"> :param classifier: a sklearn&#39;s Estimator that generates a classifier</span> <span class="sd"> :param classifier: a scikit-learn&#39;s BaseEstimator, or None, in which case the classifier is taken to be</span>
<span class="sd"> :param val_split: indicates the proportion of data to be used as a stratified held-out validation set in which the</span> <span class="sd"> the one indicated in `qp.environ[&#39;DEFAULT_CLS&#39;]`</span>
<span class="sd"> misclassification rates are to be estimated.</span> <span class="sd"> :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
<span class="sd"> This parameter can be indicated as a real value (between 0 and 1), representing a proportion of</span> <span class="sd"> learner has been trained outside the quantifier.</span>
<span class="sd"> validation data, or as an integer, indicating that the misclassification rates should be estimated via</span> <span class="sd"> :param val_split: specifies the data used for generating classifier predictions. This specification</span>
<span class="sd"> `k`-fold cross validation (this integer stands for the number of folds `k`, defaults 5), or as a</span> <span class="sd"> can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span>
<span class="sd"> :class:`quapy.data.base.LabelledCollection` (the split itself).</span> <span class="sd"> be extracted from the training set; or as an integer (default 5), indicating that the predictions</span>
<span class="sd"> are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span>
<span class="sd"> for `k`); or as a tuple (X,y) defining the specific set of data to use for validation.</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
<div class="viewcode-block" id="MS2.discard"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS2.discard">[docs]</a> <span class="k">def</span> <span class="nf">discard</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="k">return</span> <span class="p">(</span><span class="n">tpr</span><span class="o">-</span><span class="n">fpr</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="mf">0.25</span></div></div> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
<div class="viewcode-block" id="MS2.discard">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS2.discard">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">discard</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
<span class="k">return</span> <span class="p">(</span><span class="n">tpr</span><span class="o">-</span><span class="n">fpr</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="mf">0.25</span></div>
</div>
</pre></div> </pre></div>
</div> </div>

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docs/build/html/_modules/quapy/method/aggregative.html vendored
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158
docs/build/html/_modules/quapy/method/base.html vendored
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@ -1,23 +1,20 @@
<!DOCTYPE html> <!DOCTYPE html>
<html class="writer-html5" lang="en"> <html class="writer-html5" lang="en" data-content_root="../../../">
<head> <head>
<meta charset="utf-8" /> <meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" /> <meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>quapy.method.base &mdash; QuaPy: A Python-based open-source framework for quantification 0.1.8 documentation</title> <title>quapy.method.base &mdash; QuaPy: A Python-based open-source framework for quantification 0.2.1 documentation</title>
<link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" /> <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=b86133f3" />
<link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" /> <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css?v=9edc463e" />
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<script src="../../../_static/js/html5shiv.min.js"></script> <script src="../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
<![endif]--> <script src="../../../_static/documentation_options.js?v=37f418d5"></script>
<script src="../../../_static/doctools.js?v=fd6eb6e6"></script>
<script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script> <script src="../../../_static/sphinx_highlight.js?v=6ffebe34"></script>
<script src="../../../_static/jquery.js"></script>
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<link rel="index" title="Index" href="../../../genindex.html" /> <link rel="index" title="Index" href="../../../genindex.html" />
<link rel="search" title="Search" href="../../../search.html" /> <link rel="search" title="Search" href="../../../search.html" />
@ -43,7 +40,13 @@
</div> </div>
</div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu"> </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
<ul> <ul>
<li class="toctree-l1"><a class="reference internal" href="../../../modules.html">quapy</a></li> <li class="toctree-l1"><a class="reference internal" href="../../../index.html">Quickstart</a></li>
</ul>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../manuals.html">Manuals</a></li>
</ul>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../../quapy.html">API</a></li>
</ul> </ul>
</div> </div>
@ -71,63 +74,94 @@
<div itemprop="articleBody"> <div itemprop="articleBody">
<h1>Source code for quapy.method.base</h1><div class="highlight"><pre> <h1>Source code for quapy.method.base</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">ABCMeta</span><span class="p">,</span> <span class="n">abstractmethod</span> <span></span><span class="kn">from</span><span class="w"> </span><span class="nn">abc</span><span class="w"> </span><span class="kn">import</span> <span class="n">ABCMeta</span><span class="p">,</span> <span class="n">abstractmethod</span>
<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span> <span class="kn">from</span><span class="w"> </span><span class="nn">copy</span><span class="w"> </span><span class="kn">import</span> <span class="n">deepcopy</span>
<span class="kn">from</span> <span class="nn">joblib</span> <span class="kn">import</span> <span class="n">Parallel</span><span class="p">,</span> <span class="n">delayed</span> <span class="kn">from</span><span class="w"> </span><span class="nn">joblib</span><span class="w"> </span><span class="kn">import</span> <span class="n">Parallel</span><span class="p">,</span> <span class="n">delayed</span>
<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span> <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseEstimator</span>
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span> <span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
<span class="kn">from</span> <span class="nn">quapy.data</span> <span class="kn">import</span> <span class="n">LabelledCollection</span> <span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data</span><span class="w"> </span><span class="kn">import</span> <span class="n">LabelledCollection</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="c1"># Base Quantifier abstract class</span> <span class="c1"># Base Quantifier abstract class</span>
<span class="c1"># ------------------------------------</span> <span class="c1"># ------------------------------------</span>
<div class="viewcode-block" id="BaseQuantifier"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BaseQuantifier">[docs]</a><span class="k">class</span> <span class="nc">BaseQuantifier</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">):</span> <div class="viewcode-block" id="BaseQuantifier">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BaseQuantifier">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">BaseQuantifier</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Abstract Quantifier. A quantifier is defined as an object of a class that implements the method :meth:`fit` on</span> <span class="sd"> Abstract Quantifier. A quantifier is defined as an object of a class that implements the method :meth:`fit` on</span>
<span class="sd"> :class:`quapy.data.base.LabelledCollection`, the method :meth:`quantify`, and the :meth:`set_params` and</span> <span class="sd"> a pair X, y, the method :meth:`predict`, and the :meth:`set_params` and</span>
<span class="sd"> :meth:`get_params` for model selection (see :meth:`quapy.model_selection.GridSearchQ`)</span> <span class="sd"> :meth:`get_params` for model selection (see :meth:`quapy.model_selection.GridSearchQ`)</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<div class="viewcode-block" id="BaseQuantifier.fit"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BaseQuantifier.fit">[docs]</a> <span class="nd">@abstractmethod</span> <div class="viewcode-block" id="BaseQuantifier.fit">
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span> <a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BaseQuantifier.fit">[docs]</a>
<span class="nd">@abstractmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Trains a quantifier.</span> <span class="sd"> Generates a quantifier.</span>
<span class="sd"> :param data: a :class:`quapy.data.base.LabelledCollection` consisting of the training data</span> <span class="sd"> :param X: array-like, the training instances</span>
<span class="sd"> :param y: array-like, the labels</span>
<span class="sd"> :return: self</span> <span class="sd"> :return: self</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="o">...</span></div> <span class="o">...</span></div>
<div class="viewcode-block" id="BaseQuantifier.quantify"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BaseQuantifier.quantify">[docs]</a> <span class="nd">@abstractmethod</span>
<span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span> <div class="viewcode-block" id="BaseQuantifier.predict">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BaseQuantifier.predict">[docs]</a>
<span class="nd">@abstractmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Generate class prevalence estimates for the sample&#39;s instances</span> <span class="sd"> Generate class prevalence estimates for the sample&#39;s instances</span>
<span class="sd"> :param instances: array-like</span> <span class="sd"> :param X: array-like, the test instances</span>
<span class="sd"> :return: `np.ndarray` of shape `(n_classes,)` with class prevalence estimates.</span> <span class="sd"> :return: `np.ndarray` of shape `(n_classes,)` with class prevalence estimates.</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="o">...</span></div></div> <span class="o">...</span></div>
<div class="viewcode-block" id="BinaryQuantifier"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BinaryQuantifier">[docs]</a><span class="k">class</span> <span class="nc">BinaryQuantifier</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span> <div class="viewcode-block" id="BaseQuantifier.quantify">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BaseQuantifier.quantify">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Alias to :meth:`predict`, for old compatibility</span>
<span class="sd"> :param X: array-like</span>
<span class="sd"> :return: `np.ndarray` of shape `(n_classes,)` with class prevalence estimates.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
</div>
<div class="viewcode-block" id="BinaryQuantifier">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BinaryQuantifier">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">BinaryQuantifier</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Abstract class of binary quantifiers, i.e., quantifiers estimating class prevalence values for only two classes</span> <span class="sd"> Abstract class of binary quantifiers, i.e., quantifiers estimating class prevalence values for only two classes</span>
<span class="sd"> (typically, to be interpreted as one class and its complement).</span> <span class="sd"> (typically, to be interpreted as one class and its complement).</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">_check_binary</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">quantifier_name</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">_check_binary</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">quantifier_name</span><span class="p">):</span>
<span class="k">assert</span> <span class="n">data</span><span class="o">.</span><span class="n">binary</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">quantifier_name</span><span class="si">}</span><span class="s1"> works only on problems of binary classification. &#39;</span> \ <span class="n">n_classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
<span class="k">assert</span> <span class="n">n_classes</span><span class="o">==</span><span class="mi">2</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">quantifier_name</span><span class="si">}</span><span class="s1"> works only on problems of binary classification. &#39;</span> \
<span class="sa">f</span><span class="s1">&#39;Use the class OneVsAll to enable </span><span class="si">{</span><span class="n">quantifier_name</span><span class="si">}</span><span class="s1"> work on single-label data.&#39;</span></div> <span class="sa">f</span><span class="s1">&#39;Use the class OneVsAll to enable </span><span class="si">{</span><span class="n">quantifier_name</span><span class="si">}</span><span class="s1"> work on single-label data.&#39;</span></div>
<div class="viewcode-block" id="OneVsAll"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.OneVsAll">[docs]</a><span class="k">class</span> <span class="nc">OneVsAll</span><span class="p">:</span>
<div class="viewcode-block" id="OneVsAll">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.OneVsAll">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">OneVsAll</span><span class="p">:</span>
<span class="k">pass</span></div> <span class="k">pass</span></div>
<div class="viewcode-block" id="newOneVsAll"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.newOneVsAll">[docs]</a><span class="k">def</span> <span class="nf">newOneVsAll</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<div class="viewcode-block" id="newOneVsAll">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.newOneVsAll">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">newOneVsAll</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">BaseQuantifier</span><span class="p">),</span> \ <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">BaseQuantifier</span><span class="p">),</span> \
<span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">binary_quantifier</span><span class="si">}</span><span class="s1"> does not seem to be a Quantifier&#39;</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">binary_quantifier</span><span class="si">}</span><span class="s1"> does not seem to be a Quantifier&#39;</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">qp</span><span class="o">.</span><span class="n">method</span><span class="o">.</span><span class="n">aggregative</span><span class="o">.</span><span class="n">AggregativeQuantifier</span><span class="p">):</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">qp</span><span class="o">.</span><span class="n">method</span><span class="o">.</span><span class="n">aggregative</span><span class="o">.</span><span class="n">AggregativeQuantifier</span><span class="p">):</span>
@ -136,13 +170,16 @@
<span class="k">return</span> <span class="n">OneVsAllGeneric</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">n_jobs</span><span class="p">)</span></div> <span class="k">return</span> <span class="n">OneVsAllGeneric</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">n_jobs</span><span class="p">)</span></div>
<div class="viewcode-block" id="OneVsAllGeneric"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.OneVsAllGeneric">[docs]</a><span class="k">class</span> <span class="nc">OneVsAllGeneric</span><span class="p">(</span><span class="n">OneVsAll</span><span class="p">,</span> <span class="n">BaseQuantifier</span><span class="p">):</span>
<div class="viewcode-block" id="OneVsAllGeneric">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.OneVsAllGeneric">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">OneVsAllGeneric</span><span class="p">(</span><span class="n">OneVsAll</span><span class="p">,</span> <span class="n">BaseQuantifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Allows any binary quantifier to perform quantification on single-label datasets. The method maintains one binary</span> <span class="sd"> Allows any binary quantifier to perform quantification on single-label datasets. The method maintains one binary</span>
<span class="sd"> quantifier for each class, and then l1-normalizes the outputs so that the class prevelence values sum up to 1.</span> <span class="sd"> quantifier for each class, and then l1-normalizes the outputs so that the class prevalence values sum up to 1.</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">binary_quantifier</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">BaseQuantifier</span><span class="p">),</span> \ <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">BaseQuantifier</span><span class="p">),</span> \
<span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">binary_quantifier</span><span class="si">}</span><span class="s1"> does not seem to be a Quantifier&#39;</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">binary_quantifier</span><span class="si">}</span><span class="s1"> does not seem to be a Quantifier&#39;</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">qp</span><span class="o">.</span><span class="n">method</span><span class="o">.</span><span class="n">aggregative</span><span class="o">.</span><span class="n">AggregativeQuantifier</span><span class="p">):</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">qp</span><span class="o">.</span><span class="n">method</span><span class="o">.</span><span class="n">aggregative</span><span class="o">.</span><span class="n">AggregativeQuantifier</span><span class="p">):</span>
@ -151,35 +188,42 @@
<span class="bp">self</span><span class="o">.</span><span class="n">binary_quantifier</span> <span class="o">=</span> <span class="n">binary_quantifier</span> <span class="bp">self</span><span class="o">.</span><span class="n">binary_quantifier</span> <span class="o">=</span> <span class="n">binary_quantifier</span>
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_njobs</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_njobs</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)</span>
<div class="viewcode-block" id="OneVsAllGeneric.fit"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.OneVsAllGeneric.fit">[docs]</a> <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> <div class="viewcode-block" id="OneVsAllGeneric.fit">
<span class="k">assert</span> <span class="ow">not</span> <span class="n">data</span><span class="o">.</span><span class="n">binary</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1"> expect non-binary data&#39;</span> <a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.OneVsAllGeneric.fit">[docs]</a>
<span class="k">assert</span> <span class="n">fit_classifier</span> <span class="o">==</span> <span class="kc">True</span><span class="p">,</span> <span class="s1">&#39;fit_classifier must be True&#39;</span> <span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classes</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes</span><span class="p">)</span><span class="o">!=</span><span class="mi">2</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1"> expect non-binary data&#39;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dict_binary_quantifiers</span> <span class="o">=</span> <span class="p">{</span><span class="n">c</span><span class="p">:</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">binary_quantifier</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">classes_</span><span class="p">}</span> <span class="bp">self</span><span class="o">.</span><span class="n">dict_binary_quantifiers</span> <span class="o">=</span> <span class="p">{</span><span class="n">c</span><span class="p">:</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">binary_quantifier</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">classes</span><span class="p">}</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_parallel</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_delayed_binary_fit</span><span class="p">,</span> <span class="n">data</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">_parallel</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_delayed_binary_fit</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span></div> <span class="k">return</span> <span class="bp">self</span></div>
<span class="k">def</span> <span class="nf">_parallel</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_parallel</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span> <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span>
<span class="n">Parallel</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s1">&#39;threading&#39;</span><span class="p">)(</span> <span class="n">Parallel</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s1">&#39;threading&#39;</span><span class="p">)(</span>
<span class="n">delayed</span><span class="p">(</span><span class="n">func</span><span class="p">)(</span><span class="n">c</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="n">delayed</span><span class="p">(</span><span class="n">func</span><span class="p">)(</span><span class="n">c</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">classes</span>
<span class="p">)</span> <span class="p">)</span>
<span class="p">)</span> <span class="p">)</span>
<div class="viewcode-block" id="OneVsAllGeneric.quantify"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.OneVsAllGeneric.quantify">[docs]</a> <span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span> <div class="viewcode-block" id="OneVsAllGeneric.predict">
<span class="n">prevalences</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_parallel</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_delayed_binary_predict</span><span class="p">,</span> <span class="n">instances</span><span class="p">)</span> <a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.OneVsAllGeneric.predict">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="n">prevalences</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_parallel</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_delayed_binary_predict</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
<span class="k">return</span> <span class="n">qp</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">normalize_prevalence</span><span class="p">(</span><span class="n">prevalences</span><span class="p">)</span></div> <span class="k">return</span> <span class="n">qp</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">normalize_prevalence</span><span class="p">(</span><span class="n">prevalences</span><span class="p">)</span></div>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">classes_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dict_binary_quantifiers</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
<span class="k">def</span> <span class="nf">_delayed_binary_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span> <span class="c1"># @property</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">dict_binary_quantifiers</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">X</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="c1"># def classes_(self):</span>
<span class="c1"># return sorted(self.dict_binary_quantifiers.keys())</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_delayed_binary_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">dict_binary_quantifiers</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_delayed_binary_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">bindata</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">==</span> <span class="n">c</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="p">[</span><span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">])</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dict_binary_quantifiers</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="o">*</span><span class="n">bindata</span><span class="o">.</span><span class="n">Xy</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">_delayed_binary_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
<span class="n">bindata</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">labels</span> <span class="o">==</span> <span class="n">c</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="p">[</span><span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">])</span>
<span class="bp">self</span><span class="o">.</span><span class="n">dict_binary_quantifiers</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">bindata</span><span class="p">)</span></div>
</pre></div> </pre></div>
</div> </div>

456
docs/build/html/_modules/quapy/method/meta.html vendored
View File

@ -1,23 +1,20 @@
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@ -43,7 +40,13 @@
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@ -71,24 +74,24 @@
<div itemprop="articleBody"> <div itemprop="articleBody">
<h1>Source code for quapy.method.meta</h1><div class="highlight"><pre> <h1>Source code for quapy.method.meta</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">itertools</span> <span></span><span class="kn">import</span><span class="w"> </span><span class="nn">itertools</span>
<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span> <span class="kn">from</span><span class="w"> </span><span class="nn">copy</span><span class="w"> </span><span class="kn">import</span> <span class="n">deepcopy</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span> <span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">List</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span> <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LogisticRegression</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">f1_score</span><span class="p">,</span> <span class="n">make_scorer</span><span class="p">,</span> <span class="n">accuracy_score</span> <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">f1_score</span><span class="p">,</span> <span class="n">make_scorer</span><span class="p">,</span> <span class="n">accuracy_score</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">GridSearchCV</span><span class="p">,</span> <span class="n">cross_val_predict</span> <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">GridSearchCV</span><span class="p">,</span> <span class="n">cross_val_predict</span>
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span> <span class="kn">from</span><span class="w"> </span><span class="nn">tqdm</span><span class="w"> </span><span class="kn">import</span> <span class="n">tqdm</span>
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span> <span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
<span class="kn">from</span> <span class="nn">quapy</span> <span class="kn">import</span> <span class="n">functional</span> <span class="k">as</span> <span class="n">F</span> <span class="kn">from</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="kn">import</span> <span class="n">functional</span> <span class="k">as</span> <span class="n">F</span>
<span class="kn">from</span> <span class="nn">quapy.data</span> <span class="kn">import</span> <span class="n">LabelledCollection</span> <span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data</span><span class="w"> </span><span class="kn">import</span> <span class="n">LabelledCollection</span>
<span class="kn">from</span> <span class="nn">quapy.model_selection</span> <span class="kn">import</span> <span class="n">GridSearchQ</span> <span class="kn">from</span><span class="w"> </span><span class="nn">quapy.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">GridSearchQ</span>
<span class="kn">from</span> <span class="nn">quapy.method.base</span> <span class="kn">import</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">BinaryQuantifier</span> <span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">BinaryQuantifier</span>
<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="n">CC</span><span class="p">,</span> <span class="n">ACC</span><span class="p">,</span> <span class="n">PACC</span><span class="p">,</span> <span class="n">HDy</span><span class="p">,</span> <span class="n">EMQ</span><span class="p">,</span> <span class="n">AggregativeQuantifier</span> <span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.aggregative</span><span class="w"> </span><span class="kn">import</span> <span class="n">CC</span><span class="p">,</span> <span class="n">ACC</span><span class="p">,</span> <span class="n">PACC</span><span class="p">,</span> <span class="n">HDy</span><span class="p">,</span> <span class="n">EMQ</span><span class="p">,</span> <span class="n">AggregativeQuantifier</span><span class="p">,</span> <span class="n">AggregativeSoftQuantifier</span>
<span class="k">try</span><span class="p">:</span> <span class="k">try</span><span class="p">:</span>
<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_neural</span> <span class="kn">from</span><span class="w"> </span><span class="nn">.</span><span class="w"> </span><span class="kn">import</span> <span class="n">_neural</span>
<span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span> <span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
<span class="n">_neural</span> <span class="o">=</span> <span class="kc">None</span> <span class="n">_neural</span> <span class="o">=</span> <span class="kc">None</span>
@ -99,7 +102,9 @@
<span class="n">QuaNet</span> <span class="o">=</span> <span class="s2">&quot;QuaNet is not available due to missing torch package&quot;</span> <span class="n">QuaNet</span> <span class="o">=</span> <span class="s2">&quot;QuaNet is not available due to missing torch package&quot;</span>
<div class="viewcode-block" id="MedianEstimator2"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator2">[docs]</a><span class="k">class</span> <span class="nc">MedianEstimator2</span><span class="p">(</span><span class="n">BinaryQuantifier</span><span class="p">):</span> <div class="viewcode-block" id="MedianEstimator2">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator2">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">MedianEstimator2</span><span class="p">(</span><span class="n">BinaryQuantifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> This method is a meta-quantifier that returns, as the estimated class prevalence values, the median of the</span> <span class="sd"> This method is a meta-quantifier that returns, as the estimated class prevalence values, the median of the</span>
<span class="sd"> estimation returned by differently (hyper)parameterized base quantifiers.</span> <span class="sd"> estimation returned by differently (hyper)parameterized base quantifiers.</span>
@ -111,54 +116,69 @@
<span class="sd"> :param param_grid: the grid or parameters towards which the median will be computed</span> <span class="sd"> :param param_grid: the grid or parameters towards which the median will be computed</span>
<span class="sd"> :param n_jobs: number of parllel workes</span> <span class="sd"> :param n_jobs: number of parllel workes</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base_quantifier</span><span class="p">:</span> <span class="n">BinaryQuantifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base_quantifier</span><span class="p">:</span> <span class="n">BinaryQuantifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span> <span class="o">=</span> <span class="n">base_quantifier</span> <span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span> <span class="o">=</span> <span class="n">base_quantifier</span>
<span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span> <span class="o">=</span> <span class="n">param_grid</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span> <span class="o">=</span> <span class="n">param_grid</span>
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_njobs</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_njobs</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)</span>
<div class="viewcode-block" id="MedianEstimator2.get_params"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator2.get_params">[docs]</a> <span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> <div class="viewcode-block" id="MedianEstimator2.get_params">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator2.get_params">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="o">.</span><span class="n">get_params</span><span class="p">(</span><span class="n">deep</span><span class="p">)</span></div> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="o">.</span><span class="n">get_params</span><span class="p">(</span><span class="n">deep</span><span class="p">)</span></div>
<div class="viewcode-block" id="MedianEstimator2.set_params"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator2.set_params">[docs]</a> <span class="k">def</span> <span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">params</span><span class="p">):</span>
<div class="viewcode-block" id="MedianEstimator2.set_params">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator2.set_params">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">params</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span></div> <span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">_delayed_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_delayed_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">):</span> <span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">):</span>
<span class="n">params</span><span class="p">,</span> <span class="n">training</span> <span class="o">=</span> <span class="n">args</span> <span class="n">params</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">args</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="p">)</span> <span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span> <span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">training</span><span class="p">)</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="k">return</span> <span class="n">model</span> <span class="k">return</span> <span class="n">model</span>
<div class="viewcode-block" id="MedianEstimator2.fit"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator2.fit">[docs]</a> <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">training</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span> <div class="viewcode-block" id="MedianEstimator2.fit">
<span class="bp">self</span><span class="o">.</span><span class="n">_check_binary</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span> <a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator2.fit">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_check_binary</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
<span class="n">configs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">model_selection</span><span class="o">.</span><span class="n">expand_grid</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span><span class="p">)</span> <span class="n">configs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">model_selection</span><span class="o">.</span><span class="n">expand_grid</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">models</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span> <span class="bp">self</span><span class="o">.</span><span class="n">models</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_delayed_fit</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_delayed_fit</span><span class="p">,</span>
<span class="p">((</span><span class="n">params</span><span class="p">,</span> <span class="n">training</span><span class="p">)</span> <span class="k">for</span> <span class="n">params</span> <span class="ow">in</span> <span class="n">configs</span><span class="p">),</span> <span class="p">((</span><span class="n">params</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="k">for</span> <span class="n">params</span> <span class="ow">in</span> <span class="n">configs</span><span class="p">),</span>
<span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;_R_SEED&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span> <span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;_R_SEED&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
<span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span>
<span class="p">)</span> <span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span></div> <span class="k">return</span> <span class="bp">self</span></div>
<span class="k">def</span> <span class="nf">_delayed_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="n">model</span><span class="p">,</span> <span class="n">instances</span> <span class="o">=</span> <span class="n">args</span>
<span class="k">return</span> <span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
<div class="viewcode-block" id="MedianEstimator2.quantify"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator2.quantify">[docs]</a> <span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">_delayed_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="n">model</span><span class="p">,</span> <span class="n">instances</span> <span class="o">=</span> <span class="n">args</span>
<span class="k">return</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
<div class="viewcode-block" id="MedianEstimator2.predict">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator2.predict">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="n">prev_preds</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span> <span class="n">prev_preds</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_delayed_predict</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_delayed_predict</span><span class="p">,</span>
<span class="p">((</span><span class="n">model</span><span class="p">,</span> <span class="n">instances</span><span class="p">)</span> <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="p">),</span> <span class="p">((</span><span class="n">model</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span> <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="p">),</span>
<span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;_R_SEED&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span> <span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;_R_SEED&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
<span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span>
<span class="p">)</span> <span class="p">)</span>
<span class="n">prev_preds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prev_preds</span><span class="p">)</span> <span class="n">prev_preds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prev_preds</span><span class="p">)</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">prev_preds</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span></div></div> <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">prev_preds</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span></div>
</div>
<div class="viewcode-block" id="MedianEstimator"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator">[docs]</a><span class="k">class</span> <span class="nc">MedianEstimator</span><span class="p">(</span><span class="n">BinaryQuantifier</span><span class="p">):</span>
<div class="viewcode-block" id="MedianEstimator">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">MedianEstimator</span><span class="p">(</span><span class="n">BinaryQuantifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> This method is a meta-quantifier that returns, as the estimated class prevalence values, the median of the</span> <span class="sd"> This method is a meta-quantifier that returns, as the estimated class prevalence values, the median of the</span>
<span class="sd"> estimation returned by differently (hyper)parameterized base quantifiers.</span> <span class="sd"> estimation returned by differently (hyper)parameterized base quantifiers.</span>
@ -168,100 +188,73 @@
<span class="sd"> :param base_quantifier: the base, binary quantifier</span> <span class="sd"> :param base_quantifier: the base, binary quantifier</span>
<span class="sd"> :param random_state: a seed to be set before fitting any base quantifier (default None)</span> <span class="sd"> :param random_state: a seed to be set before fitting any base quantifier (default None)</span>
<span class="sd"> :param param_grid: the grid or parameters towards which the median will be computed</span> <span class="sd"> :param param_grid: the grid or parameters towards which the median will be computed</span>
<span class="sd"> :param n_jobs: number of parllel workes</span> <span class="sd"> :param n_jobs: number of parallel workers</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base_quantifier</span><span class="p">:</span> <span class="n">BinaryQuantifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base_quantifier</span><span class="p">:</span> <span class="n">BinaryQuantifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span> <span class="o">=</span> <span class="n">base_quantifier</span> <span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span> <span class="o">=</span> <span class="n">base_quantifier</span>
<span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span> <span class="o">=</span> <span class="n">param_grid</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span> <span class="o">=</span> <span class="n">param_grid</span>
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_njobs</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_njobs</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)</span>
<div class="viewcode-block" id="MedianEstimator.get_params"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator.get_params">[docs]</a> <span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> <div class="viewcode-block" id="MedianEstimator.get_params">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator.get_params">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="o">.</span><span class="n">get_params</span><span class="p">(</span><span class="n">deep</span><span class="p">)</span></div> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="o">.</span><span class="n">get_params</span><span class="p">(</span><span class="n">deep</span><span class="p">)</span></div>
<div class="viewcode-block" id="MedianEstimator.set_params"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator.set_params">[docs]</a> <span class="k">def</span> <span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">params</span><span class="p">):</span>
<div class="viewcode-block" id="MedianEstimator.set_params">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator.set_params">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">params</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span></div> <span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">_delayed_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_delayed_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">):</span> <span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">):</span>
<span class="n">params</span><span class="p">,</span> <span class="n">training</span> <span class="o">=</span> <span class="n">args</span> <span class="n">params</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">args</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="p">)</span> <span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span> <span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">training</span><span class="p">)</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="k">return</span> <span class="n">model</span> <span class="k">return</span> <span class="n">model</span>
<span class="k">def</span> <span class="nf">_delayed_fit_classifier</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span> <div class="viewcode-block" id="MedianEstimator.fit">
<span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">):</span> <a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator.fit">[docs]</a>
<span class="n">cls_params</span><span class="p">,</span> <span class="n">training</span> <span class="o">=</span> <span class="n">args</span> <span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_binary</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">cls_params</span><span class="p">)</span>
<span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">classifier_fit_predict</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="n">predict_on</span><span class="o">=</span><span class="n">model</span><span class="o">.</span><span class="n">val_split</span><span class="p">)</span>
<span class="k">return</span> <span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">predictions</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_delayed_fit_aggregation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span> <span class="n">configs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">model_selection</span><span class="o">.</span><span class="n">expand_grid</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span><span class="p">)</span>
<span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">):</span> <span class="bp">self</span><span class="o">.</span><span class="n">models</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
<span class="p">((</span><span class="n">model</span><span class="p">,</span> <span class="n">predictions</span><span class="p">),</span> <span class="n">q_params</span><span class="p">),</span> <span class="n">training</span> <span class="o">=</span> <span class="n">args</span> <span class="bp">self</span><span class="o">.</span><span class="n">_delayed_fit</span><span class="p">,</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">model</span><span class="p">)</span> <span class="p">((</span><span class="n">params</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="k">for</span> <span class="n">params</span> <span class="ow">in</span> <span class="n">configs</span><span class="p">),</span>
<span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">q_params</span><span class="p">)</span> <span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;_R_SEED&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
<span class="n">model</span><span class="o">.</span><span class="n">aggregation_fit</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="n">training</span><span class="p">)</span> <span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">,</span>
<span class="k">return</span> <span class="n">model</span> <span class="n">asarray</span><span class="o">=</span><span class="kc">False</span>
<span class="p">)</span>
<div class="viewcode-block" id="MedianEstimator.fit"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator.fit">[docs]</a> <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">training</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_check_binary</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="p">,</span> <span class="n">AggregativeQuantifier</span><span class="p">):</span>
<span class="n">cls_configs</span><span class="p">,</span> <span class="n">q_configs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">model_selection</span><span class="o">.</span><span class="n">group_params</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">cls_configs</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">models_preds</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_delayed_fit_classifier</span><span class="p">,</span>
<span class="p">((</span><span class="n">params</span><span class="p">,</span> <span class="n">training</span><span class="p">)</span> <span class="k">for</span> <span class="n">params</span> <span class="ow">in</span> <span class="n">cls_configs</span><span class="p">),</span>
<span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;_R_SEED&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
<span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">,</span>
<span class="n">asarray</span><span class="o">=</span><span class="kc">False</span>
<span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span>
<span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">cls_configs</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">classifier_fit_predict</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="n">predict_on</span><span class="o">=</span><span class="n">model</span><span class="o">.</span><span class="n">val_split</span><span class="p">)</span>
<span class="n">models_preds</span> <span class="o">=</span> <span class="p">[(</span><span class="n">model</span><span class="p">,</span> <span class="n">predictions</span><span class="p">)]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">models</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_delayed_fit_aggregation</span><span class="p">,</span>
<span class="p">((</span><span class="n">setup</span><span class="p">,</span> <span class="n">training</span><span class="p">)</span> <span class="k">for</span> <span class="n">setup</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">product</span><span class="p">(</span><span class="n">models_preds</span><span class="p">,</span> <span class="n">q_configs</span><span class="p">)),</span>
<span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;_R_SEED&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
<span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">,</span>
<span class="n">asarray</span><span class="o">=</span><span class="kc">False</span>
<span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">configs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">model_selection</span><span class="o">.</span><span class="n">expand_grid</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">models</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_delayed_fit</span><span class="p">,</span>
<span class="p">((</span><span class="n">params</span><span class="p">,</span> <span class="n">training</span><span class="p">)</span> <span class="k">for</span> <span class="n">params</span> <span class="ow">in</span> <span class="n">configs</span><span class="p">),</span>
<span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;_R_SEED&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
<span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">,</span>
<span class="n">asarray</span><span class="o">=</span><span class="kc">False</span>
<span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span></div> <span class="k">return</span> <span class="bp">self</span></div>
<span class="k">def</span> <span class="nf">_delayed_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="n">model</span><span class="p">,</span> <span class="n">instances</span> <span class="o">=</span> <span class="n">args</span>
<span class="k">return</span> <span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
<div class="viewcode-block" id="MedianEstimator.quantify"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator.quantify">[docs]</a> <span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">_delayed_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="n">model</span><span class="p">,</span> <span class="n">instances</span> <span class="o">=</span> <span class="n">args</span>
<span class="k">return</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
<div class="viewcode-block" id="MedianEstimator.predict">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator.predict">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="n">prev_preds</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span> <span class="n">prev_preds</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_delayed_predict</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_delayed_predict</span><span class="p">,</span>
<span class="p">((</span><span class="n">model</span><span class="p">,</span> <span class="n">instances</span><span class="p">)</span> <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="p">),</span> <span class="p">((</span><span class="n">model</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span> <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="p">),</span>
<span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;_R_SEED&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span> <span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;_R_SEED&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
<span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">,</span>
<span class="n">asarray</span><span class="o">=</span><span class="kc">False</span> <span class="n">asarray</span><span class="o">=</span><span class="kc">False</span>
<span class="p">)</span> <span class="p">)</span>
<span class="n">prev_preds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prev_preds</span><span class="p">)</span> <span class="n">prev_preds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prev_preds</span><span class="p">)</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">prev_preds</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span></div></div> <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">prev_preds</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span></div>
</div>
<div class="viewcode-block" id="Ensemble"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.Ensemble">[docs]</a><span class="k">class</span> <span class="nc">Ensemble</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
<div class="viewcode-block" id="Ensemble">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.Ensemble">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">Ensemble</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
<span class="n">VALID_POLICIES</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;ave&#39;</span><span class="p">,</span> <span class="s1">&#39;ptr&#39;</span><span class="p">,</span> <span class="s1">&#39;ds&#39;</span><span class="p">}</span> <span class="o">|</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">QUANTIFICATION_ERROR_NAMES</span> <span class="n">VALID_POLICIES</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;ave&#39;</span><span class="p">,</span> <span class="s1">&#39;ptr&#39;</span><span class="p">,</span> <span class="s1">&#39;ds&#39;</span><span class="p">}</span> <span class="o">|</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">QUANTIFICATION_ERROR_NAMES</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
@ -301,7 +294,7 @@
<span class="sd"> :param verbose: set to True (default is False) to get some information in standard output</span> <span class="sd"> :param verbose: set to True (default is False) to get some information in standard output</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
<span class="n">quantifier</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">quantifier</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span>
<span class="n">size</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
<span class="n">red_size</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span> <span class="n">red_size</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span>
@ -326,17 +319,20 @@
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span>
<span class="bp">self</span><span class="o">.</span><span class="n">max_sample_size</span> <span class="o">=</span> <span class="n">max_sample_size</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_sample_size</span> <span class="o">=</span> <span class="n">max_sample_size</span>
<span class="k">def</span> <span class="nf">_sout</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">msg</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">_sout</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">msg</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;[Ensemble]&#39;</span> <span class="o">+</span> <span class="n">msg</span><span class="p">)</span> <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;[Ensemble]&#39;</span> <span class="o">+</span> <span class="n">msg</span><span class="p">)</span>
<div class="viewcode-block" id="Ensemble.fit"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.Ensemble.fit">[docs]</a> <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">val_split</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="p">,</span> <span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span> <div class="viewcode-block" id="Ensemble.fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.Ensemble.fit">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">policy</span> <span class="o">==</span> <span class="s1">&#39;ds&#39;</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">data</span><span class="o">.</span><span class="n">binary</span><span class="p">:</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">policy</span> <span class="o">==</span> <span class="s1">&#39;ds&#39;</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">data</span><span class="o">.</span><span class="n">binary</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;ds policy is only defined for binary quantification, but this dataset is not binary&#39;</span><span class="p">)</span> <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;ds policy is only defined for binary quantification, but this dataset is not binary&#39;</span><span class="p">)</span>
<span class="k">if</span> <span class="n">val_split</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="n">val_split</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">val_split</span>
<span class="n">val_split</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">val_split</span>
<span class="c1"># randomly chooses the prevalences for each member of the ensemble (preventing classes with less than</span> <span class="c1"># randomly chooses the prevalences for each member of the ensemble (preventing classes with less than</span>
<span class="c1"># min_pos positive examples)</span> <span class="c1"># min_pos positive examples)</span>
@ -367,20 +363,26 @@
<span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="s1">&#39;Fit [Done]&#39;</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="s1">&#39;Fit [Done]&#39;</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span></div> <span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="Ensemble.quantify"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.Ensemble.quantify">[docs]</a> <span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
<div class="viewcode-block" id="Ensemble.predict">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.Ensemble.predict">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="n">predictions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span> <span class="n">predictions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span>
<span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span><span class="n">_delayed_quantify</span><span class="p">,</span> <span class="p">((</span><span class="n">Qi</span><span class="p">,</span> <span class="n">instances</span><span class="p">)</span> <span class="k">for</span> <span class="n">Qi</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ensemble</span><span class="p">),</span> <span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">)</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span><span class="n">_delayed_quantify</span><span class="p">,</span> <span class="p">((</span><span class="n">Qi</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span> <span class="k">for</span> <span class="n">Qi</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ensemble</span><span class="p">),</span> <span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">)</span>
<span class="p">)</span> <span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">policy</span> <span class="o">==</span> <span class="s1">&#39;ptr&#39;</span><span class="p">:</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">policy</span> <span class="o">==</span> <span class="s1">&#39;ptr&#39;</span><span class="p">:</span>
<span class="n">predictions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ptr_policy</span><span class="p">(</span><span class="n">predictions</span><span class="p">)</span> <span class="n">predictions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ptr_policy</span><span class="p">(</span><span class="n">predictions</span><span class="p">)</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">policy</span> <span class="o">==</span> <span class="s1">&#39;ds&#39;</span><span class="p">:</span> <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">policy</span> <span class="o">==</span> <span class="s1">&#39;ds&#39;</span><span class="p">:</span>
<span class="n">predictions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ds_policy</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="n">instances</span><span class="p">)</span> <span class="n">predictions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ds_policy</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
<span class="n">predictions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="n">predictions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">normalize_prevalence</span><span class="p">(</span><span class="n">predictions</span><span class="p">)</span></div> <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">normalize_prevalence</span><span class="p">(</span><span class="n">predictions</span><span class="p">)</span></div>
<div class="viewcode-block" id="Ensemble.set_params"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.Ensemble.set_params">[docs]</a> <span class="k">def</span> <span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">):</span>
<div class="viewcode-block" id="Ensemble.set_params">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.Ensemble.set_params">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> This function should not be used within :class:`quapy.model_selection.GridSearchQ` (is here for compatibility</span> <span class="sd"> This function should not be used within :class:`quapy.model_selection.GridSearchQ` (is here for compatibility</span>
<span class="sd"> with the abstract class).</span> <span class="sd"> with the abstract class).</span>
@ -396,7 +398,10 @@
<span class="sa">f</span><span class="s1">&#39;or Ensemble(Q(GridSearchCV(l))) with Q a quantifier class that has a classifier &#39;</span> <span class="sa">f</span><span class="s1">&#39;or Ensemble(Q(GridSearchCV(l))) with Q a quantifier class that has a classifier &#39;</span>
<span class="sa">f</span><span class="s1">&#39;l optimized for classification (not recommended).&#39;</span><span class="p">)</span></div> <span class="sa">f</span><span class="s1">&#39;l optimized for classification (not recommended).&#39;</span><span class="p">)</span></div>
<div class="viewcode-block" id="Ensemble.get_params"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.Ensemble.get_params">[docs]</a> <span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<div class="viewcode-block" id="Ensemble.get_params">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.Ensemble.get_params">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> This function should not be used within :class:`quapy.model_selection.GridSearchQ` (is here for compatibility</span> <span class="sd"> This function should not be used within :class:`quapy.model_selection.GridSearchQ` (is here for compatibility</span>
<span class="sd"> with the abstract class).</span> <span class="sd"> with the abstract class).</span>
@ -410,13 +415,14 @@
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div> <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div>
<span class="k">def</span> <span class="nf">_accuracy_policy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">error_name</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_accuracy_policy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">error_name</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Selects the red_size best performant quantifiers in a static way (i.e., dropping all non-selected instances).</span> <span class="sd"> Selects the red_size best performant quantifiers in a static way (i.e., dropping all non-selected instances).</span>
<span class="sd"> For each model in the ensemble, the performance is measured in terms of _error_name_ on the quantification of</span> <span class="sd"> For each model in the ensemble, the performance is measured in terms of _error_name_ on the quantification of</span>
<span class="sd"> the samples used for training the rest of the models in the ensemble.</span> <span class="sd"> the samples used for training the rest of the models in the ensemble.</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="kn">from</span> <span class="nn">quapy.evaluation</span> <span class="kn">import</span> <span class="n">evaluate_on_samples</span> <span class="kn">from</span><span class="w"> </span><span class="nn">quapy.evaluation</span><span class="w"> </span><span class="kn">import</span> <span class="n">evaluate_on_samples</span>
<span class="n">error</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">from_name</span><span class="p">(</span><span class="n">error_name</span><span class="p">)</span> <span class="n">error</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">from_name</span><span class="p">(</span><span class="n">error_name</span><span class="p">)</span>
<span class="n">tests</span> <span class="o">=</span> <span class="p">[</span><span class="n">m</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ensemble</span><span class="p">]</span> <span class="n">tests</span> <span class="o">=</span> <span class="p">[</span><span class="n">m</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ensemble</span><span class="p">]</span>
<span class="n">scores</span> <span class="o">=</span> <span class="p">[]</span> <span class="n">scores</span> <span class="o">=</span> <span class="p">[]</span>
@ -426,7 +432,7 @@
<span class="bp">self</span><span class="o">.</span><span class="n">ensemble</span> <span class="o">=</span> <span class="n">_select_k</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ensemble</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">red_size</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">ensemble</span> <span class="o">=</span> <span class="n">_select_k</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ensemble</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">red_size</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_ptr_policy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">predictions</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">_ptr_policy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">predictions</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Selects the predictions made by models that have been trained on samples with a prevalence that is most similar</span> <span class="sd"> Selects the predictions made by models that have been trained on samples with a prevalence that is most similar</span>
<span class="sd"> to a first approximation of the test prevalence as made by all models in the ensemble.</span> <span class="sd"> to a first approximation of the test prevalence as made by all models in the ensemble.</span>
@ -437,7 +443,7 @@
<span class="n">order</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">ptr_differences</span><span class="p">)</span> <span class="n">order</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">ptr_differences</span><span class="p">)</span>
<span class="k">return</span> <span class="n">_select_k</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">red_size</span><span class="p">)</span> <span class="k">return</span> <span class="n">_select_k</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">red_size</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_ds_policy_get_posteriors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">_ds_policy_get_posteriors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> In the original article, there are some aspects regarding this method that are not mentioned. The paper says</span> <span class="sd"> In the original article, there are some aspects regarding this method that are not mentioned. The paper says</span>
<span class="sd"> that the distribution of posterior probabilities from training and test examples is compared by means of the</span> <span class="sd"> that the distribution of posterior probabilities from training and test examples is compared by means of the</span>
@ -468,7 +474,7 @@
<span class="k">return</span> <span class="n">posteriors</span><span class="p">,</span> <span class="n">posteriors_generator</span> <span class="k">return</span> <span class="n">posteriors</span><span class="p">,</span> <span class="n">posteriors_generator</span>
<span class="k">def</span> <span class="nf">_ds_policy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">test</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">_ds_policy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">test</span><span class="p">):</span>
<span class="n">test_posteriors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">post_proba_fn</span><span class="p">(</span><span class="n">test</span><span class="p">)</span> <span class="n">test_posteriors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">post_proba_fn</span><span class="p">(</span><span class="n">test</span><span class="p">)</span>
<span class="n">test_distribution</span> <span class="o">=</span> <span class="n">get_probability_distribution</span><span class="p">(</span><span class="n">test_posteriors</span><span class="p">)</span> <span class="n">test_distribution</span> <span class="o">=</span> <span class="n">get_probability_distribution</span><span class="p">(</span><span class="n">test_posteriors</span><span class="p">)</span>
<span class="n">tr_distributions</span> <span class="o">=</span> <span class="p">[</span><span class="n">m</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ensemble</span><span class="p">]</span> <span class="n">tr_distributions</span> <span class="o">=</span> <span class="p">[</span><span class="n">m</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ensemble</span><span class="p">]</span>
@ -477,7 +483,7 @@
<span class="k">return</span> <span class="n">_select_k</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">red_size</span><span class="p">)</span> <span class="k">return</span> <span class="n">_select_k</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">red_size</span><span class="p">)</span>
<span class="nd">@property</span> <span class="nd">@property</span>
<span class="k">def</span> <span class="nf">aggregative</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">aggregative</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Indicates that the quantifier is not aggregative.</span> <span class="sd"> Indicates that the quantifier is not aggregative.</span>
@ -486,7 +492,7 @@
<span class="k">return</span> <span class="kc">False</span> <span class="k">return</span> <span class="kc">False</span>
<span class="nd">@property</span> <span class="nd">@property</span>
<span class="k">def</span> <span class="nf">probabilistic</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">probabilistic</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Indicates that the quantifier is not probabilistic.</span> <span class="sd"> Indicates that the quantifier is not probabilistic.</span>
@ -495,7 +501,10 @@
<span class="k">return</span> <span class="kc">False</span></div> <span class="k">return</span> <span class="kc">False</span></div>
<div class="viewcode-block" id="get_probability_distribution"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.get_probability_distribution">[docs]</a><span class="k">def</span> <span class="nf">get_probability_distribution</span><span class="p">(</span><span class="n">posterior_probabilities</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">8</span><span class="p">):</span>
<div class="viewcode-block" id="get_probability_distribution">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.get_probability_distribution">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">get_probability_distribution</span><span class="p">(</span><span class="n">posterior_probabilities</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">8</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Gets a histogram out of the posterior probabilities (only for the binary case).</span> <span class="sd"> Gets a histogram out of the posterior probabilities (only for the binary case).</span>
@ -509,11 +518,12 @@
<span class="k">return</span> <span class="n">distribution</span></div> <span class="k">return</span> <span class="n">distribution</span></div>
<span class="k">def</span> <span class="nf">_select_k</span><span class="p">(</span><span class="n">elements</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_select_k</span><span class="p">(</span><span class="n">elements</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
<span class="k">return</span> <span class="p">[</span><span class="n">elements</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">order</span><span class="p">[:</span><span class="n">k</span><span class="p">]]</span> <span class="k">return</span> <span class="p">[</span><span class="n">elements</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">order</span><span class="p">[:</span><span class="n">k</span><span class="p">]]</span>
<span class="k">def</span> <span class="nf">_delayed_new_instance</span><span class="p">(</span><span class="n">args</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">_delayed_new_instance</span><span class="p">(</span><span class="n">args</span><span class="p">):</span>
<span class="n">base_quantifier</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">val_split</span><span class="p">,</span> <span class="n">prev</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">,</span> <span class="n">keep_samples</span><span class="p">,</span> <span class="n">verbose</span><span class="p">,</span> <span class="n">sample_size</span> <span class="o">=</span> <span class="n">args</span> <span class="n">base_quantifier</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">val_split</span><span class="p">,</span> <span class="n">prev</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">,</span> <span class="n">keep_samples</span><span class="p">,</span> <span class="n">verbose</span><span class="p">,</span> <span class="n">sample_size</span> <span class="o">=</span> <span class="n">args</span>
<span class="k">if</span> <span class="n">verbose</span><span class="p">:</span> <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">fit-start for prev </span><span class="si">{</span><span class="n">F</span><span class="o">.</span><span class="n">strprev</span><span class="p">(</span><span class="n">prev</span><span class="p">)</span><span class="si">}</span><span class="s1">, sample_size=</span><span class="si">{</span><span class="n">sample_size</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">fit-start for prev </span><span class="si">{</span><span class="n">F</span><span class="o">.</span><span class="n">strprev</span><span class="p">(</span><span class="n">prev</span><span class="p">)</span><span class="si">}</span><span class="s1">, sample_size=</span><span class="si">{</span><span class="n">sample_size</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
@ -528,25 +538,25 @@
<span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">sample_index</span><span class="p">)</span> <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">sample_index</span><span class="p">)</span>
<span class="k">if</span> <span class="n">val_split</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> <span class="k">if</span> <span class="n">val_split</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="n">val_split</span><span class="p">)</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="o">*</span><span class="n">sample</span><span class="o">.</span><span class="n">Xy</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="n">val_split</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span> <span class="k">else</span><span class="p">:</span>
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="o">*</span><span class="n">sample</span><span class="o">.</span><span class="n">Xy</span><span class="p">)</span>
<span class="n">tr_prevalence</span> <span class="o">=</span> <span class="n">sample</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span> <span class="n">tr_prevalence</span> <span class="o">=</span> <span class="n">sample</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span>
<span class="n">tr_distribution</span> <span class="o">=</span> <span class="n">get_probability_distribution</span><span class="p">(</span><span class="n">posteriors</span><span class="p">[</span><span class="n">sample_index</span><span class="p">])</span> <span class="k">if</span> <span class="p">(</span><span class="n">posteriors</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> <span class="k">else</span> <span class="kc">None</span> <span class="n">tr_distribution</span> <span class="o">=</span> <span class="n">get_probability_distribution</span><span class="p">(</span><span class="n">posteriors</span><span class="p">[</span><span class="n">sample_index</span><span class="p">])</span> <span class="k">if</span> <span class="p">(</span><span class="n">posteriors</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> <span class="k">else</span> <span class="kc">None</span>
<span class="k">if</span> <span class="n">verbose</span><span class="p">:</span> <span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">\--fit-ended for prev </span><span class="si">{</span><span class="n">F</span><span class="o">.</span><span class="n">strprev</span><span class="p">(</span><span class="n">prev</span><span class="p">)</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">--fit-ended for prev </span><span class="si">{</span><span class="n">F</span><span class="o">.</span><span class="n">strprev</span><span class="p">(</span><span class="n">prev</span><span class="p">)</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="k">return</span> <span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">tr_prevalence</span><span class="p">,</span> <span class="n">tr_distribution</span><span class="p">,</span> <span class="n">sample</span> <span class="k">if</span> <span class="n">keep_samples</span> <span class="k">else</span> <span class="kc">None</span><span class="p">)</span> <span class="k">return</span> <span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">tr_prevalence</span><span class="p">,</span> <span class="n">tr_distribution</span><span class="p">,</span> <span class="n">sample</span> <span class="k">if</span> <span class="n">keep_samples</span> <span class="k">else</span> <span class="kc">None</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_delayed_quantify</span><span class="p">(</span><span class="n">args</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">_delayed_quantify</span><span class="p">(</span><span class="n">args</span><span class="p">):</span>
<span class="n">quantifier</span><span class="p">,</span> <span class="n">instances</span> <span class="o">=</span> <span class="n">args</span> <span class="n">quantifier</span><span class="p">,</span> <span class="n">instances</span> <span class="o">=</span> <span class="n">args</span>
<span class="k">return</span> <span class="n">quantifier</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span> <span class="k">return</span> <span class="n">quantifier</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_draw_simplex</span><span class="p">(</span><span class="n">ndim</span><span class="p">,</span> <span class="n">min_val</span><span class="p">,</span> <span class="n">max_trials</span><span class="o">=</span><span class="mi">100</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">_draw_simplex</span><span class="p">(</span><span class="n">ndim</span><span class="p">,</span> <span class="n">min_val</span><span class="p">,</span> <span class="n">max_trials</span><span class="o">=</span><span class="mi">100</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns a uniform sampling from the ndim-dimensional simplex but guarantees that all dimensions</span> <span class="sd"> Returns a uniform sampling from the ndim-dimensional simplex but guarantees that all dimensions</span>
<span class="sd"> are &gt;= min_class_prev (for min_val&gt;0, this makes the sampling not truly uniform)</span> <span class="sd"> are &gt;= min_class_prev (for min_val&gt;0, this makes the sampling not truly uniform)</span>
@ -571,7 +581,7 @@
<span class="sa">f</span><span class="s1">&#39;&gt;= </span><span class="si">{</span><span class="n">min_val</span><span class="si">}</span><span class="s1"> is unlikely (it failed after </span><span class="si">{</span><span class="n">max_trials</span><span class="si">}</span><span class="s1"> trials)&#39;</span><span class="p">)</span> <span class="sa">f</span><span class="s1">&#39;&gt;= </span><span class="si">{</span><span class="n">min_val</span><span class="si">}</span><span class="s1"> is unlikely (it failed after </span><span class="si">{</span><span class="n">max_trials</span><span class="si">}</span><span class="s1"> trials)&#39;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_instantiate_ensemble</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">base_quantifier_class</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_model_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">_instantiate_ensemble</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">base_quantifier_class</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_model_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="k">if</span> <span class="n">optim</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="k">if</span> <span class="n">optim</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">base_quantifier</span> <span class="o">=</span> <span class="n">base_quantifier_class</span><span class="p">(</span><span class="n">classifier</span><span class="p">)</span> <span class="n">base_quantifier</span> <span class="o">=</span> <span class="n">base_quantifier_class</span><span class="p">(</span><span class="n">classifier</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">optim</span> <span class="ow">in</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">CLASSIFICATION_ERROR</span><span class="p">:</span> <span class="k">elif</span> <span class="n">optim</span> <span class="ow">in</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">CLASSIFICATION_ERROR</span><span class="p">:</span>
@ -590,7 +600,7 @@
<span class="k">return</span> <span class="n">Ensemble</span><span class="p">(</span><span class="n">base_quantifier</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="k">return</span> <span class="n">Ensemble</span><span class="p">(</span><span class="n">base_quantifier</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_check_error</span><span class="p">(</span><span class="n">error</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">_check_error</span><span class="p">(</span><span class="n">error</span><span class="p">):</span>
<span class="k">if</span> <span class="n">error</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="k">if</span> <span class="n">error</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span> <span class="kc">None</span> <span class="k">return</span> <span class="kc">None</span>
<span class="k">if</span> <span class="n">error</span> <span class="ow">in</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">QUANTIFICATION_ERROR</span> <span class="ow">or</span> <span class="n">error</span> <span class="ow">in</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">CLASSIFICATION_ERROR</span><span class="p">:</span> <span class="k">if</span> <span class="n">error</span> <span class="ow">in</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">QUANTIFICATION_ERROR</span> <span class="ow">or</span> <span class="n">error</span> <span class="ow">in</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">CLASSIFICATION_ERROR</span><span class="p">:</span>
@ -602,7 +612,9 @@
<span class="sa">f</span><span class="s1">&#39;the name of an error function in </span><span class="si">{</span><span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">ERROR_NAMES</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span> <span class="sa">f</span><span class="s1">&#39;the name of an error function in </span><span class="si">{</span><span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">ERROR_NAMES</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
<div class="viewcode-block" id="ensembleFactory"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.ensembleFactory">[docs]</a><span class="k">def</span> <span class="nf">ensembleFactory</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">base_quantifier_class</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_model_sel</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <div class="viewcode-block" id="ensembleFactory">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.ensembleFactory">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">ensembleFactory</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">base_quantifier_class</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_model_sel</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
<span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Ensemble factory. Provides a unified interface for instantiating ensembles that can be optimized (via model</span> <span class="sd"> Ensemble factory. Provides a unified interface for instantiating ensembles that can be optimized (via model</span>
@ -653,7 +665,10 @@
<span class="k">return</span> <span class="n">_instantiate_ensemble</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">base_quantifier_class</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">error</span><span class="p">,</span> <span class="n">param_model_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div> <span class="k">return</span> <span class="n">_instantiate_ensemble</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">base_quantifier_class</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">error</span><span class="p">,</span> <span class="n">param_model_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
<div class="viewcode-block" id="ECC"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.ECC">[docs]</a><span class="k">def</span> <span class="nf">ECC</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<div class="viewcode-block" id="ECC">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.ECC">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">ECC</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Implements an ensemble of :class:`quapy.method.aggregative.CC` quantifiers, as used by</span> <span class="sd"> Implements an ensemble of :class:`quapy.method.aggregative.CC` quantifiers, as used by</span>
<span class="sd"> `Pérez-Gállego et al., 2019 &lt;https://www.sciencedirect.com/science/article/pii/S1566253517303652&gt;`_.</span> <span class="sd"> `Pérez-Gállego et al., 2019 &lt;https://www.sciencedirect.com/science/article/pii/S1566253517303652&gt;`_.</span>
@ -676,7 +691,10 @@
<span class="k">return</span> <span class="n">ensembleFactory</span><span class="p">(</span><span class="n">classifier</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></div> <span class="k">return</span> <span class="n">ensembleFactory</span><span class="p">(</span><span class="n">classifier</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></div>
<div class="viewcode-block" id="EACC"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.EACC">[docs]</a><span class="k">def</span> <span class="nf">EACC</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<div class="viewcode-block" id="EACC">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.EACC">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">EACC</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Implements an ensemble of :class:`quapy.method.aggregative.ACC` quantifiers, as used by</span> <span class="sd"> Implements an ensemble of :class:`quapy.method.aggregative.ACC` quantifiers, as used by</span>
<span class="sd"> `Pérez-Gállego et al., 2019 &lt;https://www.sciencedirect.com/science/article/pii/S1566253517303652&gt;`_.</span> <span class="sd"> `Pérez-Gállego et al., 2019 &lt;https://www.sciencedirect.com/science/article/pii/S1566253517303652&gt;`_.</span>
@ -699,7 +717,10 @@
<span class="k">return</span> <span class="n">ensembleFactory</span><span class="p">(</span><span class="n">classifier</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></div> <span class="k">return</span> <span class="n">ensembleFactory</span><span class="p">(</span><span class="n">classifier</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></div>
<div class="viewcode-block" id="EPACC"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.EPACC">[docs]</a><span class="k">def</span> <span class="nf">EPACC</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<div class="viewcode-block" id="EPACC">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.EPACC">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">EPACC</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Implements an ensemble of :class:`quapy.method.aggregative.PACC` quantifiers.</span> <span class="sd"> Implements an ensemble of :class:`quapy.method.aggregative.PACC` quantifiers.</span>
@ -721,7 +742,10 @@
<span class="k">return</span> <span class="n">ensembleFactory</span><span class="p">(</span><span class="n">classifier</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></div> <span class="k">return</span> <span class="n">ensembleFactory</span><span class="p">(</span><span class="n">classifier</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></div>
<div class="viewcode-block" id="EHDy"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.EHDy">[docs]</a><span class="k">def</span> <span class="nf">EHDy</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<div class="viewcode-block" id="EHDy">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.EHDy">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">EHDy</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Implements an ensemble of :class:`quapy.method.aggregative.HDy` quantifiers, as used by</span> <span class="sd"> Implements an ensemble of :class:`quapy.method.aggregative.HDy` quantifiers, as used by</span>
<span class="sd"> `Pérez-Gállego et al., 2019 &lt;https://www.sciencedirect.com/science/article/pii/S1566253517303652&gt;`_.</span> <span class="sd"> `Pérez-Gállego et al., 2019 &lt;https://www.sciencedirect.com/science/article/pii/S1566253517303652&gt;`_.</span>
@ -744,7 +768,10 @@
<span class="k">return</span> <span class="n">ensembleFactory</span><span class="p">(</span><span class="n">classifier</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></div> <span class="k">return</span> <span class="n">ensembleFactory</span><span class="p">(</span><span class="n">classifier</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></div>
<div class="viewcode-block" id="EEMQ"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.EEMQ">[docs]</a><span class="k">def</span> <span class="nf">EEMQ</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<div class="viewcode-block" id="EEMQ">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.EEMQ">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">EEMQ</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Implements an ensemble of :class:`quapy.method.aggregative.EMQ` quantifiers.</span> <span class="sd"> Implements an ensemble of :class:`quapy.method.aggregative.EMQ` quantifiers.</span>
@ -764,6 +791,141 @@
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">ensembleFactory</span><span class="p">(</span><span class="n">classifier</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></div> <span class="k">return</span> <span class="n">ensembleFactory</span><span class="p">(</span><span class="n">classifier</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></div>
<div class="viewcode-block" id="merge">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.merge">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">merge</span><span class="p">(</span><span class="n">prev_predictions</span><span class="p">,</span> <span class="n">merge_fun</span><span class="p">):</span>
<span class="n">prev_predictions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prev_predictions</span><span class="p">)</span>
<span class="k">if</span> <span class="n">merge_fun</span> <span class="o">==</span> <span class="s1">&#39;median&#39;</span><span class="p">:</span>
<span class="n">prevalences</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">prev_predictions</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">prevalences</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">normalize_prevalence</span><span class="p">(</span><span class="n">prevalences</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;l1&#39;</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">merge_fun</span> <span class="o">==</span> <span class="s1">&#39;mean&#39;</span><span class="p">:</span>
<span class="n">prevalences</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">prev_predictions</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;merge function </span><span class="si">{</span><span class="n">merge_fun</span><span class="si">}</span><span class="s1"> not implemented!&#39;</span><span class="p">)</span>
<span class="k">return</span> <span class="n">prevalences</span></div>
<div class="viewcode-block" id="SCMQ">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.SCMQ">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">SCMQ</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">):</span>
<span class="n">MERGE_FUNCTIONS</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;median&#39;</span><span class="p">,</span> <span class="s1">&#39;mean&#39;</span><span class="p">]</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">quantifiers</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">AggregativeSoftQuantifier</span><span class="p">],</span> <span class="n">merge_fun</span><span class="o">=</span><span class="s1">&#39;median&#39;</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
<span class="bp">self</span><span class="o">.</span><span class="n">quantifiers</span> <span class="o">=</span> <span class="p">[</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">q</span><span class="p">)</span> <span class="k">for</span> <span class="n">q</span> <span class="ow">in</span> <span class="n">quantifiers</span><span class="p">]</span>
<span class="k">assert</span> <span class="n">merge_fun</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">MERGE_FUNCTIONS</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;unknown </span><span class="si">{</span><span class="n">merge_fun</span><span class="si">=}</span><span class="s1">, valid ones are </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">MERGE_FUNCTIONS</span><span class="si">}</span><span class="s1">&#39;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">merge_fun</span> <span class="o">=</span> <span class="n">merge_fun</span>
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
<div class="viewcode-block" id="SCMQ.aggregation_fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.SCMQ.aggregation_fit">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
<span class="k">for</span> <span class="n">quantifier</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">quantifiers</span><span class="p">:</span>
<span class="n">quantifier</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span>
<span class="n">quantifier</span><span class="o">.</span><span class="n">aggregation_fit</span><span class="p">(</span><span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="SCMQ.aggregate">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.SCMQ.aggregate">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
<span class="n">prev_predictions</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">quantifier_i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">quantifiers</span><span class="p">:</span>
<span class="n">prevalence_i</span> <span class="o">=</span> <span class="n">quantifier_i</span><span class="o">.</span><span class="n">aggregate</span><span class="p">(</span><span class="n">classif_predictions</span><span class="p">)</span>
<span class="n">prev_predictions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prevalence_i</span><span class="p">)</span>
<span class="k">return</span> <span class="n">merge</span><span class="p">(</span><span class="n">prev_predictions</span><span class="p">,</span> <span class="n">merge_fun</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">merge_fun</span><span class="p">)</span></div>
</div>
<div class="viewcode-block" id="MCSQ">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MCSQ">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">MCSQ</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifiers</span><span class="p">,</span> <span class="n">quantifier</span><span class="p">:</span> <span class="n">AggregativeSoftQuantifier</span><span class="p">,</span> <span class="n">merge_fun</span><span class="o">=</span><span class="s1">&#39;median&#39;</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">merge_fun</span> <span class="o">=</span> <span class="n">merge_fun</span>
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
<span class="bp">self</span><span class="o">.</span><span class="n">mcsqs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">classifier</span> <span class="ow">in</span> <span class="n">classifiers</span><span class="p">:</span>
<span class="n">quantifier</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">quantifier</span><span class="p">)</span>
<span class="n">quantifier</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
<span class="bp">self</span><span class="o">.</span><span class="n">mcsqs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">quantifier</span><span class="p">)</span>
<div class="viewcode-block" id="MCSQ.fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MCSQ.fit">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
<span class="k">for</span> <span class="n">q</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">mcsqs</span><span class="p">:</span>
<span class="n">q</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">val_split</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="MCSQ.quantify">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MCSQ.quantify">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
<span class="n">prev_predictions</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">q</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">mcsqs</span><span class="p">:</span>
<span class="n">prevalence_i</span> <span class="o">=</span> <span class="n">q</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
<span class="n">prev_predictions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prevalence_i</span><span class="p">)</span>
<span class="k">return</span> <span class="n">merge</span><span class="p">(</span><span class="n">prev_predictions</span><span class="p">,</span> <span class="n">merge_fun</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">merge_fun</span><span class="p">)</span></div>
</div>
<div class="viewcode-block" id="MCMQ">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MCMQ">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">MCMQ</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifiers</span><span class="p">,</span> <span class="n">quantifiers</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">AggregativeSoftQuantifier</span><span class="p">],</span> <span class="n">merge_fun</span><span class="o">=</span><span class="s1">&#39;median&#39;</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">merge_fun</span> <span class="o">=</span> <span class="n">merge_fun</span>
<span class="bp">self</span><span class="o">.</span><span class="n">scmqs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">classifier</span> <span class="ow">in</span> <span class="n">classifiers</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">scmqs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">SCMQ</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">quantifiers</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="n">val_split</span><span class="p">))</span>
<div class="viewcode-block" id="MCMQ.fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MCMQ.fit">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
<span class="k">for</span> <span class="n">q</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">scmqs</span><span class="p">:</span>
<span class="n">q</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="MCMQ.quantify">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MCMQ.quantify">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
<span class="n">prev_predictions</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">q</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">scmqs</span><span class="p">:</span>
<span class="n">prevalence_i</span> <span class="o">=</span> <span class="n">q</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
<span class="n">prev_predictions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prevalence_i</span><span class="p">)</span>
<span class="k">return</span> <span class="n">merge</span><span class="p">(</span><span class="n">prev_predictions</span><span class="p">,</span> <span class="n">merge_fun</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">merge_fun</span><span class="p">)</span></div>
</div>
</pre></div> </pre></div>
</div> </div>

298
docs/build/html/_modules/quapy/method/non_aggregative.html vendored
View File

@ -1,23 +1,20 @@
<!DOCTYPE html> <!DOCTYPE html>
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@ -43,7 +40,13 @@
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@ -71,16 +74,25 @@
<div itemprop="articleBody"> <div itemprop="articleBody">
<h1>Source code for quapy.method.non_aggregative</h1><div class="highlight"><pre> <h1>Source code for quapy.method.non_aggregative</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Callable</span> <span></span><span class="kn">from</span><span class="w"> </span><span class="nn">itertools</span><span class="w"> </span><span class="kn">import</span> <span class="n">product</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span class="kn">from</span><span class="w"> </span><span class="nn">tqdm</span><span class="w"> </span><span class="kn">import</span> <span class="n">tqdm</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Counter</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.feature_extraction.text</span><span class="w"> </span><span class="kn">import</span> <span class="n">CountVectorizer</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.utils</span><span class="w"> </span><span class="kn">import</span> <span class="n">resample</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <span class="n">normalize</span>
<span class="kn">from</span> <span class="nn">quapy.functional</span> <span class="kn">import</span> <span class="n">get_divergence</span> <span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.confidence</span><span class="w"> </span><span class="kn">import</span> <span class="n">WithConfidenceABC</span><span class="p">,</span> <span class="n">ConfidenceRegionABC</span>
<span class="kn">from</span> <span class="nn">quapy.data</span> <span class="kn">import</span> <span class="n">LabelledCollection</span> <span class="kn">from</span><span class="w"> </span><span class="nn">quapy.functional</span><span class="w"> </span><span class="kn">import</span> <span class="n">get_divergence</span>
<span class="kn">from</span> <span class="nn">quapy.method.base</span> <span class="kn">import</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">BinaryQuantifier</span> <span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">BinaryQuantifier</span>
<span class="kn">import</span> <span class="nn">quapy.functional</span> <span class="k">as</span> <span class="nn">F</span> <span class="kn">import</span><span class="w"> </span><span class="nn">quapy.functional</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">F</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">scipy.optimize</span><span class="w"> </span><span class="kn">import</span> <span class="n">lsq_linear</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">scipy</span><span class="w"> </span><span class="kn">import</span> <span class="n">sparse</span>
<div class="viewcode-block" id="MaximumLikelihoodPrevalenceEstimation"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation">[docs]</a><span class="k">class</span> <span class="nc">MaximumLikelihoodPrevalenceEstimation</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span> <div class="viewcode-block" id="MaximumLikelihoodPrevalenceEstimation">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">MaximumLikelihoodPrevalenceEstimation</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> The `Maximum Likelihood Prevalence Estimation` (MLPE) method is a lazy method that assumes there is no prior</span> <span class="sd"> The `Maximum Likelihood Prevalence Estimation` (MLPE) method is a lazy method that assumes there is no prior</span>
<span class="sd"> probability shift between training and test instances (put it other way, that the i.i.d. assumpion holds).</span> <span class="sd"> probability shift between training and test instances (put it other way, that the i.i.d. assumpion holds).</span>
@ -89,30 +101,41 @@
<span class="sd"> any quantification method should beat.</span> <span class="sd"> any quantification method should beat.</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_classes_</span> <span class="o">=</span> <span class="kc">None</span> <span class="bp">self</span><span class="o">.</span><span class="n">_classes_</span> <span class="o">=</span> <span class="kc">None</span>
<div class="viewcode-block" id="MaximumLikelihoodPrevalenceEstimation.fit"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.fit">[docs]</a> <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span> <div class="viewcode-block" id="MaximumLikelihoodPrevalenceEstimation.fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.fit">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Computes the training prevalence and stores it.</span> <span class="sd"> Computes the training prevalence and stores it.</span>
<span class="sd"> :param data: the training sample</span> <span class="sd"> :param X: array-like of shape `(n_samples, n_features)`, the training instances</span>
<span class="sd"> :param y: array-like of shape `(n_samples,)`, the labels</span>
<span class="sd"> :return: self</span> <span class="sd"> :return: self</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">estimated_prevalence</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span> <span class="bp">self</span><span class="o">.</span><span class="n">_classes_</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">classes_from_labels</span><span class="p">(</span><span class="n">labels</span><span class="o">=</span><span class="n">y</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">estimated_prevalence</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">prevalence_from_labels</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_classes_</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span></div> <span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="MaximumLikelihoodPrevalenceEstimation.quantify"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.quantify">[docs]</a> <span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
<div class="viewcode-block" id="MaximumLikelihoodPrevalenceEstimation.predict">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.predict">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Ignores the input instances and returns, as the class prevalence estimantes, the training prevalence.</span> <span class="sd"> Ignores the input instances and returns, as the class prevalence estimantes, the training prevalence.</span>
<span class="sd"> :param instances: array-like (ignored)</span> <span class="sd"> :param X: array-like (ignored)</span>
<span class="sd"> :return: the class prevalence seen during training</span> <span class="sd"> :return: the class prevalence seen during training</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">estimated_prevalence</span></div></div> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">estimated_prevalence</span></div>
</div>
<div class="viewcode-block" id="DMx"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.DMx">[docs]</a><span class="k">class</span> <span class="nc">DMx</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
<div class="viewcode-block" id="DMx">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.DMx">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">DMx</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Generic Distribution Matching quantifier for binary or multiclass quantification based on the space of covariates.</span> <span class="sd"> Generic Distribution Matching quantifier for binary or multiclass quantification based on the space of covariates.</span>
<span class="sd"> This implementation takes the number of bins, the divergence, and the possibility to work on CDF as hyperparameters.</span> <span class="sd"> This implementation takes the number of bins, the divergence, and the possibility to work on CDF as hyperparameters.</span>
@ -125,15 +148,17 @@
<span class="sd"> :param n_jobs: number of parallel workers (default None)</span> <span class="sd"> :param n_jobs: number of parallel workers (default None)</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">divergence</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">]</span><span class="o">=</span><span class="s1">&#39;HD&#39;</span><span class="p">,</span> <span class="n">cdf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">search</span><span class="o">=</span><span class="s1">&#39;optim_minimize&#39;</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">divergence</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">]</span><span class="o">=</span><span class="s1">&#39;HD&#39;</span><span class="p">,</span> <span class="n">cdf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">search</span><span class="o">=</span><span class="s1">&#39;optim_minimize&#39;</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nbins</span> <span class="o">=</span> <span class="n">nbins</span> <span class="bp">self</span><span class="o">.</span><span class="n">nbins</span> <span class="o">=</span> <span class="n">nbins</span>
<span class="bp">self</span><span class="o">.</span><span class="n">divergence</span> <span class="o">=</span> <span class="n">divergence</span> <span class="bp">self</span><span class="o">.</span><span class="n">divergence</span> <span class="o">=</span> <span class="n">divergence</span>
<span class="bp">self</span><span class="o">.</span><span class="n">cdf</span> <span class="o">=</span> <span class="n">cdf</span> <span class="bp">self</span><span class="o">.</span><span class="n">cdf</span> <span class="o">=</span> <span class="n">cdf</span>
<span class="bp">self</span><span class="o">.</span><span class="n">search</span> <span class="o">=</span> <span class="n">search</span> <span class="bp">self</span><span class="o">.</span><span class="n">search</span> <span class="o">=</span> <span class="n">search</span>
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
<div class="viewcode-block" id="DMx.HDx"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.DMx.HDx">[docs]</a> <span class="nd">@classmethod</span> <div class="viewcode-block" id="DMx.HDx">
<span class="k">def</span> <span class="nf">HDx</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> <a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.DMx.HDx">[docs]</a>
<span class="nd">@classmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">HDx</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> `Hellinger Distance x &lt;https://www.sciencedirect.com/science/article/pii/S0020025512004069&gt;`_ (HDx).</span> <span class="sd"> `Hellinger Distance x &lt;https://www.sciencedirect.com/science/article/pii/S0020025512004069&gt;`_ (HDx).</span>
<span class="sd"> HDx is a method for training binary quantifiers, that models quantification as the problem of</span> <span class="sd"> HDx is a method for training binary quantifiers, that models quantification as the problem of</span>
@ -149,14 +174,15 @@
<span class="sd"> :return: an instance of this class setup to mimick the performance of the HDx as originally proposed by</span> <span class="sd"> :return: an instance of this class setup to mimick the performance of the HDx as originally proposed by</span>
<span class="sd"> González-Castro, Alaiz-Rodríguez, Alegre (2013)</span> <span class="sd"> González-Castro, Alaiz-Rodríguez, Alegre (2013)</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="kn">from</span> <span class="nn">quapy.method.meta</span> <span class="kn">import</span> <span class="n">MedianEstimator</span> <span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.meta</span><span class="w"> </span><span class="kn">import</span> <span class="n">MedianEstimator</span>
<span class="n">dmx</span> <span class="o">=</span> <span class="n">DMx</span><span class="p">(</span><span class="n">divergence</span><span class="o">=</span><span class="s1">&#39;HD&#39;</span><span class="p">,</span> <span class="n">cdf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">search</span><span class="o">=</span><span class="s1">&#39;linear_search&#39;</span><span class="p">)</span> <span class="n">dmx</span> <span class="o">=</span> <span class="n">DMx</span><span class="p">(</span><span class="n">divergence</span><span class="o">=</span><span class="s1">&#39;HD&#39;</span><span class="p">,</span> <span class="n">cdf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">search</span><span class="o">=</span><span class="s1">&#39;linear_search&#39;</span><span class="p">)</span>
<span class="n">nbins</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;nbins&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">110</span><span class="p">,</span> <span class="mi">11</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)}</span> <span class="n">nbins</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;nbins&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">110</span><span class="p">,</span> <span class="mi">11</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)}</span>
<span class="n">hdx</span> <span class="o">=</span> <span class="n">MedianEstimator</span><span class="p">(</span><span class="n">base_quantifier</span><span class="o">=</span><span class="n">dmx</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">nbins</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">)</span> <span class="n">hdx</span> <span class="o">=</span> <span class="n">MedianEstimator</span><span class="p">(</span><span class="n">base_quantifier</span><span class="o">=</span><span class="n">dmx</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">nbins</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">)</span>
<span class="k">return</span> <span class="n">hdx</span></div> <span class="k">return</span> <span class="n">hdx</span></div>
<span class="k">def</span> <span class="nf">__get_distributions</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">__get_distributions</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="n">histograms</span> <span class="o">=</span> <span class="p">[]</span> <span class="n">histograms</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">feat_idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nfeats</span><span class="p">):</span> <span class="k">for</span> <span class="n">feat_idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nfeats</span><span class="p">):</span>
@ -172,7 +198,9 @@
<span class="k">return</span> <span class="n">distributions</span> <span class="k">return</span> <span class="n">distributions</span>
<div class="viewcode-block" id="DMx.fit"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.DMx.fit">[docs]</a> <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span> <div class="viewcode-block" id="DMx.fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.DMx.fit">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Generates the validation distributions out of the training data (covariates).</span> <span class="sd"> Generates the validation distributions out of the training data (covariates).</span>
<span class="sd"> The validation distributions have shape `(n, nfeats, nbins)`, with `n` the number of classes, `nfeats`</span> <span class="sd"> The validation distributions have shape `(n, nfeats, nbins)`, with `n` the number of classes, `nfeats`</span>
@ -181,46 +209,222 @@
<span class="sd"> training data labelled with class `i`; while `dij = di[j]` is the discrete distribution for feature j in</span> <span class="sd"> training data labelled with class `i`; while `dij = di[j]` is the discrete distribution for feature j in</span>
<span class="sd"> training data labelled with class `i`, and `dij[k]` is the fraction of instances with a value in the `k`-th bin.</span> <span class="sd"> training data labelled with class `i`, and `dij[k]` is the fraction of instances with a value in the `k`-th bin.</span>
<span class="sd"> :param data: the training set</span> <span class="sd"> :param X: array-like of shape `(n_samples, n_features)`, the training instances</span>
<span class="sd"> :param y: array-like of shape `(n_samples,)`, the labels</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">Xy</span>
<span class="bp">self</span><span class="o">.</span><span class="n">nfeats</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="bp">self</span><span class="o">.</span><span class="n">nfeats</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="bp">self</span><span class="o">.</span><span class="n">feat_ranges</span> <span class="o">=</span> <span class="n">_get_features_range</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">feat_ranges</span> <span class="o">=</span> <span class="n">_get_features_range</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="n">n_classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">validation_distribution</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span> <span class="bp">self</span><span class="o">.</span><span class="n">validation_distribution</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span>
<span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">__get_distributions</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">y</span><span class="o">==</span><span class="n">cat</span><span class="p">])</span> <span class="k">for</span> <span class="n">cat</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">n_classes</span><span class="p">)]</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">__get_distributions</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">y</span><span class="o">==</span><span class="n">cat</span><span class="p">])</span> <span class="k">for</span> <span class="n">cat</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_classes</span><span class="p">)]</span>
<span class="p">)</span> <span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span></div> <span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="DMx.quantify"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.DMx.quantify">[docs]</a> <span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
<div class="viewcode-block" id="DMx.predict">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.DMx.predict">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Searches for the mixture model parameter (the sought prevalence values) that yields a validation distribution</span> <span class="sd"> Searches for the mixture model parameter (the sought prevalence values) that yields a validation distribution</span>
<span class="sd"> (the mixture) that best matches the test distribution, in terms of the divergence measure of choice.</span> <span class="sd"> (the mixture) that best matches the test distribution, in terms of the divergence measure of choice.</span>
<span class="sd"> The matching is computed as the average dissimilarity (in terms of the dissimilarity measure of choice)</span> <span class="sd"> The matching is computed as the average dissimilarity (in terms of the dissimilarity measure of choice)</span>
<span class="sd"> between all feature-specific discrete distributions.</span> <span class="sd"> between all feature-specific discrete distributions.</span>
<span class="sd"> :param instances: instances in the sample</span> <span class="sd"> :param X: instances in the sample</span>
<span class="sd"> :return: a vector of class prevalence estimates</span> <span class="sd"> :return: a vector of class prevalence estimates</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">assert</span> <span class="n">instances</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">nfeats</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;wrong shape; expected </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">nfeats</span><span class="si">}</span><span class="s1">, found </span><span class="si">{</span><span class="n">instances</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s1">&#39;</span> <span class="k">assert</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">nfeats</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;wrong shape; expected </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">nfeats</span><span class="si">}</span><span class="s1">, found </span><span class="si">{</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s1">&#39;</span>
<span class="n">test_distribution</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__get_distributions</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span> <span class="n">test_distribution</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__get_distributions</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="n">divergence</span> <span class="o">=</span> <span class="n">get_divergence</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">divergence</span><span class="p">)</span> <span class="n">divergence</span> <span class="o">=</span> <span class="n">get_divergence</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">divergence</span><span class="p">)</span>
<span class="n">n_classes</span><span class="p">,</span> <span class="n">n_feats</span><span class="p">,</span> <span class="n">nbins</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">validation_distribution</span><span class="o">.</span><span class="n">shape</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">n_feats</span><span class="p">,</span> <span class="n">nbins</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">validation_distribution</span><span class="o">.</span><span class="n">shape</span>
<span class="k">def</span> <span class="nf">loss</span><span class="p">(</span><span class="n">prev</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">loss</span><span class="p">(</span><span class="n">prev</span><span class="p">):</span>
<span class="n">prev</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">prev</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="n">prev</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">prev</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">mixture_distribution</span> <span class="o">=</span> <span class="p">(</span><span class="n">prev</span> <span class="o">@</span> <span class="bp">self</span><span class="o">.</span><span class="n">validation_distribution</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">n_classes</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">n_feats</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="n">mixture_distribution</span> <span class="o">=</span> <span class="p">(</span><span class="n">prev</span> <span class="o">@</span> <span class="bp">self</span><span class="o">.</span><span class="n">validation_distribution</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">n_classes</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">n_feats</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="n">divs</span> <span class="o">=</span> <span class="p">[</span><span class="n">divergence</span><span class="p">(</span><span class="n">test_distribution</span><span class="p">[</span><span class="n">feat</span><span class="p">],</span> <span class="n">mixture_distribution</span><span class="p">[</span><span class="n">feat</span><span class="p">])</span> <span class="k">for</span> <span class="n">feat</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_feats</span><span class="p">)]</span> <span class="n">divs</span> <span class="o">=</span> <span class="p">[</span><span class="n">divergence</span><span class="p">(</span><span class="n">test_distribution</span><span class="p">[</span><span class="n">feat</span><span class="p">],</span> <span class="n">mixture_distribution</span><span class="p">[</span><span class="n">feat</span><span class="p">])</span> <span class="k">for</span> <span class="n">feat</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_feats</span><span class="p">)]</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">divs</span><span class="p">)</span> <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">divs</span><span class="p">)</span>
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">argmin_prevalence</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">search</span><span class="p">)</span></div></div> <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">argmin_prevalence</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">search</span><span class="p">)</span></div>
</div>
<span class="k">def</span> <span class="nf">_get_features_range</span><span class="p">(</span><span class="n">X</span><span class="p">):</span>
<div class="viewcode-block" id="ReadMe">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.ReadMe">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">ReadMe</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">WithConfidenceABC</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> ReadMe is a non-aggregative quantification system proposed by</span>
<span class="sd"> `Daniel Hopkins and Gary King, 2007. A method of automated nonparametric content analysis for</span>
<span class="sd"> social science. American Journal of Political Science, 54(1):229–247.</span>
<span class="sd"> &lt;https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1540-5907.2009.00428.x&gt;`_.</span>
<span class="sd"> The idea is to estimate `Q(Y=i)` directly from:</span>
<span class="sd"> :math:`Q(X)=\\sum_{i=1} Q(X|Y=i) Q(Y=i)`</span>
<span class="sd"> via least-squares regression, i.e., without incurring the cost of computing posterior probabilities.</span>
<span class="sd"> However, this poses a very difficult representation in which the vector `Q(X)` and the matrix `Q(X|Y=i)`</span>
<span class="sd"> can be of very high dimensions. In order to render the problem tracktable, ReadMe performs bagging in</span>
<span class="sd"> the feature space. ReadMe also combines bagging with bootstrap in order to derive confidence intervals</span>
<span class="sd"> around point estimations.</span>
<span class="sd"> We use the same default parameters as in the official</span>
<span class="sd"> `R implementation &lt;https://github.com/iqss-research/ReadMeV1/blob/master/R/prototype.R&gt;`_.</span>
<span class="sd"> :param prob_model: str (&#39;naive&#39;, or &#39;full&#39;), selects the modality in which the probabilities `Q(X)` and</span>
<span class="sd"> `Q(X|Y)` are to be modelled. Options include &quot;full&quot;, which corresponds to the original formulation of</span>
<span class="sd"> ReadMe, in which X is constrained to be a binary matrix (e.g., of term presence/absence) and in which</span>
<span class="sd"> `Q(X)` and `Q(X|Y)` are modelled, respectively, as matrices of `(2^K, 1)` and `(2^K, n)` values, where</span>
<span class="sd"> `K` is the number of columns in the data matrix (i.e., `bagging_range`), and `n` is the number of classes.</span>
<span class="sd"> Of course, this approach is computationally prohibited for large `K`, so the computation is restricted to data</span>
<span class="sd"> matrices with `K&lt;=25` (although we recommend even smaller values of `K`). A much faster model is &quot;naive&quot;, which</span>
<span class="sd"> considers the `Q(X)` and `Q(X|Y)` be multinomial distributions under the `bag-of-words` perspective. In this</span>
<span class="sd"> case, `bagging_range` can be set to much larger values. Default is &quot;full&quot; (i.e., original ReadMe behavior).</span>
<span class="sd"> :param bootstrap_trials: int, number of bootstrap trials (default 300)</span>
<span class="sd"> :param bagging_trials: int, number of bagging trials (default 300)</span>
<span class="sd"> :param bagging_range: int, number of features to keep for each bagging trial (default 15)</span>
<span class="sd"> :param confidence_level: float, a value in (0,1) reflecting the desired confidence level (default 0.95)</span>
<span class="sd"> :param region: str in &#39;intervals&#39;, &#39;ellipse&#39;, &#39;ellipse-clr&#39;; indicates the preferred method for</span>
<span class="sd"> defining the confidence region (see :class:`WithConfidenceABC`)</span>
<span class="sd"> :param random_state: int or None, allows replicability (default None)</span>
<span class="sd"> :param verbose: bool, whether to display information during the process (default False)</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">MAX_FEATURES_FOR_EMPIRICAL_ESTIMATION</span> <span class="o">=</span> <span class="mi">25</span>
<span class="n">PROBABILISTIC_MODELS</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;naive&quot;</span><span class="p">,</span> <span class="s2">&quot;full&quot;</span><span class="p">]</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
<span class="n">prob_model</span><span class="o">=</span><span class="s2">&quot;full&quot;</span><span class="p">,</span>
<span class="n">bootstrap_trials</span><span class="o">=</span><span class="mi">300</span><span class="p">,</span>
<span class="n">bagging_trials</span><span class="o">=</span><span class="mi">300</span><span class="p">,</span>
<span class="n">bagging_range</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span>
<span class="n">confidence_level</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span>
<span class="n">region</span><span class="o">=</span><span class="s1">&#39;intervals&#39;</span><span class="p">,</span>
<span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="k">assert</span> <span class="n">prob_model</span> <span class="ow">in</span> <span class="n">ReadMe</span><span class="o">.</span><span class="n">PROBABILISTIC_MODELS</span><span class="p">,</span> \
<span class="sa">f</span><span class="s1">&#39;unknown </span><span class="si">{</span><span class="n">prob_model</span><span class="si">=}</span><span class="s1">, valid ones are </span><span class="si">{</span><span class="n">ReadMe</span><span class="o">.</span><span class="n">PROBABILISTIC_MODELS</span><span class="si">=}</span><span class="s1">&#39;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">prob_model</span> <span class="o">=</span> <span class="n">prob_model</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bootstrap_trials</span> <span class="o">=</span> <span class="n">bootstrap_trials</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bagging_trials</span> <span class="o">=</span> <span class="n">bagging_trials</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bagging_range</span> <span class="o">=</span> <span class="n">bagging_range</span>
<span class="bp">self</span><span class="o">.</span><span class="n">confidence_level</span> <span class="o">=</span> <span class="n">confidence_level</span>
<span class="bp">self</span><span class="o">.</span><span class="n">region</span> <span class="o">=</span> <span class="n">region</span>
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span>
<div class="viewcode-block" id="ReadMe.fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.ReadMe.fit">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_check_matrix</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="n">Xsize</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="c1"># Bootstrap loop</span>
<span class="bp">self</span><span class="o">.</span><span class="n">Xboots</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">yboots</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bootstrap_trials</span><span class="p">):</span>
<span class="n">idx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rng</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">Xsize</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">Xsize</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">Xboots</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span>
<span class="bp">self</span><span class="o">.</span><span class="n">yboots</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">y</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span>
<span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="ReadMe.predict_conf">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.ReadMe.predict_conf">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">predict_conf</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">confidence_level</span><span class="o">=</span><span class="mf">0.95</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">ConfidenceRegionABC</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_check_matrix</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="n">n_features</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">boots_prevalences</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">Xboots</span><span class="p">,</span> <span class="n">yboots</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span>
<span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Xboots</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">yboots</span><span class="p">),</span>
<span class="n">desc</span><span class="o">=</span><span class="s1">&#39;bootstrap predictions&#39;</span><span class="p">,</span> <span class="n">total</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">bootstrap_trials</span><span class="p">,</span> <span class="n">disable</span><span class="o">=</span><span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span>
<span class="p">):</span>
<span class="n">bagging_estimates</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bagging_trials</span><span class="p">):</span>
<span class="n">feat_idx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rng</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">n_features</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">bagging_range</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">Xboots_bagging</span> <span class="o">=</span> <span class="n">Xboots</span><span class="p">[:,</span> <span class="n">feat_idx</span><span class="p">]</span>
<span class="n">X_boots_bagging</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="n">feat_idx</span><span class="p">]</span>
<span class="n">bagging_prev</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_quantify_iteration</span><span class="p">(</span><span class="n">Xboots_bagging</span><span class="p">,</span> <span class="n">yboots</span><span class="p">,</span> <span class="n">X_boots_bagging</span><span class="p">)</span>
<span class="n">bagging_estimates</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">bagging_prev</span><span class="p">)</span>
<span class="n">boots_prevalences</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">bagging_estimates</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
<span class="n">conf</span> <span class="o">=</span> <span class="n">WithConfidenceABC</span><span class="o">.</span><span class="n">construct_region</span><span class="p">(</span><span class="n">boots_prevalences</span><span class="p">,</span> <span class="n">confidence_level</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">region</span><span class="p">)</span>
<span class="n">prev_estim</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">point_estimate</span><span class="p">()</span>
<span class="k">return</span> <span class="n">prev_estim</span><span class="p">,</span> <span class="n">conf</span></div>
<div class="viewcode-block" id="ReadMe.predict">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.ReadMe.predict">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="n">prev_estim</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">predict_conf</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="k">return</span> <span class="n">prev_estim</span></div>
<span class="k">def</span><span class="w"> </span><span class="nf">_quantify_iteration</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">Xtr</span><span class="p">,</span> <span class="n">ytr</span><span class="p">,</span> <span class="n">Xte</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Single ReadMe estimate.&quot;&quot;&quot;</span>
<span class="n">PX_given_Y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">_compute_P</span><span class="p">(</span><span class="n">Xtr</span><span class="p">[</span><span class="n">ytr</span> <span class="o">==</span> <span class="n">c</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">c</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)])</span>
<span class="n">PX</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_P</span><span class="p">(</span><span class="n">Xte</span><span class="p">)</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">lsq_linear</span><span class="p">(</span><span class="n">A</span><span class="o">=</span><span class="n">PX_given_Y</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="n">PX</span><span class="p">,</span> <span class="n">bounds</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
<span class="n">pY</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">res</span><span class="o">.</span><span class="n">x</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="k">return</span> <span class="n">pY</span> <span class="o">/</span> <span class="n">pY</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_check_matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;the &quot;full&quot; model requires estimating empirical distributions; due to the high computational cost,</span>
<span class="sd"> this function is only made available for binary matrices&quot;&quot;&quot;</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">prob_model</span> <span class="o">==</span> <span class="s1">&#39;full&#39;</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_binary_matrix</span><span class="p">(</span><span class="n">X</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;the empirical distribution can only be computed efficiently on binary matrices&#39;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_is_binary_matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">data</span> <span class="k">if</span> <span class="n">sparse</span><span class="o">.</span><span class="n">issparse</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="k">else</span> <span class="n">X</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">((</span><span class="n">data</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">data</span> <span class="o">==</span> <span class="mi">1</span><span class="p">))</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_compute_P</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">prob_model</span> <span class="o">==</span> <span class="s1">&#39;naive&#39;</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_multinomial_distribution</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">prob_model</span> <span class="o">==</span> <span class="s1">&#39;full&#39;</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_empirical_distribution</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;unknown </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">prob_model</span><span class="si">}</span><span class="s1">; valid ones are </span><span class="si">{</span><span class="n">ReadMe</span><span class="o">.</span><span class="n">PROBABILISTIC_MODELS</span><span class="si">=}</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_empirical_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="k">if</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">MAX_FEATURES_FOR_EMPIRICAL_ESTIMATION</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;the empirical distribution can only be computed efficiently for dimensions &#39;</span>
<span class="sa">f</span><span class="s1">&#39;less or equal than </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">MAX_FEATURES_FOR_EMPIRICAL_ESTIMATION</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="c1"># we convert every binary row (e.g., 0 0 1 0 1) into the equivalent number (e.g., 5)</span>
<span class="n">K</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">binary_powers</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">&lt;&lt;</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">K</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># (2^K, ..., 32, 16, 8, 4, 2, 1)</span>
<span class="n">X_as_binary_numbers</span> <span class="o">=</span> <span class="n">X</span> <span class="o">@</span> <span class="n">binary_powers</span>
<span class="c1"># count occurrences and compute probs</span>
<span class="n">counts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">bincount</span><span class="p">(</span><span class="n">X_as_binary_numbers</span><span class="p">,</span> <span class="n">minlength</span><span class="o">=</span><span class="mi">2</span> <span class="o">**</span> <span class="n">K</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
<span class="n">probs</span> <span class="o">=</span> <span class="n">counts</span> <span class="o">/</span> <span class="n">counts</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="k">return</span> <span class="n">probs</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_multinomial_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="n">PX</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
<span class="n">PX</span> <span class="o">=</span> <span class="n">normalize</span><span class="p">(</span><span class="n">PX</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="s1">&#39;l1&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="k">return</span> <span class="n">PX</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span></div>
<span class="k">def</span><span class="w"> </span><span class="nf">_get_features_range</span><span class="p">(</span><span class="n">X</span><span class="p">):</span>
<span class="n">feat_ranges</span> <span class="o">=</span> <span class="p">[]</span> <span class="n">feat_ranges</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">ncols</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="n">ncols</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="k">for</span> <span class="n">col_idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ncols</span><span class="p">):</span> <span class="k">for</span> <span class="n">col_idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ncols</span><span class="p">):</span>

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<h1>Source code for quapy.model_selection</h1><div class="highlight"><pre> <h1>Source code for quapy.model_selection</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">itertools</span> <span></span><span class="kn">import</span><span class="w"> </span><span class="nn">itertools</span>
<span class="kn">import</span> <span class="nn">signal</span> <span class="kn">import</span><span class="w"> </span><span class="nn">signal</span>
<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span> <span class="kn">from</span><span class="w"> </span><span class="nn">copy</span><span class="w"> </span><span class="kn">import</span> <span class="n">deepcopy</span>
<span class="kn">from</span> <span class="nn">enum</span> <span class="kn">import</span> <span class="n">Enum</span> <span class="kn">from</span><span class="w"> </span><span class="nn">enum</span><span class="w"> </span><span class="kn">import</span> <span class="n">Enum</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Callable</span> <span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Callable</span>
<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">wraps</span> <span class="kn">from</span><span class="w"> </span><span class="nn">functools</span><span class="w"> </span><span class="kn">import</span> <span class="n">wraps</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">clone</span> <span class="kn">from</span><span class="w"> </span><span class="nn">sklearn</span><span class="w"> </span><span class="kn">import</span> <span class="n">clone</span>
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span> <span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
<span class="kn">from</span> <span class="nn">quapy</span> <span class="kn">import</span> <span class="n">evaluation</span> <span class="kn">from</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="kn">import</span> <span class="n">evaluation</span>
<span class="kn">from</span> <span class="nn">quapy.protocol</span> <span class="kn">import</span> <span class="n">AbstractProtocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span> <span class="kn">from</span><span class="w"> </span><span class="nn">quapy.protocol</span><span class="w"> </span><span class="kn">import</span> <span class="n">AbstractProtocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span>
<span class="kn">from</span> <span class="nn">quapy.data.base</span> <span class="kn">import</span> <span class="n">LabelledCollection</span> <span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">LabelledCollection</span>
<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">AggregativeQuantifier</span> <span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.aggregative</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">AggregativeQuantifier</span>
<span class="kn">from</span> <span class="nn">quapy.util</span> <span class="kn">import</span> <span class="n">timeout</span> <span class="kn">from</span><span class="w"> </span><span class="nn">quapy.util</span><span class="w"> </span><span class="kn">import</span> <span class="n">timeout</span>
<span class="kn">from</span> <span class="nn">time</span> <span class="kn">import</span> <span class="n">time</span> <span class="kn">from</span><span class="w"> </span><span class="nn">time</span><span class="w"> </span><span class="kn">import</span> <span class="n">time</span>
<div class="viewcode-block" id="Status"><a class="viewcode-back" href="../../quapy.html#quapy.model_selection.Status">[docs]</a><span class="k">class</span> <span class="nc">Status</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span> <div class="viewcode-block" id="Status">
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.Status">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">Status</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
<span class="n">SUCCESS</span> <span class="o">=</span> <span class="mi">1</span> <span class="n">SUCCESS</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">TIMEOUT</span> <span class="o">=</span> <span class="mi">2</span> <span class="n">TIMEOUT</span> <span class="o">=</span> <span class="mi">2</span>
<span class="n">INVALID</span> <span class="o">=</span> <span class="mi">3</span> <span class="n">INVALID</span> <span class="o">=</span> <span class="mi">3</span>
<span class="n">ERROR</span> <span class="o">=</span> <span class="mi">4</span></div> <span class="n">ERROR</span> <span class="o">=</span> <span class="mi">4</span></div>
<div class="viewcode-block" id="ConfigStatus"><a class="viewcode-back" href="../../quapy.html#quapy.model_selection.ConfigStatus">[docs]</a><span class="k">class</span> <span class="nc">ConfigStatus</span><span class="p">:</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">msg</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">):</span> <div class="viewcode-block" id="ConfigStatus">
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.ConfigStatus">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">ConfigStatus</span><span class="p">:</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">msg</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">params</span> <span class="o">=</span> <span class="n">params</span> <span class="bp">self</span><span class="o">.</span><span class="n">params</span> <span class="o">=</span> <span class="n">params</span>
<span class="bp">self</span><span class="o">.</span><span class="n">status</span> <span class="o">=</span> <span class="n">status</span> <span class="bp">self</span><span class="o">.</span><span class="n">status</span> <span class="o">=</span> <span class="n">status</span>
<span class="bp">self</span><span class="o">.</span><span class="n">msg</span> <span class="o">=</span> <span class="n">msg</span> <span class="bp">self</span><span class="o">.</span><span class="n">msg</span> <span class="o">=</span> <span class="n">msg</span>
<span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="sa">f</span><span class="s1">&#39;:params:</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="si">}</span><span class="s1"> :status:</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="si">}</span><span class="s1"> &#39;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">msg</span> <span class="k">return</span> <span class="sa">f</span><span class="s1">&#39;:params:</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="si">}</span><span class="s1"> :status:</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="si">}</span><span class="s1"> &#39;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">msg</span>
<span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
<div class="viewcode-block" id="ConfigStatus.success"><a class="viewcode-back" href="../../quapy.html#quapy.model_selection.ConfigStatus.success">[docs]</a> <span class="k">def</span> <span class="nf">success</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <div class="viewcode-block" id="ConfigStatus.success">
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.ConfigStatus.success">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">success</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">status</span> <span class="o">==</span> <span class="n">Status</span><span class="o">.</span><span class="n">SUCCESS</span></div> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">status</span> <span class="o">==</span> <span class="n">Status</span><span class="o">.</span><span class="n">SUCCESS</span></div>
<div class="viewcode-block" id="ConfigStatus.failed"><a class="viewcode-back" href="../../quapy.html#quapy.model_selection.ConfigStatus.failed">[docs]</a> <span class="k">def</span> <span class="nf">failed</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">status</span> <span class="o">!=</span> <span class="n">Status</span><span class="o">.</span><span class="n">SUCCESS</span></div></div> <div class="viewcode-block" id="ConfigStatus.failed">
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.ConfigStatus.failed">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">failed</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">status</span> <span class="o">!=</span> <span class="n">Status</span><span class="o">.</span><span class="n">SUCCESS</span></div>
</div>
<div class="viewcode-block" id="GridSearchQ"><a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ">[docs]</a><span class="k">class</span> <span class="nc">GridSearchQ</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
<div class="viewcode-block" id="GridSearchQ">
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">GridSearchQ</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Grid Search optimization targeting a quantification-oriented metric.</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;Grid Search optimization targeting a quantification-oriented metric.</span>
<span class="sd"> Optimizes the hyperparameters of a quantification method, based on an evaluation method and on an evaluation</span> <span class="sd"> Optimizes the hyperparameters of a quantification method, based on an evaluation method and on an evaluation</span>
@ -139,7 +157,7 @@
<span class="sd"> :param verbose: set to True to get information through the stdout</span> <span class="sd"> :param verbose: set to True to get information through the stdout</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
<span class="n">model</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">model</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span>
<span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span>
<span class="n">protocol</span><span class="p">:</span> <span class="n">AbstractProtocol</span><span class="p">,</span> <span class="n">protocol</span><span class="p">:</span> <span class="n">AbstractProtocol</span><span class="p">,</span>
@ -158,14 +176,14 @@
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_njobs</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_njobs</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">raise_errors</span> <span class="o">=</span> <span class="n">raise_errors</span> <span class="bp">self</span><span class="o">.</span><span class="n">raise_errors</span> <span class="o">=</span> <span class="n">raise_errors</span>
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span>
<span class="bp">self</span><span class="o">.</span><span class="n">__check_error</span><span class="p">(</span><span class="n">error</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">__check_error_measure</span><span class="p">(</span><span class="n">error</span><span class="p">)</span>
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">protocol</span><span class="p">,</span> <span class="n">AbstractProtocol</span><span class="p">),</span> <span class="s1">&#39;unknown protocol&#39;</span> <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">protocol</span><span class="p">,</span> <span class="n">AbstractProtocol</span><span class="p">),</span> <span class="s1">&#39;unknown protocol&#39;</span>
<span class="k">def</span> <span class="nf">_sout</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">msg</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">_sout</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">msg</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;[</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1">:</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1">]: </span><span class="si">{</span><span class="n">msg</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;[</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1">:</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1">]: </span><span class="si">{</span><span class="n">msg</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">__check_error</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">error</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">__check_error_measure</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">error</span><span class="p">):</span>
<span class="k">if</span> <span class="n">error</span> <span class="ow">in</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">QUANTIFICATION_ERROR</span><span class="p">:</span> <span class="k">if</span> <span class="n">error</span> <span class="ow">in</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">QUANTIFICATION_ERROR</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">error</span> <span class="o">=</span> <span class="n">error</span> <span class="bp">self</span><span class="o">.</span><span class="n">error</span> <span class="o">=</span> <span class="n">error</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">error</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span> <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">error</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
@ -176,26 +194,27 @@
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;unexpected error type; must either be a callable function or a str representing</span><span class="se">\n</span><span class="s1">&#39;</span> <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;unexpected error type; must either be a callable function or a str representing</span><span class="se">\n</span><span class="s1">&#39;</span>
<span class="sa">f</span><span class="s1">&#39;the name of an error function in </span><span class="si">{</span><span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">QUANTIFICATION_ERROR_NAMES</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span> <span class="sa">f</span><span class="s1">&#39;the name of an error function in </span><span class="si">{</span><span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">QUANTIFICATION_ERROR_NAMES</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_prepare_classifier</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">cls_params</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">_prepare_classifier</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">cls_params</span><span class="p">):</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span> <span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">job</span><span class="p">(</span><span class="n">cls_params</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">job</span><span class="p">(</span><span class="n">cls_params</span><span class="p">):</span>
<span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">cls_params</span><span class="p">)</span> <span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">cls_params</span><span class="p">)</span>
<span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">classifier_fit_predict</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_training</span><span class="p">)</span> <span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">classifier_fit_predict</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_training_X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_training_y</span><span class="p">)</span>
<span class="k">return</span> <span class="n">predictions</span> <span class="k">return</span> <span class="n">predictions</span>
<span class="n">predictions</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_error_handler</span><span class="p">(</span><span class="n">job</span><span class="p">,</span> <span class="n">cls_params</span><span class="p">)</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_error_handler</span><span class="p">(</span><span class="n">job</span><span class="p">,</span> <span class="n">cls_params</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;[classifier fit] hyperparams=</span><span class="si">{</span><span class="n">cls_params</span><span class="si">}</span><span class="s1"> [took </span><span class="si">{</span><span class="n">took</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">s]&#39;</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;[classifier fit] hyperparams=</span><span class="si">{</span><span class="n">cls_params</span><span class="si">}</span><span class="s1"> [took </span><span class="si">{</span><span class="n">took</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">s]&#39;</span><span class="p">)</span>
<span class="k">return</span> <span class="n">model</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span> <span class="k">return</span> <span class="n">model</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span>
<span class="k">def</span> <span class="nf">_prepare_aggregation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">_prepare_aggregation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
<span class="n">model</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">cls_took</span><span class="p">,</span> <span class="n">cls_params</span><span class="p">,</span> <span class="n">q_params</span> <span class="o">=</span> <span class="n">args</span> <span class="n">model</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">cls_took</span><span class="p">,</span> <span class="n">cls_params</span><span class="p">,</span> <span class="n">q_params</span> <span class="o">=</span> <span class="n">args</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">model</span><span class="p">)</span> <span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
<span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="o">**</span><span class="n">cls_params</span><span class="p">,</span> <span class="o">**</span><span class="n">q_params</span><span class="p">}</span> <span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="o">**</span><span class="n">cls_params</span><span class="p">,</span> <span class="o">**</span><span class="n">q_params</span><span class="p">}</span>
<span class="k">def</span> <span class="nf">job</span><span class="p">(</span><span class="n">q_params</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">job</span><span class="p">(</span><span class="n">q_params</span><span class="p">):</span>
<span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">q_params</span><span class="p">)</span> <span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">q_params</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">aggregation_fit</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_training</span><span class="p">)</span> <span class="n">P</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">predictions</span>
<span class="n">model</span><span class="o">.</span><span class="n">aggregation_fit</span><span class="p">(</span><span class="n">P</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="n">score</span> <span class="o">=</span> <span class="n">evaluation</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">protocol</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">protocol</span><span class="p">,</span> <span class="n">error_metric</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">error</span><span class="p">)</span> <span class="n">score</span> <span class="o">=</span> <span class="n">evaluation</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">protocol</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">protocol</span><span class="p">,</span> <span class="n">error_metric</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">error</span><span class="p">)</span>
<span class="k">return</span> <span class="n">score</span> <span class="k">return</span> <span class="n">score</span>
@ -203,12 +222,12 @@
<span class="bp">self</span><span class="o">.</span><span class="n">_print_status</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">aggr_took</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">_print_status</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">aggr_took</span><span class="p">)</span>
<span class="k">return</span> <span class="n">model</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="p">(</span><span class="n">cls_took</span><span class="o">+</span><span class="n">aggr_took</span><span class="p">)</span> <span class="k">return</span> <span class="n">model</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="p">(</span><span class="n">cls_took</span><span class="o">+</span><span class="n">aggr_took</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_prepare_nonaggr_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">_prepare_nonaggr_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span> <span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">job</span><span class="p">(</span><span class="n">params</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">job</span><span class="p">(</span><span class="n">params</span><span class="p">):</span>
<span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span> <span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_training</span><span class="p">)</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_training_X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_training_y</span><span class="p">)</span>
<span class="n">score</span> <span class="o">=</span> <span class="n">evaluation</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">protocol</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">protocol</span><span class="p">,</span> <span class="n">error_metric</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">error</span><span class="p">)</span> <span class="n">score</span> <span class="o">=</span> <span class="n">evaluation</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">protocol</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">protocol</span><span class="p">,</span> <span class="n">error_metric</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">error</span><span class="p">)</span>
<span class="k">return</span> <span class="n">score</span> <span class="k">return</span> <span class="n">score</span>
@ -216,7 +235,7 @@
<span class="bp">self</span><span class="o">.</span><span class="n">_print_status</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">_print_status</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span><span class="p">)</span>
<span class="k">return</span> <span class="n">model</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span> <span class="k">return</span> <span class="n">model</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span>
<span class="k">def</span> <span class="nf">_break_down_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">_break_down_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Decides whether to break down the fit phase in two (classifier-fit followed by aggregation-fit).</span> <span class="sd"> Decides whether to break down the fit phase in two (classifier-fit followed by aggregation-fit).</span>
<span class="sd"> In order to do so, some conditions should be met: a) the quantifier is of type aggregative,</span> <span class="sd"> In order to do so, some conditions should be met: a) the quantifier is of type aggregative,</span>
@ -231,17 +250,19 @@
<span class="k">return</span> <span class="kc">False</span> <span class="k">return</span> <span class="kc">False</span>
<span class="k">return</span> <span class="kc">True</span> <span class="k">return</span> <span class="kc">True</span>
<span class="k">def</span> <span class="nf">_compute_scores_aggregative</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">training</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">_compute_scores_aggregative</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="c1"># break down the set of hyperparameters into two: classifier-specific, quantifier-specific</span> <span class="c1"># break down the set of hyperparameters into two: classifier-specific, quantifier-specific</span>
<span class="n">cls_configs</span><span class="p">,</span> <span class="n">q_configs</span> <span class="o">=</span> <span class="n">group_params</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span><span class="p">)</span> <span class="n">cls_configs</span><span class="p">,</span> <span class="n">q_configs</span> <span class="o">=</span> <span class="n">group_params</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span><span class="p">)</span>
<span class="c1"># train all classifiers and get the predictions</span> <span class="c1"># train all classifiers and get the predictions</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_training</span> <span class="o">=</span> <span class="n">training</span> <span class="bp">self</span><span class="o">.</span><span class="n">_training_X</span> <span class="o">=</span> <span class="n">X</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_training_y</span> <span class="o">=</span> <span class="n">y</span>
<span class="n">cls_outs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span> <span class="n">cls_outs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_prepare_classifier</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prepare_classifier</span><span class="p">,</span>
<span class="n">cls_configs</span><span class="p">,</span> <span class="n">cls_configs</span><span class="p">,</span>
<span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;_R_SEED&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span> <span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;_R_SEED&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
<span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">,</span>
<span class="n">asarray</span><span class="o">=</span><span class="kc">False</span>
<span class="p">)</span> <span class="p">)</span>
<span class="c1"># filter out classifier configurations that yielded any error</span> <span class="c1"># filter out classifier configurations that yielded any error</span>
@ -266,9 +287,10 @@
<span class="k">return</span> <span class="n">aggr_outs</span> <span class="k">return</span> <span class="n">aggr_outs</span>
<span class="k">def</span> <span class="nf">_compute_scores_nonaggregative</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">training</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">_compute_scores_nonaggregative</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="n">configs</span> <span class="o">=</span> <span class="n">expand_grid</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span><span class="p">)</span> <span class="n">configs</span> <span class="o">=</span> <span class="n">expand_grid</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_training</span> <span class="o">=</span> <span class="n">training</span> <span class="bp">self</span><span class="o">.</span><span class="n">_training_X</span> <span class="o">=</span> <span class="n">X</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_training_y</span> <span class="o">=</span> <span class="n">y</span>
<span class="n">scores</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span> <span class="n">scores</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_prepare_nonaggr_model</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prepare_nonaggr_model</span><span class="p">,</span>
<span class="n">configs</span><span class="p">,</span> <span class="n">configs</span><span class="p">,</span>
@ -277,17 +299,20 @@
<span class="p">)</span> <span class="p">)</span>
<span class="k">return</span> <span class="n">scores</span> <span class="k">return</span> <span class="n">scores</span>
<span class="k">def</span> <span class="nf">_print_status</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">_print_status</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span><span class="p">):</span>
<span class="k">if</span> <span class="n">status</span><span class="o">.</span><span class="n">success</span><span class="p">():</span> <span class="k">if</span> <span class="n">status</span><span class="o">.</span><span class="n">success</span><span class="p">():</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;hyperparams=[</span><span class="si">{</span><span class="n">params</span><span class="si">}</span><span class="s1">]</span><span class="se">\t</span><span class="s1"> got </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1"> = </span><span class="si">{</span><span class="n">score</span><span class="si">:</span><span class="s1">.5f</span><span class="si">}</span><span class="s1"> [took </span><span class="si">{</span><span class="n">took</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">s]&#39;</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;hyperparams=[</span><span class="si">{</span><span class="n">params</span><span class="si">}</span><span class="s1">]</span><span class="se">\t</span><span class="s1"> got </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1"> = </span><span class="si">{</span><span class="n">score</span><span class="si">:</span><span class="s1">.5f</span><span class="si">}</span><span class="s1"> [took </span><span class="si">{</span><span class="n">took</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">s]&#39;</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span> <span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;error=</span><span class="si">{</span><span class="n">status</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;error=</span><span class="si">{</span><span class="n">status</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
<div class="viewcode-block" id="GridSearchQ.fit"><a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.fit">[docs]</a> <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">training</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span> <div class="viewcode-block" id="GridSearchQ.fit">
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.fit">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot; Learning routine. Fits methods with all combinations of hyperparameters and selects the one minimizing</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot; Learning routine. Fits methods with all combinations of hyperparameters and selects the one minimizing</span>
<span class="sd"> the error metric.</span> <span class="sd"> the error metric.</span>
<span class="sd"> :param training: the training set on which to optimize the hyperparameters</span> <span class="sd"> :param X: array-like, training covariates</span>
<span class="sd"> :param y: array-like, labels of training data</span>
<span class="sd"> :return: self</span> <span class="sd"> :return: self</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
@ -303,9 +328,9 @@
<span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;starting model selection with n_jobs=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;starting model selection with n_jobs=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_break_down_fit</span><span class="p">():</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_break_down_fit</span><span class="p">():</span>
<span class="n">results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_scores_aggregative</span><span class="p">(</span><span class="n">training</span><span class="p">)</span> <span class="n">results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_scores_aggregative</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span> <span class="k">else</span><span class="p">:</span>
<span class="n">results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_scores_nonaggregative</span><span class="p">(</span><span class="n">training</span><span class="p">)</span> <span class="n">results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_scores_nonaggregative</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">param_scores_</span> <span class="o">=</span> <span class="p">{}</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_scores_</span> <span class="o">=</span> <span class="p">{}</span>
<span class="bp">self</span><span class="o">.</span><span class="n">best_score_</span> <span class="o">=</span> <span class="kc">None</span> <span class="bp">self</span><span class="o">.</span><span class="n">best_score_</span> <span class="o">=</span> <span class="kc">None</span>
@ -320,13 +345,13 @@
<span class="bp">self</span><span class="o">.</span><span class="n">param_scores_</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">params</span><span class="p">)]</span> <span class="o">=</span> <span class="n">status</span><span class="o">.</span><span class="n">status</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_scores_</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">params</span><span class="p">)]</span> <span class="o">=</span> <span class="n">status</span><span class="o">.</span><span class="n">status</span>
<span class="bp">self</span><span class="o">.</span><span class="n">error_collector</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">status</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">error_collector</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">status</span><span class="p">)</span>
<span class="n">tend</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span><span class="o">-</span><span class="n">tinit</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_time_</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span><span class="o">-</span><span class="n">tinit</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">best_score_</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">best_score_</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;no combination of hyperparameters seemed to work&#39;</span><span class="p">)</span> <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;no combination of hyperparameters seemed to work&#39;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;optimization finished: best params </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">best_params_</span><span class="si">}</span><span class="s1"> (score=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">best_score_</span><span class="si">:</span><span class="s1">.5f</span><span class="si">}</span><span class="s1">) &#39;</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;optimization finished: best params </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">best_params_</span><span class="si">}</span><span class="s1"> (score=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">best_score_</span><span class="si">:</span><span class="s1">.5f</span><span class="si">}</span><span class="s1">) &#39;</span>
<span class="sa">f</span><span class="s1">&#39;[took </span><span class="si">{</span><span class="n">tend</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">s]&#39;</span><span class="p">)</span> <span class="sa">f</span><span class="s1">&#39;[took </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">fit_time_</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">s]&#39;</span><span class="p">)</span>
<span class="n">no_errors</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">error_collector</span><span class="p">)</span> <span class="n">no_errors</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">error_collector</span><span class="p">)</span>
<span class="k">if</span> <span class="n">no_errors</span><span class="o">&gt;</span><span class="mi">0</span><span class="p">:</span> <span class="k">if</span> <span class="n">no_errors</span><span class="o">&gt;</span><span class="mi">0</span><span class="p">:</span>
@ -338,7 +363,10 @@
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">protocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">protocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
<span class="n">tinit</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span> <span class="n">tinit</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;refitting on the whole development set&#39;</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;refitting on the whole development set&#39;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">best_model_</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">training</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">protocol</span><span class="o">.</span><span class="n">get_labelled_collection</span><span class="p">())</span> <span class="n">validation_collection</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">protocol</span><span class="o">.</span><span class="n">get_labelled_collection</span><span class="p">()</span>
<span class="n">training_collection</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="n">validation_collection</span><span class="o">.</span><span class="n">classes</span><span class="p">)</span>
<span class="n">devel_collection</span> <span class="o">=</span> <span class="n">training_collection</span> <span class="o">+</span> <span class="n">validation_collection</span>
<span class="bp">self</span><span class="o">.</span><span class="n">best_model_</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="o">*</span><span class="n">devel_collection</span><span class="o">.</span><span class="n">Xy</span><span class="p">)</span>
<span class="n">tend</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">tinit</span> <span class="n">tend</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">tinit</span>
<span class="bp">self</span><span class="o">.</span><span class="n">refit_time_</span> <span class="o">=</span> <span class="n">tend</span> <span class="bp">self</span><span class="o">.</span><span class="n">refit_time_</span> <span class="o">=</span> <span class="n">tend</span>
<span class="k">else</span><span class="p">:</span> <span class="k">else</span><span class="p">:</span>
@ -347,24 +375,33 @@
<span class="k">return</span> <span class="bp">self</span></div> <span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="GridSearchQ.quantify"><a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.quantify">[docs]</a> <span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
<div class="viewcode-block" id="GridSearchQ.predict">
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.predict">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Estimate class prevalence values using the best model found after calling the :meth:`fit` method.</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;Estimate class prevalence values using the best model found after calling the :meth:`fit` method.</span>
<span class="sd"> :param instances: sample contanining the instances</span> <span class="sd"> :param X: sample contanining the instances</span>
<span class="sd"> :return: a ndarray of shape `(n_classes)` with class prevalence estimates as according to the best model found</span> <span class="sd"> :return: a ndarray of shape `(n_classes)` with class prevalence estimates as according to the best model found</span>
<span class="sd"> by the model selection process.</span> <span class="sd"> by the model selection process.</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;best_model_&#39;</span><span class="p">),</span> <span class="s1">&#39;quantify called before fit&#39;</span> <span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;best_model_&#39;</span><span class="p">),</span> <span class="s1">&#39;quantify called before fit&#39;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">best_model</span><span class="p">()</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span></div> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">best_model</span><span class="p">()</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
<div class="viewcode-block" id="GridSearchQ.set_params"><a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.set_params">[docs]</a> <span class="k">def</span> <span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">):</span>
<div class="viewcode-block" id="GridSearchQ.set_params">
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.set_params">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Sets the hyper-parameters to explore.</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;Sets the hyper-parameters to explore.</span>
<span class="sd"> :param parameters: a dictionary with keys the parameter names and values the list of values to explore</span> <span class="sd"> :param parameters: a dictionary with keys the parameter names and values the list of values to explore</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span> <span class="o">=</span> <span class="n">parameters</span></div> <span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span> <span class="o">=</span> <span class="n">parameters</span></div>
<div class="viewcode-block" id="GridSearchQ.get_params"><a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.get_params">[docs]</a> <span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<div class="viewcode-block" id="GridSearchQ.get_params">
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.get_params">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Returns the dictionary of hyper-parameters to explore (`param_grid`)</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;Returns the dictionary of hyper-parameters to explore (`param_grid`)</span>
<span class="sd"> :param deep: Unused</span> <span class="sd"> :param deep: Unused</span>
@ -372,7 +409,10 @@
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span></div> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span></div>
<div class="viewcode-block" id="GridSearchQ.best_model"><a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.best_model">[docs]</a> <span class="k">def</span> <span class="nf">best_model</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<div class="viewcode-block" id="GridSearchQ.best_model">
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.best_model">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">best_model</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the best model found after calling the :meth:`fit` method, i.e., the one trained on the combination</span> <span class="sd"> Returns the best model found after calling the :meth:`fit` method, i.e., the one trained on the combination</span>
<span class="sd"> of hyper-parameters that minimized the error function.</span> <span class="sd"> of hyper-parameters that minimized the error function.</span>
@ -383,7 +423,8 @@
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">best_model_</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">best_model_</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;best_model called before fit&#39;</span><span class="p">)</span></div> <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;best_model called before fit&#39;</span><span class="p">)</span></div>
<span class="k">def</span> <span class="nf">_error_handler</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_error_handler</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Endorses one job with two returned values: the status, and the time of execution</span> <span class="sd"> Endorses one job with two returned values: the status, and the time of execution</span>
@ -396,11 +437,11 @@
<span class="n">output</span> <span class="o">=</span> <span class="kc">None</span> <span class="n">output</span> <span class="o">=</span> <span class="kc">None</span>
<span class="k">def</span> <span class="nf">_handle</span><span class="p">(</span><span class="n">status</span><span class="p">,</span> <span class="n">exception</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">_handle</span><span class="p">(</span><span class="n">status</span><span class="p">,</span> <span class="n">exception</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">raise_errors</span><span class="p">:</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">raise_errors</span><span class="p">:</span>
<span class="k">raise</span> <span class="n">exception</span> <span class="k">raise</span> <span class="n">exception</span>
<span class="k">else</span><span class="p">:</span> <span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">ConfigStatus</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">status</span><span class="p">)</span> <span class="k">return</span> <span class="n">ConfigStatus</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">msg</span><span class="o">=</span><span class="nb">str</span><span class="p">(</span><span class="n">exception</span><span class="p">))</span>
<span class="k">try</span><span class="p">:</span> <span class="k">try</span><span class="p">:</span>
<span class="k">with</span> <span class="n">timeout</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">timeout</span><span class="p">):</span> <span class="k">with</span> <span class="n">timeout</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">timeout</span><span class="p">):</span>
@ -421,7 +462,10 @@
<span class="k">return</span> <span class="n">output</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span></div> <span class="k">return</span> <span class="n">output</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span></div>
<div class="viewcode-block" id="cross_val_predict"><a class="viewcode-back" href="../../quapy.html#quapy.model_selection.cross_val_predict">[docs]</a><span class="k">def</span> <span class="nf">cross_val_predict</span><span class="p">(</span><span class="n">quantifier</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">nfolds</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<div class="viewcode-block" id="cross_val_predict">
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.cross_val_predict">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">cross_val_predict</span><span class="p">(</span><span class="n">quantifier</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">nfolds</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Akin to `scikit-learn&#39;s cross_val_predict &lt;https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_predict.html&gt;`_</span> <span class="sd"> Akin to `scikit-learn&#39;s cross_val_predict &lt;https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_predict.html&gt;`_</span>
<span class="sd"> but for quantification.</span> <span class="sd"> but for quantification.</span>
@ -436,15 +480,18 @@
<span class="n">total_prev</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">n_classes</span><span class="p">)</span> <span class="n">total_prev</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">n_classes</span><span class="p">)</span>
<span class="k">for</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">kFCV</span><span class="p">(</span><span class="n">nfolds</span><span class="o">=</span><span class="n">nfolds</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">):</span> <span class="k">for</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">kFCV</span><span class="p">(</span><span class="n">nfolds</span><span class="o">=</span><span class="n">nfolds</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">):</span>
<span class="n">quantifier</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train</span><span class="p">)</span> <span class="n">quantifier</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="o">*</span><span class="n">train</span><span class="o">.</span><span class="n">Xy</span><span class="p">)</span>
<span class="n">fold_prev</span> <span class="o">=</span> <span class="n">quantifier</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">X</span><span class="p">)</span> <span class="n">fold_prev</span> <span class="o">=</span> <span class="n">quantifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">X</span><span class="p">)</span>
<span class="n">rel_size</span> <span class="o">=</span> <span class="mf">1.</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">test</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="n">rel_size</span> <span class="o">=</span> <span class="mf">1.</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">test</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="n">total_prev</span> <span class="o">+=</span> <span class="n">fold_prev</span><span class="o">*</span><span class="n">rel_size</span> <span class="n">total_prev</span> <span class="o">+=</span> <span class="n">fold_prev</span><span class="o">*</span><span class="n">rel_size</span>
<span class="k">return</span> <span class="n">total_prev</span></div> <span class="k">return</span> <span class="n">total_prev</span></div>
<div class="viewcode-block" id="expand_grid"><a class="viewcode-back" href="../../quapy.html#quapy.model_selection.expand_grid">[docs]</a><span class="k">def</span> <span class="nf">expand_grid</span><span class="p">(</span><span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
<div class="viewcode-block" id="expand_grid">
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.expand_grid">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">expand_grid</span><span class="p">(</span><span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Expands a param_grid dictionary as a list of configurations.</span> <span class="sd"> Expands a param_grid dictionary as a list of configurations.</span>
<span class="sd"> Example:</span> <span class="sd"> Example:</span>
@ -463,7 +510,10 @@
<span class="k">return</span> <span class="n">configs</span></div> <span class="k">return</span> <span class="n">configs</span></div>
<div class="viewcode-block" id="group_params"><a class="viewcode-back" href="../../quapy.html#quapy.model_selection.group_params">[docs]</a><span class="k">def</span> <span class="nf">group_params</span><span class="p">(</span><span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
<div class="viewcode-block" id="group_params">
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.group_params">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">group_params</span><span class="p">(</span><span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Partitions a param_grid dictionary as two lists of configurations, one for the classifier-specific</span> <span class="sd"> Partitions a param_grid dictionary as two lists of configurations, one for the classifier-specific</span>
<span class="sd"> hyper-parameters, and another for que quantifier-specific hyper-parameters</span> <span class="sd"> hyper-parameters, and another for que quantifier-specific hyper-parameters</span>
@ -484,6 +534,7 @@
<span class="k">return</span> <span class="n">classifier_configs</span><span class="p">,</span> <span class="n">quantifier_configs</span></div> <span class="k">return</span> <span class="n">classifier_configs</span><span class="p">,</span> <span class="n">quantifier_configs</span></div>
</pre></div> </pre></div>
</div> </div>

259
docs/build/html/_modules/quapy/plot.html vendored
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@ -1,22 +1,20 @@
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@ -70,16 +74,16 @@
<div itemprop="articleBody"> <div itemprop="articleBody">
<h1>Source code for quapy.plot</h1><div class="highlight"><pre> <h1>Source code for quapy.plot</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span> <span></span><span class="kn">from</span><span class="w"> </span><span class="nn">collections</span><span class="w"> </span><span class="kn">import</span> <span class="n">defaultdict</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span> <span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">matplotlib.cm</span> <span class="kn">import</span> <span class="n">get_cmap</span> <span class="kn">from</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="kn">import</span> <span class="n">get_cmap</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">cm</span> <span class="kn">from</span><span class="w"> </span><span class="nn">matplotlib</span><span class="w"> </span><span class="kn">import</span> <span class="n">cm</span>
<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">ttest_ind_from_stats</span> <span class="kn">from</span><span class="w"> </span><span class="nn">scipy.stats</span><span class="w"> </span><span class="kn">import</span> <span class="n">ttest_ind_from_stats</span>
<span class="kn">from</span> <span class="nn">matplotlib.ticker</span> <span class="kn">import</span> <span class="n">ScalarFormatter</span> <span class="kn">from</span><span class="w"> </span><span class="nn">matplotlib.ticker</span><span class="w"> </span><span class="kn">import</span> <span class="n">ScalarFormatter</span>
<span class="kn">import</span> <span class="nn">math</span> <span class="kn">import</span><span class="w"> </span><span class="nn">math</span>
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span> <span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;figure.figsize&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6</span><span class="p">]</span> <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;figure.figsize&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6</span><span class="p">]</span>
<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;figure.dpi&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">200</span> <span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;figure.dpi&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">200</span>
@ -88,7 +92,7 @@
<div class="viewcode-block" id="binary_diagonal"> <div class="viewcode-block" id="binary_diagonal">
<a class="viewcode-back" href="../../quapy.html#quapy.plot.binary_diagonal">[docs]</a> <a class="viewcode-back" href="../../quapy.html#quapy.plot.binary_diagonal">[docs]</a>
<span class="k">def</span> <span class="nf">binary_diagonal</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show_std</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="k">def</span><span class="w"> </span><span class="nf">binary_diagonal</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show_std</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">train_prev</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">savepath</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">method_order</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> <span class="n">train_prev</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">savepath</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">method_order</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> The diagonal plot displays the predicted prevalence values (along the y-axis) as a function of the true prevalence</span> <span class="sd"> The diagonal plot displays the predicted prevalence values (along the y-axis) as a function of the true prevalence</span>
@ -97,21 +101,29 @@
<span class="sd"> indicating which class is to be taken as the positive class. (For multiclass quantification problems, other plots</span> <span class="sd"> indicating which class is to be taken as the positive class. (For multiclass quantification problems, other plots</span>
<span class="sd"> like the :meth:`error_by_drift` might be preferable though).</span> <span class="sd"> like the :meth:`error_by_drift` might be preferable though).</span>
<span class="sd"> The format convention is as follows: `method_names`, `true_prevs`, and `estim_prevs` are array-like of the same</span>
<span class="sd"> length, with the ith element describing the output of an independent experiment. The elements of `true_prevs`, and</span>
<span class="sd"> `estim_prevs` are `ndarrays` with coherent shape for the same experiment. Experiments for the same method on</span>
<span class="sd"> different datasets can be used, in which case the method name can appear more than once in `method_names`.</span>
<span class="sd"> :param method_names: array-like with the method names for each experiment</span> <span class="sd"> :param method_names: array-like with the method names for each experiment</span>
<span class="sd"> :param true_prevs: array-like with the true prevalence values (each being a ndarray with n_classes components) for</span> <span class="sd"> :param true_prevs: array-like with the true prevalence values for each experiment. Each entry is a ndarray of</span>
<span class="sd"> each experiment</span> <span class="sd"> shape `(n_samples, n_classes)` components.</span>
<span class="sd"> :param estim_prevs: array-like with the estimated prevalence values (each being a ndarray with n_classes components)</span> <span class="sd"> :param estim_prevs: array-like with the estimated prevalence values for each experiment. Each entry is a ndarray of</span>
<span class="sd"> for each experiment</span> <span class="sd"> shape `(n_samples, n_classes)` components and `n_samples` must coincide with the corresponding entry in</span>
<span class="sd"> :param pos_class: index of the positive class</span> <span class="sd"> `true_prevs`.</span>
<span class="sd"> :param title: the title to be displayed in the plot</span> <span class="sd"> :param pos_class: index of the positive class (default 1)</span>
<span class="sd"> :param show_std: whether or not to show standard deviations (represented by color bands). This might be inconvenient</span> <span class="sd"> :param title: the title to be displayed in the plot (default None)</span>
<span class="sd"> :param show_std: whether to show standard deviations (represented by color bands). This might be inconvenient</span>
<span class="sd"> for cases in which many methods are compared, or when the standard deviations are high -- default True)</span> <span class="sd"> for cases in which many methods are compared, or when the standard deviations are high -- default True)</span>
<span class="sd"> :param legend: whether or not to display the leyend (default True)</span> <span class="sd"> :param legend: whether to display the legend (default True)</span>
<span class="sd"> :param train_prev: if indicated (default is None), the training prevalence (for the positive class) is hightlighted</span> <span class="sd"> :param train_prev: if indicated (default is None), the training prevalence (for the positive class) is highlighted</span>
<span class="sd"> in the plot. This is convenient when all the experiments have been conducted in the same dataset.</span> <span class="sd"> in the plot. This is convenient when all the experiments have been conducted in the same dataset, or in</span>
<span class="sd"> datasets with the same training prevalence.</span>
<span class="sd"> :param savepath: path where to save the plot. If not indicated (as default), the plot is shown.</span> <span class="sd"> :param savepath: path where to save the plot. If not indicated (as default), the plot is shown.</span>
<span class="sd"> :param method_order: if indicated (default is None), imposes the order in which the methods are processed (i.e.,</span> <span class="sd"> :param method_order: if indicated (default is None), imposes the order in which the methods are processed (i.e.,</span>
<span class="sd"> listed in the legend and associated with matplotlib colors).</span> <span class="sd"> listed in the legend and associated with matplotlib colors).</span>
<span class="sd"> :return: returns (fig, ax) matplotlib objects for eventual customisation</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_aspect</span><span class="p">(</span><span class="s1">&#39;equal&#39;</span><span class="p">)</span> <span class="n">ax</span><span class="o">.</span><span class="n">set_aspect</span><span class="p">(</span><span class="s1">&#39;equal&#39;</span><span class="p">)</span>
@ -152,31 +164,34 @@
<span class="k">if</span> <span class="n">legend</span><span class="p">:</span> <span class="k">if</span> <span class="n">legend</span><span class="p">:</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;center left&#39;</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">))</span> <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;center left&#39;</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">))</span>
<span class="c1"># box = ax.get_position()</span>
<span class="c1"># ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])</span>
<span class="c1"># ax.legend(loc=&#39;lower center&#39;,</span>
<span class="c1"># bbox_to_anchor=(1, -0.5),</span>
<span class="c1"># ncol=(len(method_names)+1)//2)</span>
<span class="n">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">)</span></div> <span class="n">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">)</span>
<span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span></div>
<div class="viewcode-block" id="binary_bias_global"> <div class="viewcode-block" id="binary_bias_global">
<a class="viewcode-back" href="../../quapy.html#quapy.plot.binary_bias_global">[docs]</a> <a class="viewcode-back" href="../../quapy.html#quapy.plot.binary_bias_global">[docs]</a>
<span class="k">def</span> <span class="nf">binary_bias_global</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">savepath</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">binary_bias_global</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">savepath</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Box-plots displaying the global bias (i.e., signed error computed as the estimated value minus the true value)</span> <span class="sd"> Box-plots displaying the global bias (i.e., signed error computed as the estimated value minus the true value)</span>
<span class="sd"> for each quantification method with respect to a given positive class.</span> <span class="sd"> for each quantification method with respect to a given positive class.</span>
<span class="sd"> The format convention is as follows: `method_names`, `true_prevs`, and `estim_prevs` are array-like of the same</span>
<span class="sd"> length, with the ith element describing the output of an independent experiment. The elements of `true_prevs`, and</span>
<span class="sd"> `estim_prevs` are `ndarrays` with coherent shape for the same experiment. Experiments for the same method on</span>
<span class="sd"> different datasets can be used, in which case the method name can appear more than once in `method_names`.</span>
<span class="sd"> :param method_names: array-like with the method names for each experiment</span> <span class="sd"> :param method_names: array-like with the method names for each experiment</span>
<span class="sd"> :param true_prevs: array-like with the true prevalence values (each being a ndarray with n_classes components) for</span> <span class="sd"> :param true_prevs: array-like with the true prevalence values for each experiment. Each entry is a ndarray of</span>
<span class="sd"> each experiment</span> <span class="sd"> shape `(n_samples, n_classes)` components.</span>
<span class="sd"> :param estim_prevs: array-like with the estimated prevalence values (each being a ndarray with n_classes components)</span> <span class="sd"> :param estim_prevs: array-like with the estimated prevalence values for each experiment. Each entry is a ndarray of</span>
<span class="sd"> for each experiment</span> <span class="sd"> shape `(n_samples, n_classes)` components and `n_samples` must coincide with the corresponding entry in</span>
<span class="sd"> `true_prevs`.</span>
<span class="sd"> :param pos_class: index of the positive class</span> <span class="sd"> :param pos_class: index of the positive class</span>
<span class="sd"> :param title: the title to be displayed in the plot</span> <span class="sd"> :param title: the title to be displayed in the plot (default None)</span>
<span class="sd"> :param savepath: path where to save the plot. If not indicated (as default), the plot is shown.</span> <span class="sd"> :param savepath: path where to save the plot. If not indicated (as default), the plot is shown.</span>
<span class="sd"> :return: returns (fig, ax) matplotlib objects for eventual customisation</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span> <span class="o">=</span> <span class="n">_merge</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">)</span> <span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span> <span class="o">=</span> <span class="n">_merge</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">)</span>
@ -195,32 +210,41 @@
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span> <span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">ylabel</span><span class="o">=</span><span class="s1">&#39;error bias&#39;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="n">title</span><span class="p">)</span> <span class="n">ax</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">ylabel</span><span class="o">=</span><span class="s1">&#39;error bias&#39;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="n">title</span><span class="p">)</span>
<span class="n">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">)</span></div> <span class="n">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">)</span>
<span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span></div>
<div class="viewcode-block" id="binary_bias_bins"> <div class="viewcode-block" id="binary_bias_bins">
<a class="viewcode-back" href="../../quapy.html#quapy.plot.binary_bias_bins">[docs]</a> <a class="viewcode-back" href="../../quapy.html#quapy.plot.binary_bias_bins">[docs]</a>
<span class="k">def</span> <span class="nf">binary_bias_bins</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">colormap</span><span class="o">=</span><span class="n">cm</span><span class="o">.</span><span class="n">tab10</span><span class="p">,</span> <span class="k">def</span><span class="w"> </span><span class="nf">binary_bias_bins</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">colormap</span><span class="o">=</span><span class="n">cm</span><span class="o">.</span><span class="n">tab10</span><span class="p">,</span>
<span class="n">vertical_xticks</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">savepath</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> <span class="n">vertical_xticks</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">savepath</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Box-plots displaying the local bias (i.e., signed error computed as the estimated value minus the true value)</span> <span class="sd"> Box-plots displaying the local bias (i.e., signed error computed as the estimated value minus the true value)</span>
<span class="sd"> for different bins of (true) prevalence of the positive classs, for each quantification method.</span> <span class="sd"> for different bins of (true) prevalence of the positive class, for each quantification method.</span>
<span class="sd"> The format convention is as follows: `method_names`, `true_prevs`, and `estim_prevs` are array-like of the same</span>
<span class="sd"> length, with the ith element describing the output of an independent experiment. The elements of `true_prevs`, and</span>
<span class="sd"> `estim_prevs` are `ndarrays` with coherent shape for the same experiment. Experiments for the same method on</span>
<span class="sd"> different datasets can be used, in which case the method name can appear more than once in `method_names`.</span>
<span class="sd"> :param method_names: array-like with the method names for each experiment</span> <span class="sd"> :param method_names: array-like with the method names for each experiment</span>
<span class="sd"> :param true_prevs: array-like with the true prevalence values (each being a ndarray with n_classes components) for</span> <span class="sd"> :param true_prevs: array-like with the true prevalence values for each experiment. Each entry is a ndarray of</span>
<span class="sd"> each experiment</span> <span class="sd"> shape `(n_samples, n_classes)` components.</span>
<span class="sd"> :param estim_prevs: array-like with the estimated prevalence values (each being a ndarray with n_classes components)</span> <span class="sd"> :param estim_prevs: array-like with the estimated prevalence values for each experiment. Each entry is a ndarray of</span>
<span class="sd"> for each experiment</span> <span class="sd"> shape `(n_samples, n_classes)` components and `n_samples` must coincide with the corresponding entry in</span>
<span class="sd"> `true_prevs`.</span>
<span class="sd"> :param pos_class: index of the positive class</span> <span class="sd"> :param pos_class: index of the positive class</span>
<span class="sd"> :param title: the title to be displayed in the plot</span> <span class="sd"> :param title: the title to be displayed in the plot (default None)</span>
<span class="sd"> :param nbins: number of bins</span> <span class="sd"> :param nbins: number of bins (default 5)</span>
<span class="sd"> :param colormap: the matplotlib colormap to use (default cm.tab10)</span> <span class="sd"> :param colormap: the matplotlib colormap to use (default cm.tab10)</span>
<span class="sd"> :param vertical_xticks: whether or not to add secondary grid (default is False)</span> <span class="sd"> :param vertical_xticks: whether or not to add secondary grid (default is False)</span>
<span class="sd"> :param legend: whether or not to display the legend (default is True)</span> <span class="sd"> :param legend: whether or not to display the legend (default is True)</span>
<span class="sd"> :param savepath: path where to save the plot. If not indicated (as default), the plot is shown.</span> <span class="sd"> :param savepath: path where to save the plot. If not indicated (as default), the plot is shown.</span>
<span class="sd"> :return: returns (fig, ax) matplotlib objects for eventual customisation</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="kn">from</span> <span class="nn">pylab</span> <span class="kn">import</span> <span class="n">boxplot</span><span class="p">,</span> <span class="n">plot</span><span class="p">,</span> <span class="n">setp</span> <span class="kn">from</span><span class="w"> </span><span class="nn">pylab</span><span class="w"> </span><span class="kn">import</span> <span class="n">boxplot</span><span class="p">,</span> <span class="n">plot</span><span class="p">,</span> <span class="n">setp</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span> <span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
@ -288,18 +312,20 @@
<span class="n">ax</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">xlabel</span><span class="o">=</span><span class="s1">&#39;prevalence&#39;</span><span class="p">,</span> <span class="n">ylabel</span><span class="o">=</span><span class="s1">&#39;error bias&#39;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="n">title</span><span class="p">)</span> <span class="n">ax</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">xlabel</span><span class="o">=</span><span class="s1">&#39;prevalence&#39;</span><span class="p">,</span> <span class="n">ylabel</span><span class="o">=</span><span class="s1">&#39;error bias&#39;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="n">title</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">)</span></div> <span class="n">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">)</span>
<span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span></div>
<div class="viewcode-block" id="error_by_drift"> <div class="viewcode-block" id="error_by_drift">
<a class="viewcode-back" href="../../quapy.html#quapy.plot.error_by_drift">[docs]</a> <a class="viewcode-back" href="../../quapy.html#quapy.plot.error_by_drift">[docs]</a>
<span class="k">def</span> <span class="nf">error_by_drift</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">tr_prevs</span><span class="p">,</span> <span class="k">def</span><span class="w"> </span><span class="nf">error_by_drift</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">tr_prevs</span><span class="p">,</span>
<span class="n">n_bins</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">error_name</span><span class="o">=</span><span class="s1">&#39;ae&#39;</span><span class="p">,</span> <span class="n">show_std</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">n_bins</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">error_name</span><span class="o">=</span><span class="s1">&#39;ae&#39;</span><span class="p">,</span> <span class="n">show_std</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">show_density</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">show_density</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">show_legend</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">show_legend</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">logscale</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">logscale</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">title</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;Quantification error as a function of distribution shift&#39;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">vlines</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">vlines</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">method_order</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">method_order</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">savepath</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> <span class="n">savepath</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
@ -310,11 +336,17 @@
<span class="sd"> fare in different regions of the prior probability shift spectrum (e.g., in the low-shift regime vs. in the</span> <span class="sd"> fare in different regions of the prior probability shift spectrum (e.g., in the low-shift regime vs. in the</span>
<span class="sd"> high-shift regime).</span> <span class="sd"> high-shift regime).</span>
<span class="sd"> The format convention is as follows: `method_names`, `true_prevs`, and `estim_prevs` are array-like of the same</span>
<span class="sd"> length, with the ith element describing the output of an independent experiment. The elements of `true_prevs`, and</span>
<span class="sd"> `estim_prevs` are `ndarrays` with coherent shape for the same experiment. Experiments for the same method on</span>
<span class="sd"> different datasets can be used, in which case the method name can appear more than once in `method_names`.</span>
<span class="sd"> :param method_names: array-like with the method names for each experiment</span> <span class="sd"> :param method_names: array-like with the method names for each experiment</span>
<span class="sd"> :param true_prevs: array-like with the true prevalence values (each being a ndarray with n_classes components) for</span> <span class="sd"> :param true_prevs: array-like with the true prevalence values for each experiment. Each entry is a ndarray of</span>
<span class="sd"> each experiment</span> <span class="sd"> shape `(n_samples, n_classes)` components.</span>
<span class="sd"> :param estim_prevs: array-like with the estimated prevalence values (each being a ndarray with n_classes components)</span> <span class="sd"> :param estim_prevs: array-like with the estimated prevalence values for each experiment. Each entry is a ndarray of</span>
<span class="sd"> for each experiment</span> <span class="sd"> shape `(n_samples, n_classes)` components and `n_samples` must coincide with the corresponding entry in</span>
<span class="sd"> `true_prevs`.</span>
<span class="sd"> :param tr_prevs: training prevalence of each experiment</span> <span class="sd"> :param tr_prevs: training prevalence of each experiment</span>
<span class="sd"> :param n_bins: number of bins in which the y-axis is to be divided (default is 20)</span> <span class="sd"> :param n_bins: number of bins in which the y-axis is to be divided (default is 20)</span>
<span class="sd"> :param error_name: a string representing the name of an error function (as defined in `quapy.error`, default is &quot;ae&quot;)</span> <span class="sd"> :param error_name: a string representing the name of an error function (as defined in `quapy.error`, default is &quot;ae&quot;)</span>
@ -322,12 +354,13 @@
<span class="sd"> :param show_density: whether or not to display the distribution of experiments for each bin (default is True)</span> <span class="sd"> :param show_density: whether or not to display the distribution of experiments for each bin (default is True)</span>
<span class="sd"> :param show_density: whether or not to display the legend of the chart (default is True)</span> <span class="sd"> :param show_density: whether or not to display the legend of the chart (default is True)</span>
<span class="sd"> :param logscale: whether or not to log-scale the y-error measure (default is False)</span> <span class="sd"> :param logscale: whether or not to log-scale the y-error measure (default is False)</span>
<span class="sd"> :param title: title of the plot (default is &quot;Quantification error as a function of distribution shift&quot;)</span> <span class="sd"> :param title: title of the plot (default is None)</span>
<span class="sd"> :param vlines: array-like list of values (default is None). If indicated, highlights some regions of the space</span> <span class="sd"> :param vlines: array-like list of values (default is None). If indicated, highlights some regions of the space</span>
<span class="sd"> using vertical dotted lines.</span> <span class="sd"> using vertical dotted lines.</span>
<span class="sd"> :param method_order: if indicated (default is None), imposes the order in which the methods are processed (i.e.,</span> <span class="sd"> :param method_order: if indicated (default is None), imposes the order in which the methods are processed (i.e.,</span>
<span class="sd"> listed in the legend and associated with matplotlib colors).</span> <span class="sd"> listed in the legend and associated with matplotlib colors).</span>
<span class="sd"> :param savepath: path where to save the plot. If not indicated (as default), the plot is shown.</span> <span class="sd"> :param savepath: path where to save the plot. If not indicated (as default), the plot is shown.</span>
<span class="sd"> :return: returns (fig, ax) matplotlib objects for eventual customisation</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
@ -336,14 +369,14 @@
<span class="n">x_error</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">ae</span> <span class="n">x_error</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">ae</span>
<span class="n">y_error</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="p">,</span> <span class="n">error_name</span><span class="p">)</span> <span class="n">y_error</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="p">,</span> <span class="n">error_name</span><span class="p">)</span>
<span class="k">if</span> <span class="n">method_order</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">method_order</span> <span class="o">=</span> <span class="p">[]</span>
<span class="c1"># get all data as a dictionary {&#39;m&#39;:{&#39;x&#39;:ndarray, &#39;y&#39;:ndarray}} where &#39;m&#39; is a method name (in the same</span> <span class="c1"># get all data as a dictionary {&#39;m&#39;:{&#39;x&#39;:ndarray, &#39;y&#39;:ndarray}} where &#39;m&#39; is a method name (in the same</span>
<span class="c1"># order as in method_order (if specified), and where &#39;x&#39; are the train-test shifts (computed as according to</span> <span class="c1"># order as in method_order (if specified), and where &#39;x&#39; are the train-test shifts (computed as according to</span>
<span class="c1"># x_error function) and &#39;y&#39; is the estim-test shift (computed as according to y_error)</span> <span class="c1"># x_error function) and &#39;y&#39; is the estim-test shift (computed as according to y_error)</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">_join_data_by_drift</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">tr_prevs</span><span class="p">,</span> <span class="n">x_error</span><span class="p">,</span> <span class="n">y_error</span><span class="p">,</span> <span class="n">method_order</span><span class="p">)</span> <span class="n">data</span> <span class="o">=</span> <span class="n">_join_data_by_drift</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">tr_prevs</span><span class="p">,</span> <span class="n">x_error</span><span class="p">,</span> <span class="n">y_error</span><span class="p">,</span> <span class="n">method_order</span><span class="p">)</span>
<span class="k">if</span> <span class="n">method_order</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">method_order</span> <span class="o">=</span> <span class="n">method_names</span>
<span class="n">_set_colors</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">n_methods</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">method_order</span><span class="p">))</span> <span class="n">_set_colors</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">n_methods</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">method_order</span><span class="p">))</span>
<span class="n">bins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">n_bins</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="n">bins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">n_bins</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
@ -396,11 +429,11 @@
<span class="n">ax2</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">&#39;right&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s1">&#39;g&#39;</span><span class="p">)</span> <span class="n">ax2</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">&#39;right&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s1">&#39;g&#39;</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;g&#39;</span><span class="p">)</span> <span class="n">ax2</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">&#39;g&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">xlabel</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;Distribution shift between training set and test sample&#39;</span><span class="p">,</span> <span class="n">ax</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">xlabel</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;Prior shift between training set and test sample&#39;</span><span class="p">,</span>
<span class="n">ylabel</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">error_name</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span><span class="si">}</span><span class="s1"> (true distribution, predicted distribution)&#39;</span><span class="p">,</span> <span class="n">ylabel</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">error_name</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span><span class="si">}</span><span class="s1"> (true prev, predicted prev)&#39;</span><span class="p">,</span>
<span class="n">title</span><span class="o">=</span><span class="n">title</span><span class="p">)</span> <span class="n">title</span><span class="o">=</span><span class="n">title</span><span class="p">)</span>
<span class="n">box</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_position</span><span class="p">()</span> <span class="c1"># box = ax.get_position()</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_position</span><span class="p">([</span><span class="n">box</span><span class="o">.</span><span class="n">x0</span><span class="p">,</span> <span class="n">box</span><span class="o">.</span><span class="n">y0</span><span class="p">,</span> <span class="n">box</span><span class="o">.</span><span class="n">width</span> <span class="o">*</span> <span class="mf">0.8</span><span class="p">,</span> <span class="n">box</span><span class="o">.</span><span class="n">height</span><span class="p">])</span> <span class="c1"># ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])</span>
<span class="k">if</span> <span class="n">vlines</span><span class="p">:</span> <span class="k">if</span> <span class="n">vlines</span><span class="p">:</span>
<span class="k">for</span> <span class="n">vline</span> <span class="ow">in</span> <span class="n">vlines</span><span class="p">:</span> <span class="k">for</span> <span class="n">vline</span> <span class="ow">in</span> <span class="n">vlines</span><span class="p">:</span>
<span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">vline</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;--&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">)</span> <span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">vline</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;--&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">)</span>
@ -410,19 +443,20 @@
<span class="c1">#nice scale for the logaritmic axis</span> <span class="c1">#nice scale for the logaritmic axis</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">10</span> <span class="o">**</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">max_y</span><span class="p">)))</span> <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">10</span> <span class="o">**</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">max_y</span><span class="p">)))</span>
<span class="k">if</span> <span class="n">show_legend</span><span class="p">:</span> <span class="k">if</span> <span class="n">show_legend</span><span class="p">:</span>
<span class="n">fig</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;lower center&#39;</span><span class="p">,</span> <span class="n">fig</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;center left&#39;</span><span class="p">,</span>
<span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">),</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">),</span>
<span class="n">ncol</span><span class="o">=</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">method_names</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">//</span><span class="mi">2</span><span class="p">)</span> <span class="n">ncol</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">)</span></div> <span class="n">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">)</span>
<span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span></div>
<div class="viewcode-block" id="brokenbar_supremacy_by_drift"> <div class="viewcode-block" id="brokenbar_supremacy_by_drift">
<a class="viewcode-back" href="../../quapy.html#quapy.plot.brokenbar_supremacy_by_drift">[docs]</a> <a class="viewcode-back" href="../../quapy.html#quapy.plot.brokenbar_supremacy_by_drift">[docs]</a>
<span class="k">def</span> <span class="nf">brokenbar_supremacy_by_drift</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">tr_prevs</span><span class="p">,</span> <span class="k">def</span><span class="w"> </span><span class="nf">brokenbar_supremacy_by_drift</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">tr_prevs</span><span class="p">,</span>
<span class="n">n_bins</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">binning</span><span class="o">=</span><span class="s1">&#39;isomerous&#39;</span><span class="p">,</span> <span class="n">n_bins</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">binning</span><span class="o">=</span><span class="s1">&#39;isomerous&#39;</span><span class="p">,</span>
<span class="n">x_error</span><span class="o">=</span><span class="s1">&#39;ae&#39;</span><span class="p">,</span> <span class="n">y_error</span><span class="o">=</span><span class="s1">&#39;ae&#39;</span><span class="p">,</span> <span class="n">ttest_alpha</span><span class="o">=</span><span class="mf">0.005</span><span class="p">,</span> <span class="n">tail_density_threshold</span><span class="o">=</span><span class="mf">0.005</span><span class="p">,</span> <span class="n">x_error</span><span class="o">=</span><span class="s1">&#39;ae&#39;</span><span class="p">,</span> <span class="n">y_error</span><span class="o">=</span><span class="s1">&#39;ae&#39;</span><span class="p">,</span> <span class="n">ttest_alpha</span><span class="o">=</span><span class="mf">0.005</span><span class="p">,</span> <span class="n">tail_density_threshold</span><span class="o">=</span><span class="mf">0.005</span><span class="p">,</span>
<span class="n">method_order</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">method_order</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
@ -436,11 +470,17 @@
<span class="sd"> plot is displayed on top, that displays the distribution of experiments for each bin (when binning=&quot;isometric&quot;) or</span> <span class="sd"> plot is displayed on top, that displays the distribution of experiments for each bin (when binning=&quot;isometric&quot;) or</span>
<span class="sd"> the percentiles points of the distribution (when binning=&quot;isomerous&quot;).</span> <span class="sd"> the percentiles points of the distribution (when binning=&quot;isomerous&quot;).</span>
<span class="sd"> The format convention is as follows: `method_names`, `true_prevs`, and `estim_prevs` are array-like of the same</span>
<span class="sd"> length, with the ith element describing the output of an independent experiment. The elements of `true_prevs`, and</span>
<span class="sd"> `estim_prevs` are `ndarrays` with coherent shape for the same experiment. Experiments for the same method on</span>
<span class="sd"> different datasets can be used, in which case the method name can appear more than once in `method_names`.</span>
<span class="sd"> :param method_names: array-like with the method names for each experiment</span> <span class="sd"> :param method_names: array-like with the method names for each experiment</span>
<span class="sd"> :param true_prevs: array-like with the true prevalence values (each being a ndarray with n_classes components) for</span> <span class="sd"> :param true_prevs: array-like with the true prevalence values for each experiment. Each entry is a ndarray of</span>
<span class="sd"> each experiment</span> <span class="sd"> shape `(n_samples, n_classes)` components.</span>
<span class="sd"> :param estim_prevs: array-like with the estimated prevalence values (each being a ndarray with n_classes components)</span> <span class="sd"> :param estim_prevs: array-like with the estimated prevalence values for each experiment. Each entry is a ndarray of</span>
<span class="sd"> for each experiment</span> <span class="sd"> shape `(n_samples, n_classes)` components and `n_samples` must coincide with the corresponding entry in</span>
<span class="sd"> `true_prevs`.</span>
<span class="sd"> :param tr_prevs: training prevalence of each experiment</span> <span class="sd"> :param tr_prevs: training prevalence of each experiment</span>
<span class="sd"> :param n_bins: number of bins in which the y-axis is to be divided (default is 20)</span> <span class="sd"> :param n_bins: number of bins in which the y-axis is to be divided (default is 20)</span>
<span class="sd"> :param binning: type of binning, either &quot;isomerous&quot; (default) or &quot;isometric&quot;</span> <span class="sd"> :param binning: type of binning, either &quot;isomerous&quot; (default) or &quot;isometric&quot;</span>
@ -457,13 +497,16 @@
<span class="sd"> :param method_order: if indicated (default is None), imposes the order in which the methods are processed (i.e.,</span> <span class="sd"> :param method_order: if indicated (default is None), imposes the order in which the methods are processed (i.e.,</span>
<span class="sd"> listed in the legend and associated with matplotlib colors).</span> <span class="sd"> listed in the legend and associated with matplotlib colors).</span>
<span class="sd"> :param savepath: path where to save the plot. If not indicated (as default), the plot is shown.</span> <span class="sd"> :param savepath: path where to save the plot. If not indicated (as default), the plot is shown.</span>
<span class="sd"> :return:</span> <span class="sd"> :return: returns (fig, ax) matplotlib objects for eventual customisation</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">assert</span> <span class="n">binning</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;isomerous&#39;</span><span class="p">,</span> <span class="s1">&#39;isometric&#39;</span><span class="p">],</span> <span class="s1">&#39;unknown binning type; valid types are &quot;isomerous&quot; and &quot;isometric&quot;&#39;</span> <span class="k">assert</span> <span class="n">binning</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;isomerous&#39;</span><span class="p">,</span> <span class="s1">&#39;isometric&#39;</span><span class="p">],</span> <span class="s1">&#39;unknown binning type; valid types are &quot;isomerous&quot; and &quot;isometric&quot;&#39;</span>
<span class="n">x_error</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="p">,</span> <span class="n">x_error</span><span class="p">)</span> <span class="n">x_error</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="p">,</span> <span class="n">x_error</span><span class="p">)</span>
<span class="n">y_error</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="p">,</span> <span class="n">y_error</span><span class="p">)</span> <span class="n">y_error</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="p">,</span> <span class="n">y_error</span><span class="p">)</span>
<span class="k">if</span> <span class="n">method_order</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">method_order</span> <span class="o">=</span> <span class="p">[]</span>
<span class="c1"># get all data as a dictionary {&#39;m&#39;:{&#39;x&#39;:ndarray, &#39;y&#39;:ndarray}} where &#39;m&#39; is a method name (in the same</span> <span class="c1"># get all data as a dictionary {&#39;m&#39;:{&#39;x&#39;:ndarray, &#39;y&#39;:ndarray}} where &#39;m&#39; is a method name (in the same</span>
<span class="c1"># order as in method_order (if specified), and where &#39;x&#39; are the train-test shifts (computed as according to</span> <span class="c1"># order as in method_order (if specified), and where &#39;x&#39; are the train-test shifts (computed as according to</span>
<span class="c1"># x_error function) and &#39;y&#39; is the estim-test shift (computed as according to y_error)</span> <span class="c1"># x_error function) and &#39;y&#39; is the estim-test shift (computed as according to y_error)</span>
@ -602,11 +645,13 @@
<span class="n">ax</span><span class="o">.</span><span class="n">get_xaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_xaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">wspace</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">hspace</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">wspace</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">hspace</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">)</span></div> <span class="n">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">)</span>
<span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span></div>
<span class="k">def</span> <span class="nf">_merge</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">_merge</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">):</span>
<span class="n">ndims</span> <span class="o">=</span> <span class="n">true_prevs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="n">ndims</span> <span class="o">=</span> <span class="n">true_prevs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;true&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">ndims</span><span class="p">)),</span> <span class="s1">&#39;estim&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">ndims</span><span class="p">))})</span> <span class="n">data</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;true&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">ndims</span><span class="p">)),</span> <span class="s1">&#39;estim&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">ndims</span><span class="p">))})</span>
<span class="n">method_order</span><span class="o">=</span><span class="p">[]</span> <span class="n">method_order</span><span class="o">=</span><span class="p">[]</span>
@ -620,13 +665,14 @@
<span class="k">return</span> <span class="n">method_order</span><span class="p">,</span> <span class="n">true_prevs_</span><span class="p">,</span> <span class="n">estim_prevs_</span> <span class="k">return</span> <span class="n">method_order</span><span class="p">,</span> <span class="n">true_prevs_</span><span class="p">,</span> <span class="n">estim_prevs_</span>
<span class="k">def</span> <span class="nf">_set_colors</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">n_methods</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">_set_colors</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">n_methods</span><span class="p">):</span>
<span class="n">NUM_COLORS</span> <span class="o">=</span> <span class="n">n_methods</span> <span class="n">NUM_COLORS</span> <span class="o">=</span> <span class="n">n_methods</span>
<span class="n">cm</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;tab20&#39;</span><span class="p">)</span> <span class="k">if</span> <span class="n">NUM_COLORS</span><span class="o">&gt;</span><span class="mi">10</span><span class="p">:</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_prop_cycle</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="n">cm</span><span class="p">(</span><span class="mf">1.</span> <span class="o">*</span> <span class="n">i</span> <span class="o">/</span> <span class="n">NUM_COLORS</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">NUM_COLORS</span><span class="p">)])</span> <span class="n">cm</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="s1">&#39;tab20&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_prop_cycle</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="n">cm</span><span class="p">(</span><span class="mf">1.</span> <span class="o">*</span> <span class="n">i</span> <span class="o">/</span> <span class="n">NUM_COLORS</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">NUM_COLORS</span><span class="p">)])</span>
<span class="k">def</span> <span class="nf">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">):</span>
<span class="c1"># if savepath is specified, then saves the plot in that path; otherwise the plot is shown</span> <span class="c1"># if savepath is specified, then saves the plot in that path; otherwise the plot is shown</span>
<span class="k">if</span> <span class="n">savepath</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> <span class="k">if</span> <span class="n">savepath</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">create_parent_dir</span><span class="p">(</span><span class="n">savepath</span><span class="p">)</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">create_parent_dir</span><span class="p">(</span><span class="n">savepath</span><span class="p">)</span>
@ -636,7 +682,7 @@
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">_join_data_by_drift</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">tr_prevs</span><span class="p">,</span> <span class="n">x_error</span><span class="p">,</span> <span class="n">y_error</span><span class="p">,</span> <span class="n">method_order</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">_join_data_by_drift</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">tr_prevs</span><span class="p">,</span> <span class="n">x_error</span><span class="p">,</span> <span class="n">y_error</span><span class="p">,</span> <span class="n">method_order</span><span class="p">):</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;x&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">)),</span> <span class="s1">&#39;y&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">))})</span> <span class="n">data</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;x&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">)),</span> <span class="s1">&#39;y&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">))})</span>
<span class="k">if</span> <span class="n">method_order</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="k">if</span> <span class="n">method_order</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
@ -655,6 +701,45 @@
<span class="n">method_order</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">method</span><span class="p">)</span> <span class="n">method_order</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">method</span><span class="p">)</span>
<span class="k">return</span> <span class="n">data</span> <span class="k">return</span> <span class="n">data</span>
<div class="viewcode-block" id="calibration_plot">
<a class="viewcode-back" href="../../quapy.html#quapy.plot.calibration_plot">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">calibration_plot</span><span class="p">(</span><span class="n">prob_classifier</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">savepath</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="n">posteriors</span> <span class="o">=</span> <span class="n">prob_classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="k">assert</span> <span class="n">posteriors</span><span class="o">.</span><span class="n">ndim</span><span class="o">==</span><span class="mi">2</span><span class="p">,</span> <span class="s1">&#39;calibration plot only works for binary problems&#39;</span>
<span class="n">posteriors</span> <span class="o">=</span> <span class="n">posteriors</span><span class="p">[:,</span><span class="mi">1</span><span class="p">]</span>
<span class="n">pred_y</span> <span class="o">=</span> <span class="n">posteriors</span><span class="o">&gt;=</span><span class="mf">0.5</span>
<span class="n">bins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">nbins</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">binned_values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">digitize</span><span class="p">(</span><span class="n">posteriors</span><span class="p">,</span> <span class="n">bins</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">binned_values</span><span class="p">))</span>
<span class="n">correct</span> <span class="o">=</span> <span class="n">pred_y</span> <span class="o">==</span> <span class="n">y</span>
<span class="n">bin_centers</span> <span class="o">=</span> <span class="p">(</span><span class="n">bins</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">bins</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span> <span class="o">/</span> <span class="mi">2</span>
<span class="n">bins_names</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">nbins</span><span class="p">)</span>
<span class="n">y_axis</span> <span class="o">=</span> <span class="p">[</span><span class="n">correct</span><span class="p">[</span><span class="n">binned_values</span><span class="o">==</span><span class="nb">bin</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="k">for</span> <span class="nb">bin</span> <span class="ow">in</span> <span class="n">bins_names</span><span class="p">]</span>
<span class="n">y_axis</span> <span class="o">=</span> <span class="p">[</span><span class="n">v</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">else</span> <span class="mi">0</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">y_axis</span><span class="p">]</span>
<span class="c1"># Crear el gráfico de barras</span>
<span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">bin_centers</span><span class="p">,</span> <span class="n">y_axis</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="n">bins</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">bins</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
<span class="c1"># Etiquetas y título</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Bin&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Value&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">&quot;Bar plot of calculated values per bin&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">bin_centers</span><span class="p">,</span> <span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">b</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">b</span> <span class="ow">in</span> <span class="n">bin_centers</span><span class="p">],</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
<span class="c1"># Mostrar el gráfico</span>
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span></div>
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LogisticRegression</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_UCIBinaryDataset</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">UCI_BINARY_DATASETS</span><span class="p">[</span><span class="mi">6</span><span class="p">])</span>
<span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">train_test</span>
<span class="n">classifier</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">()</span>
<span class="n">classifier</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="o">*</span><span class="n">train</span><span class="o">.</span><span class="n">Xy</span><span class="p">)</span>
<span class="n">calibration_plot</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="o">*</span><span class="n">test</span><span class="o">.</span><span class="n">Xy</span><span class="p">)</span>
</pre></div> </pre></div>
</div> </div>

381
docs/build/html/_modules/quapy/protocol.html vendored
View File

@ -1,23 +1,20 @@
<!DOCTYPE html> <!DOCTYPE html>
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@ -43,7 +40,13 @@
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@ -71,25 +74,31 @@
<div itemprop="articleBody"> <div itemprop="articleBody">
<h1>Source code for quapy.protocol</h1><div class="highlight"><pre> <h1>Source code for quapy.protocol</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span> <span></span><span class="kn">from</span><span class="w"> </span><span class="nn">copy</span><span class="w"> </span><span class="kn">import</span> <span class="n">deepcopy</span>
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span> <span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Iterable</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">itertools</span> <span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
<span class="kn">from</span> <span class="nn">contextlib</span> <span class="kn">import</span> <span class="n">ExitStack</span> <span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">ABCMeta</span><span class="p">,</span> <span class="n">abstractmethod</span> <span class="kn">import</span><span class="w"> </span><span class="nn">itertools</span>
<span class="kn">from</span> <span class="nn">quapy.data</span> <span class="kn">import</span> <span class="n">LabelledCollection</span> <span class="kn">from</span><span class="w"> </span><span class="nn">contextlib</span><span class="w"> </span><span class="kn">import</span> <span class="n">ExitStack</span>
<span class="kn">import</span> <span class="nn">quapy.functional</span> <span class="k">as</span> <span class="nn">F</span> <span class="kn">from</span><span class="w"> </span><span class="nn">abc</span><span class="w"> </span><span class="kn">import</span> <span class="n">ABCMeta</span><span class="p">,</span> <span class="n">abstractmethod</span>
<span class="kn">from</span> <span class="nn">os.path</span> <span class="kn">import</span> <span class="n">exists</span> <span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data</span><span class="w"> </span><span class="kn">import</span> <span class="n">LabelledCollection</span>
<span class="kn">from</span> <span class="nn">glob</span> <span class="kn">import</span> <span class="n">glob</span> <span class="kn">import</span><span class="w"> </span><span class="nn">quapy.functional</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">F</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">os.path</span><span class="w"> </span><span class="kn">import</span> <span class="n">exists</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">glob</span><span class="w"> </span><span class="kn">import</span> <span class="n">glob</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">collections.abc</span><span class="w"> </span><span class="kn">import</span> <span class="n">Iterable</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">numbers</span><span class="w"> </span><span class="kn">import</span> <span class="n">Number</span>
<div class="viewcode-block" id="AbstractProtocol"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractProtocol">[docs]</a><span class="k">class</span> <span class="nc">AbstractProtocol</span><span class="p">(</span><span class="n">metaclass</span><span class="o">=</span><span class="n">ABCMeta</span><span class="p">):</span> <div class="viewcode-block" id="AbstractProtocol">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractProtocol">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">AbstractProtocol</span><span class="p">(</span><span class="n">metaclass</span><span class="o">=</span><span class="n">ABCMeta</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Abstract parent class for sample generation protocols.</span> <span class="sd"> Abstract parent class for sample generation protocols.</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="nd">@abstractmethod</span> <span class="nd">@abstractmethod</span>
<span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Implements the protocol. Yields one sample at a time along with its prevalence</span> <span class="sd"> Implements the protocol. Yields one sample at a time along with its prevalence</span>
@ -98,25 +107,31 @@
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="o">...</span> <span class="o">...</span>
<div class="viewcode-block" id="AbstractProtocol.total"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractProtocol.total">[docs]</a> <span class="k">def</span> <span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <div class="viewcode-block" id="AbstractProtocol.total">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractProtocol.total">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Indicates the total number of samples that the protocol generates.</span> <span class="sd"> Indicates the total number of samples that the protocol generates.</span>
<span class="sd"> :return: The number of samples to generate if known, or `None` otherwise.</span> <span class="sd"> :return: The number of samples to generate if known, or `None` otherwise.</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="kc">None</span></div></div> <span class="k">return</span> <span class="kc">None</span></div>
</div>
<div class="viewcode-block" id="IterateProtocol"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.IterateProtocol">[docs]</a><span class="k">class</span> <span class="nc">IterateProtocol</span><span class="p">(</span><span class="n">AbstractProtocol</span><span class="p">):</span>
<div class="viewcode-block" id="IterateProtocol">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.IterateProtocol">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">IterateProtocol</span><span class="p">(</span><span class="n">AbstractProtocol</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> A very simple protocol which simply iterates over a list of previously generated samples</span> <span class="sd"> A very simple protocol which simply iterates over a list of previously generated samples</span>
<span class="sd"> :param samples: a list of :class:`quapy.data.base.LabelledCollection`</span> <span class="sd"> :param samples: a list of :class:`quapy.data.base.LabelledCollection`</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">samples</span><span class="p">:</span> <span class="p">[</span><span class="n">LabelledCollection</span><span class="p">]):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">samples</span><span class="p">:</span> <span class="p">[</span><span class="n">LabelledCollection</span><span class="p">]):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">samples</span> <span class="o">=</span> <span class="n">samples</span> <span class="bp">self</span><span class="o">.</span><span class="n">samples</span> <span class="o">=</span> <span class="n">samples</span>
<span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Yields one sample from the initial list at a time</span> <span class="sd"> Yields one sample from the initial list at a time</span>
@ -126,16 +141,58 @@
<span class="k">for</span> <span class="n">sample</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">samples</span><span class="p">:</span> <span class="k">for</span> <span class="n">sample</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">samples</span><span class="p">:</span>
<span class="k">yield</span> <span class="n">sample</span><span class="o">.</span><span class="n">Xp</span> <span class="k">yield</span> <span class="n">sample</span><span class="o">.</span><span class="n">Xp</span>
<div class="viewcode-block" id="IterateProtocol.total"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.IterateProtocol.total">[docs]</a> <span class="k">def</span> <span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <div class="viewcode-block" id="IterateProtocol.total">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.IterateProtocol.total">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the number of samples in this protocol</span> <span class="sd"> Returns the number of samples in this protocol</span>
<span class="sd"> :return: int</span> <span class="sd"> :return: int</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">samples</span><span class="p">)</span></div></div> <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">samples</span><span class="p">)</span></div>
</div>
<div class="viewcode-block" id="AbstractStochasticSeededProtocol"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractStochasticSeededProtocol">[docs]</a><span class="k">class</span> <span class="nc">AbstractStochasticSeededProtocol</span><span class="p">(</span><span class="n">AbstractProtocol</span><span class="p">):</span>
<div class="viewcode-block" id="ProtocolFromIndex">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.ProtocolFromIndex">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">ProtocolFromIndex</span><span class="p">(</span><span class="n">AbstractProtocol</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> A protocol from a list of indexes</span>
<span class="sd"> :param data: a :class:`quapy.data.base.LabelledCollection`</span>
<span class="sd"> :param indexes: a list of indexes</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">indexes</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span>
<span class="bp">self</span><span class="o">.</span><span class="n">indexes</span> <span class="o">=</span> <span class="n">indexes</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Yields one sample at a time extracted using the indexes</span>
<span class="sd"> :return: yields a tuple `(sample, prev) at a time, where `sample` is a set of instances</span>
<span class="sd"> and in which `prev` is an `nd.array` with the class prevalence values</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">for</span> <span class="n">index</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexes</span><span class="p">:</span>
<span class="k">yield</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">index</span><span class="p">)</span><span class="o">.</span><span class="n">Xp</span>
<div class="viewcode-block" id="ProtocolFromIndex.total">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.ProtocolFromIndex.total">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the number of samples in this protocol</span>
<span class="sd"> :return: int</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">indexes</span><span class="p">)</span></div>
</div>
<div class="viewcode-block" id="AbstractStochasticSeededProtocol">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractStochasticSeededProtocol">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">AbstractStochasticSeededProtocol</span><span class="p">(</span><span class="n">AbstractProtocol</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> An `AbstractStochasticSeededProtocol` is a protocol that generates, via any random procedure (e.g.,</span> <span class="sd"> An `AbstractStochasticSeededProtocol` is a protocol that generates, via any random procedure (e.g.,</span>
<span class="sd"> via random sampling), sequences of :class:`quapy.data.base.LabelledCollection` samples.</span> <span class="sd"> via random sampling), sequences of :class:`quapy.data.base.LabelledCollection` samples.</span>
@ -152,19 +209,21 @@
<span class="n">_random_state</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span> <span class="c1"># means &quot;not set&quot;</span> <span class="n">_random_state</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span> <span class="c1"># means &quot;not set&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
<span class="nd">@property</span> <span class="nd">@property</span>
<span class="k">def</span> <span class="nf">random_state</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">random_state</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_random_state</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_random_state</span>
<span class="nd">@random_state</span><span class="o">.</span><span class="n">setter</span> <span class="nd">@random_state</span><span class="o">.</span><span class="n">setter</span>
<span class="k">def</span> <span class="nf">random_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">random_state</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">random_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">random_state</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_random_state</span> <span class="o">=</span> <span class="n">random_state</span> <span class="bp">self</span><span class="o">.</span><span class="n">_random_state</span> <span class="o">=</span> <span class="n">random_state</span>
<div class="viewcode-block" id="AbstractStochasticSeededProtocol.samples_parameters"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.samples_parameters">[docs]</a> <span class="nd">@abstractmethod</span> <div class="viewcode-block" id="AbstractStochasticSeededProtocol.samples_parameters">
<span class="k">def</span> <span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.samples_parameters">[docs]</a>
<span class="nd">@abstractmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> This function has to return all the necessary parameters to replicate the samples</span> <span class="sd"> This function has to return all the necessary parameters to replicate the samples</span>
@ -172,8 +231,11 @@
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="o">...</span></div> <span class="o">...</span></div>
<div class="viewcode-block" id="AbstractStochasticSeededProtocol.sample"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.sample">[docs]</a> <span class="nd">@abstractmethod</span>
<span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span> <div class="viewcode-block" id="AbstractStochasticSeededProtocol.sample">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.sample">[docs]</a>
<span class="nd">@abstractmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Extract one sample determined by the given parameters</span> <span class="sd"> Extract one sample determined by the given parameters</span>
@ -182,7 +244,8 @@
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="o">...</span></div> <span class="o">...</span></div>
<span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Yields one sample at a time. The type of object returned depends on the `collator` function. The</span> <span class="sd"> Yields one sample at a time. The type of object returned depends on the `collator` function. The</span>
<span class="sd"> default behaviour returns tuples of the form `(sample, prevalence)`.</span> <span class="sd"> default behaviour returns tuples of the form `(sample, prevalence)`.</span>
@ -197,9 +260,11 @@
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">stack</span><span class="o">.</span><span class="n">enter_context</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">))</span> <span class="n">stack</span><span class="o">.</span><span class="n">enter_context</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">))</span>
<span class="k">for</span> <span class="n">params</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">samples_parameters</span><span class="p">():</span> <span class="k">for</span> <span class="n">params</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">samples_parameters</span><span class="p">():</span>
<span class="k">yield</span> <span class="bp">self</span><span class="o">.</span><span class="n">collator</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">params</span><span class="p">))</span> <span class="k">yield</span> <span class="bp">self</span><span class="o">.</span><span class="n">collator</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">params</span><span class="p">),</span> <span class="n">params</span><span class="p">)</span>
<div class="viewcode-block" id="AbstractStochasticSeededProtocol.collator"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.collator">[docs]</a> <span class="k">def</span> <span class="nf">collator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span> <div class="viewcode-block" id="AbstractStochasticSeededProtocol.collator">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.collator">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">collator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> The collator prepares the sample to accommodate the desired output format before returning the output.</span> <span class="sd"> The collator prepares the sample to accommodate the desired output format before returning the output.</span>
<span class="sd"> This collator simply returns the sample as it is. Classes inheriting from this abstract class can</span> <span class="sd"> This collator simply returns the sample as it is. Classes inheriting from this abstract class can</span>
@ -209,17 +274,23 @@
<span class="sd"> :param args: additional arguments</span> <span class="sd"> :param args: additional arguments</span>
<span class="sd"> :return: the sample adhering to a desired output format (in this case, the sample is returned as it is)</span> <span class="sd"> :return: the sample adhering to a desired output format (in this case, the sample is returned as it is)</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">sample</span></div></div> <span class="k">return</span> <span class="n">sample</span></div>
</div>
<div class="viewcode-block" id="OnLabelledCollectionProtocol"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.OnLabelledCollectionProtocol">[docs]</a><span class="k">class</span> <span class="nc">OnLabelledCollectionProtocol</span><span class="p">:</span>
<div class="viewcode-block" id="OnLabelledCollectionProtocol">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.OnLabelledCollectionProtocol">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">OnLabelledCollectionProtocol</span><span class="p">(</span><span class="n">AbstractStochasticSeededProtocol</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Protocols that generate samples from a :class:`qp.data.LabelledCollection` object.</span> <span class="sd"> Protocols that generate samples from a :class:`qp.data.LabelledCollection` object.</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="n">RETURN_TYPES</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;sample_prev&#39;</span><span class="p">,</span> <span class="s1">&#39;labelled_collection&#39;</span><span class="p">,</span> <span class="s1">&#39;index&#39;</span><span class="p">]</span> <span class="n">RETURN_TYPES</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;sample_prev&#39;</span><span class="p">,</span> <span class="s1">&#39;labelled_collection&#39;</span><span class="p">,</span> <span class="s1">&#39;index&#39;</span><span class="p">]</span>
<div class="viewcode-block" id="OnLabelledCollectionProtocol.get_labelled_collection"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.OnLabelledCollectionProtocol.get_labelled_collection">[docs]</a> <span class="k">def</span> <span class="nf">get_labelled_collection</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <div class="viewcode-block" id="OnLabelledCollectionProtocol.get_labelled_collection">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.OnLabelledCollectionProtocol.get_labelled_collection">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">get_labelled_collection</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the labelled collection on which this protocol acts.</span> <span class="sd"> Returns the labelled collection on which this protocol acts.</span>
@ -227,7 +298,10 @@
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span></div> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span></div>
<div class="viewcode-block" id="OnLabelledCollectionProtocol.on_preclassified_instances"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.OnLabelledCollectionProtocol.on_preclassified_instances">[docs]</a> <span class="k">def</span> <span class="nf">on_preclassified_instances</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pre_classifications</span><span class="p">,</span> <span class="n">in_place</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<div class="viewcode-block" id="OnLabelledCollectionProtocol.on_preclassified_instances">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.OnLabelledCollectionProtocol.on_preclassified_instances">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">on_preclassified_instances</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pre_classifications</span><span class="p">,</span> <span class="n">in_place</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns a copy of this protocol that acts on a modified version of the original</span> <span class="sd"> Returns a copy of this protocol that acts on a modified version of the original</span>
<span class="sd"> :class:`qp.data.LabelledCollection` in which the original instances have been replaced</span> <span class="sd"> :class:`qp.data.LabelledCollection` in which the original instances have been replaced</span>
@ -250,8 +324,11 @@
<span class="n">new</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="n">new</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
<span class="k">return</span> <span class="n">new</span><span class="o">.</span><span class="n">on_preclassified_instances</span><span class="p">(</span><span class="n">pre_classifications</span><span class="p">,</span> <span class="n">in_place</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div> <span class="k">return</span> <span class="n">new</span><span class="o">.</span><span class="n">on_preclassified_instances</span><span class="p">(</span><span class="n">pre_classifications</span><span class="p">,</span> <span class="n">in_place</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>
<div class="viewcode-block" id="OnLabelledCollectionProtocol.get_collator"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.OnLabelledCollectionProtocol.get_collator">[docs]</a> <span class="nd">@classmethod</span>
<span class="k">def</span> <span class="nf">get_collator</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">return_type</span><span class="o">=</span><span class="s1">&#39;sample_prev&#39;</span><span class="p">):</span> <div class="viewcode-block" id="OnLabelledCollectionProtocol.get_collator">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.OnLabelledCollectionProtocol.get_collator">[docs]</a>
<span class="nd">@classmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">get_collator</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">return_type</span><span class="o">=</span><span class="s1">&#39;sample_prev&#39;</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns a collator function, i.e., a function that prepares the yielded data</span> <span class="sd"> Returns a collator function, i.e., a function that prepares the yielded data</span>
@ -264,12 +341,30 @@
<span class="k">assert</span> <span class="n">return_type</span> <span class="ow">in</span> <span class="bp">cls</span><span class="o">.</span><span class="n">RETURN_TYPES</span><span class="p">,</span> \ <span class="k">assert</span> <span class="n">return_type</span> <span class="ow">in</span> <span class="bp">cls</span><span class="o">.</span><span class="n">RETURN_TYPES</span><span class="p">,</span> \
<span class="sa">f</span><span class="s1">&#39;unknown return type passed as argument; valid ones are </span><span class="si">{</span><span class="bp">cls</span><span class="o">.</span><span class="n">RETURN_TYPES</span><span class="si">}</span><span class="s1">&#39;</span> <span class="sa">f</span><span class="s1">&#39;unknown return type passed as argument; valid ones are </span><span class="si">{</span><span class="bp">cls</span><span class="o">.</span><span class="n">RETURN_TYPES</span><span class="si">}</span><span class="s1">&#39;</span>
<span class="k">if</span> <span class="n">return_type</span><span class="o">==</span><span class="s1">&#39;sample_prev&#39;</span><span class="p">:</span> <span class="k">if</span> <span class="n">return_type</span><span class="o">==</span><span class="s1">&#39;sample_prev&#39;</span><span class="p">:</span>
<span class="k">return</span> <span class="k">lambda</span> <span class="n">lc</span><span class="p">:</span><span class="n">lc</span><span class="o">.</span><span class="n">Xp</span> <span class="k">return</span> <span class="k">lambda</span> <span class="n">lc</span><span class="p">,</span><span class="n">params</span><span class="p">:</span><span class="n">lc</span><span class="o">.</span><span class="n">Xp</span>
<span class="k">elif</span> <span class="n">return_type</span><span class="o">==</span><span class="s1">&#39;labelled_collection&#39;</span><span class="p">:</span> <span class="k">elif</span> <span class="n">return_type</span><span class="o">==</span><span class="s1">&#39;labelled_collection&#39;</span><span class="p">:</span>
<span class="k">return</span> <span class="k">lambda</span> <span class="n">lc</span><span class="p">:</span><span class="n">lc</span></div></div> <span class="k">return</span> <span class="k">lambda</span> <span class="n">lc</span><span class="p">,</span><span class="n">params</span><span class="p">:</span><span class="n">lc</span>
<span class="k">elif</span> <span class="n">return_type</span><span class="o">==</span><span class="s1">&#39;index&#39;</span><span class="p">:</span>
<span class="k">return</span> <span class="k">lambda</span> <span class="n">lc</span><span class="p">,</span><span class="n">params</span><span class="p">:</span><span class="n">params</span></div>
<div class="viewcode-block" id="APP"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.APP">[docs]</a><span class="k">class</span> <span class="nc">APP</span><span class="p">(</span><span class="n">AbstractStochasticSeededProtocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span> <div class="viewcode-block" id="OnLabelledCollectionProtocol.sample">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.OnLabelledCollectionProtocol.sample">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Realizes the sample given the index of the instances.</span>
<span class="sd"> :param index: indexes of the instances to select</span>
<span class="sd"> :return: an instance of :class:`qp.data.LabelledCollection`</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">index</span><span class="p">)</span></div>
</div>
<div class="viewcode-block" id="APP">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.APP">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">APP</span><span class="p">(</span><span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Implementation of the artificial prevalence protocol (APP).</span> <span class="sd"> Implementation of the artificial prevalence protocol (APP).</span>
<span class="sd"> The APP consists of exploring a grid of prevalence values containing `n_prevalences` points (e.g.,</span> <span class="sd"> The APP consists of exploring a grid of prevalence values containing `n_prevalences` points (e.g.,</span>
@ -293,7 +388,7 @@
<span class="sd"> to &quot;labelled_collection&quot; to get instead instances of LabelledCollection</span> <span class="sd"> to &quot;labelled_collection&quot; to get instead instances of LabelledCollection</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_prevalences</span><span class="o">=</span><span class="mi">21</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_prevalences</span><span class="o">=</span><span class="mi">21</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
<span class="n">smooth_limits_epsilon</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">sanity_check</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">return_type</span><span class="o">=</span><span class="s1">&#39;sample_prev&#39;</span><span class="p">):</span> <span class="n">smooth_limits_epsilon</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">sanity_check</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">return_type</span><span class="o">=</span><span class="s1">&#39;sample_prev&#39;</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">APP</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">random_state</span><span class="p">)</span> <span class="nb">super</span><span class="p">(</span><span class="n">APP</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">random_state</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span>
@ -313,7 +408,9 @@
<span class="bp">self</span><span class="o">.</span><span class="n">collator</span> <span class="o">=</span> <span class="n">OnLabelledCollectionProtocol</span><span class="o">.</span><span class="n">get_collator</span><span class="p">(</span><span class="n">return_type</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">collator</span> <span class="o">=</span> <span class="n">OnLabelledCollectionProtocol</span><span class="o">.</span><span class="n">get_collator</span><span class="p">(</span><span class="n">return_type</span><span class="p">)</span>
<div class="viewcode-block" id="APP.prevalence_grid"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.APP.prevalence_grid">[docs]</a> <span class="k">def</span> <span class="nf">prevalence_grid</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <div class="viewcode-block" id="APP.prevalence_grid">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.APP.prevalence_grid">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">prevalence_grid</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Generates vectors of prevalence values from an exhaustive grid of prevalence values. The</span> <span class="sd"> Generates vectors of prevalence values from an exhaustive grid of prevalence values. The</span>
<span class="sd"> number of prevalence values explored for each dimension depends on `n_prevalences`, so that, if, for example,</span> <span class="sd"> number of prevalence values explored for each dimension depends on `n_prevalences`, so that, if, for example,</span>
@ -338,7 +435,10 @@
<span class="n">prevs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">repeats</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="n">prevs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">repeats</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="k">return</span> <span class="n">prevs</span></div> <span class="k">return</span> <span class="n">prevs</span></div>
<div class="viewcode-block" id="APP.samples_parameters"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.APP.samples_parameters">[docs]</a> <span class="k">def</span> <span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<div class="viewcode-block" id="APP.samples_parameters">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.APP.samples_parameters">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Return all the necessary parameters to replicate the samples as according to the APP protocol.</span> <span class="sd"> Return all the necessary parameters to replicate the samples as according to the APP protocol.</span>
@ -350,25 +450,23 @@
<span class="n">indexes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> <span class="n">indexes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">index</span><span class="p">)</span>
<span class="k">return</span> <span class="n">indexes</span></div> <span class="k">return</span> <span class="n">indexes</span></div>
<div class="viewcode-block" id="APP.sample"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.APP.sample">[docs]</a> <span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Realizes the sample given the index of the instances.</span>
<span class="sd"> :param index: indexes of the instances to select</span> <div class="viewcode-block" id="APP.total">
<span class="sd"> :return: an instance of :class:`qp.data.LabelledCollection`</span> <a class="viewcode-back" href="../../quapy.html#quapy.protocol.APP.total">[docs]</a>
<span class="sd"> &quot;&quot;&quot;</span> <span class="k">def</span><span class="w"> </span><span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">index</span><span class="p">)</span></div>
<div class="viewcode-block" id="APP.total"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.APP.total">[docs]</a> <span class="k">def</span> <span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the number of samples that will be generated</span> <span class="sd"> Returns the number of samples that will be generated</span>
<span class="sd"> :return: int</span> <span class="sd"> :return: int</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">num_prevalence_combinations</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_prevalences</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">n_classes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">repeats</span><span class="p">)</span></div></div> <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">num_prevalence_combinations</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_prevalences</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">n_classes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">repeats</span><span class="p">)</span></div>
</div>
<div class="viewcode-block" id="NPP"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.NPP">[docs]</a><span class="k">class</span> <span class="nc">NPP</span><span class="p">(</span><span class="n">AbstractStochasticSeededProtocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
<div class="viewcode-block" id="NPP">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.NPP">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">NPP</span><span class="p">(</span><span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> A generator of samples that implements the natural prevalence protocol (NPP). The NPP consists of drawing</span> <span class="sd"> A generator of samples that implements the natural prevalence protocol (NPP). The NPP consists of drawing</span>
<span class="sd"> samples uniformly at random, therefore approximately preserving the natural prevalence of the collection.</span> <span class="sd"> samples uniformly at random, therefore approximately preserving the natural prevalence of the collection.</span>
@ -383,7 +481,7 @@
<span class="sd"> to &quot;labelled_collection&quot; to get instead instances of LabelledCollection</span> <span class="sd"> to &quot;labelled_collection&quot; to get instead instances of LabelledCollection</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span><span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span><span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">return_type</span><span class="o">=</span><span class="s1">&#39;sample_prev&#39;</span><span class="p">):</span> <span class="n">return_type</span><span class="o">=</span><span class="s1">&#39;sample_prev&#39;</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">NPP</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">random_state</span><span class="p">)</span> <span class="nb">super</span><span class="p">(</span><span class="n">NPP</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">random_state</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span>
@ -392,7 +490,9 @@
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
<span class="bp">self</span><span class="o">.</span><span class="n">collator</span> <span class="o">=</span> <span class="n">OnLabelledCollectionProtocol</span><span class="o">.</span><span class="n">get_collator</span><span class="p">(</span><span class="n">return_type</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">collator</span> <span class="o">=</span> <span class="n">OnLabelledCollectionProtocol</span><span class="o">.</span><span class="n">get_collator</span><span class="p">(</span><span class="n">return_type</span><span class="p">)</span>
<div class="viewcode-block" id="NPP.samples_parameters"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.NPP.samples_parameters">[docs]</a> <span class="k">def</span> <span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <div class="viewcode-block" id="NPP.samples_parameters">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.NPP.samples_parameters">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Return all the necessary parameters to replicate the samples as according to the NPP protocol.</span> <span class="sd"> Return all the necessary parameters to replicate the samples as according to the NPP protocol.</span>
@ -404,25 +504,23 @@
<span class="n">indexes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> <span class="n">indexes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">index</span><span class="p">)</span>
<span class="k">return</span> <span class="n">indexes</span></div> <span class="k">return</span> <span class="n">indexes</span></div>
<div class="viewcode-block" id="NPP.sample"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.NPP.sample">[docs]</a> <span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Realizes the sample given the index of the instances.</span>
<span class="sd"> :param index: indexes of the instances to select</span> <div class="viewcode-block" id="NPP.total">
<span class="sd"> :return: an instance of :class:`qp.data.LabelledCollection`</span> <a class="viewcode-back" href="../../quapy.html#quapy.protocol.NPP.total">[docs]</a>
<span class="sd"> &quot;&quot;&quot;</span> <span class="k">def</span><span class="w"> </span><span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">index</span><span class="p">)</span></div>
<div class="viewcode-block" id="NPP.total"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.NPP.total">[docs]</a> <span class="k">def</span> <span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the number of samples that will be generated (equals to &quot;repeats&quot;)</span> <span class="sd"> Returns the number of samples that will be generated (equals to &quot;repeats&quot;)</span>
<span class="sd"> :return: int</span> <span class="sd"> :return: int</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">repeats</span></div></div> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">repeats</span></div>
</div>
<div class="viewcode-block" id="UPP"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.UPP">[docs]</a><span class="k">class</span> <span class="nc">UPP</span><span class="p">(</span><span class="n">AbstractStochasticSeededProtocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
<div class="viewcode-block" id="UPP">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.UPP">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">UPP</span><span class="p">(</span><span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> A variant of :class:`APP` that, instead of using a grid of equidistant prevalence values,</span> <span class="sd"> A variant of :class:`APP` that, instead of using a grid of equidistant prevalence values,</span>
<span class="sd"> relies on the Kraemer algorithm for sampling unit (k-1)-simplex uniformly at random, with</span> <span class="sd"> relies on the Kraemer algorithm for sampling unit (k-1)-simplex uniformly at random, with</span>
@ -441,7 +539,7 @@
<span class="sd"> to &quot;labelled_collection&quot; to get instead instances of LabelledCollection</span> <span class="sd"> to &quot;labelled_collection&quot; to get instead instances of LabelledCollection</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">return_type</span><span class="o">=</span><span class="s1">&#39;sample_prev&#39;</span><span class="p">):</span> <span class="n">return_type</span><span class="o">=</span><span class="s1">&#39;sample_prev&#39;</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">UPP</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">random_state</span><span class="p">)</span> <span class="nb">super</span><span class="p">(</span><span class="n">UPP</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">random_state</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span>
@ -450,7 +548,9 @@
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
<span class="bp">self</span><span class="o">.</span><span class="n">collator</span> <span class="o">=</span> <span class="n">OnLabelledCollectionProtocol</span><span class="o">.</span><span class="n">get_collator</span><span class="p">(</span><span class="n">return_type</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">collator</span> <span class="o">=</span> <span class="n">OnLabelledCollectionProtocol</span><span class="o">.</span><span class="n">get_collator</span><span class="p">(</span><span class="n">return_type</span><span class="p">)</span>
<div class="viewcode-block" id="UPP.samples_parameters"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.UPP.samples_parameters">[docs]</a> <span class="k">def</span> <span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <div class="viewcode-block" id="UPP.samples_parameters">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.UPP.samples_parameters">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Return all the necessary parameters to replicate the samples as according to the UPP protocol.</span> <span class="sd"> Return all the necessary parameters to replicate the samples as according to the UPP protocol.</span>
@ -462,25 +562,96 @@
<span class="n">indexes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">index</span><span class="p">)</span> <span class="n">indexes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">index</span><span class="p">)</span>
<span class="k">return</span> <span class="n">indexes</span></div> <span class="k">return</span> <span class="n">indexes</span></div>
<div class="viewcode-block" id="UPP.sample"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.UPP.sample">[docs]</a> <span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Realizes the sample given the index of the instances.</span>
<span class="sd"> :param index: indexes of the instances to select</span> <div class="viewcode-block" id="UPP.total">
<span class="sd"> :return: an instance of :class:`qp.data.LabelledCollection`</span> <a class="viewcode-back" href="../../quapy.html#quapy.protocol.UPP.total">[docs]</a>
<span class="sd"> &quot;&quot;&quot;</span> <span class="k">def</span><span class="w"> </span><span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">index</span><span class="p">)</span></div>
<div class="viewcode-block" id="UPP.total"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.UPP.total">[docs]</a> <span class="k">def</span> <span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the number of samples that will be generated (equals to &quot;repeats&quot;)</span> <span class="sd"> Returns the number of samples that will be generated (equals to &quot;repeats&quot;)</span>
<span class="sd"> :return: int</span> <span class="sd"> :return: int</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">repeats</span></div></div> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">repeats</span></div>
</div>
<div class="viewcode-block" id="DomainMixer"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.DomainMixer">[docs]</a><span class="k">class</span> <span class="nc">DomainMixer</span><span class="p">(</span><span class="n">AbstractStochasticSeededProtocol</span><span class="p">):</span>
<div class="viewcode-block" id="DirichletProtocol">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.DirichletProtocol">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">DirichletProtocol</span><span class="p">(</span><span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> A protocol that establishes a prior Dirichlet distribution for the prevalence of the samples.</span>
<span class="sd"> Note that providing an all-ones vector of Dirichlet parameters is equivalent to invoking the</span>
<span class="sd"> APP protocol (although each protocol will generate a different series of samples given a</span>
<span class="sd"> fixed seed, since the implementation is different).</span>
<span class="sd"> :param data: a `LabelledCollection` from which the samples will be drawn</span>
<span class="sd"> :param alpha: an array-like of shape (n_classes,) with the parameters of the Dirichlet distribution</span>
<span class="sd"> :param sample_size: integer, the number of instances in each sample; if None (default) then it is taken from</span>
<span class="sd"> qp.environ[&quot;SAMPLE_SIZE&quot;]. If this is not set, a ValueError exception is raised.</span>
<span class="sd"> :param repeats: the number of samples to generate. Default is 100.</span>
<span class="sd"> :param random_state: allows replicating samples across runs (default 0, meaning that the sequence of samples</span>
<span class="sd"> will be the same every time the protocol is called)</span>
<span class="sd"> :param return_type: set to &quot;sample_prev&quot; (default) to get the pairs of (sample, prevalence) at each iteration, or</span>
<span class="sd"> to &quot;labelled_collection&quot; to get instead instances of LabelledCollection</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">alpha</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">return_type</span><span class="o">=</span><span class="s1">&#39;sample_prev&#39;</span><span class="p">):</span>
<span class="c1">#assert ((isinstance(alpha, str) and alpha == &#39;uniform&#39;) or</span>
<span class="c1"># isinstance(alpha, Number) or</span>
<span class="c1"># (isinstance(alpha, Iterable) and all(isinstance(v, Number) for v in alpha))), \</span>
<span class="c1"># f&#39;wrong type for {alpha=}; expected &quot;uniform&quot;, a real scalar, or an array-like of real values&#39;</span>
<span class="n">n_classes</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">n_classes</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">alpha</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="ow">and</span> <span class="n">alpha</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">n_classes</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">alpha</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">n_classes</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="n">alpha</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">alpha</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">1</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span> <span class="o">!=</span> <span class="n">n_classes</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="sa">f</span><span class="s1">&#39;wrong shape for alpha; expected </span><span class="si">{</span><span class="n">n_classes</span><span class="si">}</span><span class="s1"> values, found shape </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s1">&#39;</span>
<span class="p">)</span>
<span class="nb">super</span><span class="p">(</span><span class="n">DirichletProtocol</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">random_state</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span>
<span class="c1">#self.alpha = alpha</span>
<span class="bp">self</span><span class="o">.</span><span class="n">sample_size</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_sample_size</span><span class="p">(</span><span class="n">sample_size</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">repeats</span> <span class="o">=</span> <span class="n">repeats</span>
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
<span class="bp">self</span><span class="o">.</span><span class="n">collator</span> <span class="o">=</span> <span class="n">OnLabelledCollectionProtocol</span><span class="o">.</span><span class="n">get_collator</span><span class="p">(</span><span class="n">return_type</span><span class="p">)</span>
<div class="viewcode-block" id="DirichletProtocol.samples_parameters">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.DirichletProtocol.samples_parameters">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Return all the necessary parameters to replicate the samples.</span>
<span class="sd"> :return: a list of indexes that realize the sampling</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">prevs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">dirichlet</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">repeats</span><span class="p">)</span>
<span class="n">indexes</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">sampling_index</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sample_size</span><span class="p">,</span> <span class="o">*</span><span class="n">prevs_i</span><span class="p">)</span> <span class="k">for</span> <span class="n">prevs_i</span> <span class="ow">in</span> <span class="n">prevs</span><span class="p">]</span>
<span class="k">return</span> <span class="n">indexes</span></div>
<div class="viewcode-block" id="DirichletProtocol.total">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.DirichletProtocol.total">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the number of samples that will be generated (equals to &quot;repeats&quot;)</span>
<span class="sd"> :return: int</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">repeats</span></div>
</div>
<div class="viewcode-block" id="DomainMixer">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.DomainMixer">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">DomainMixer</span><span class="p">(</span><span class="n">AbstractStochasticSeededProtocol</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Generates mixtures of two domains (A and B) at controlled rates, but preserving the original class prevalence.</span> <span class="sd"> Generates mixtures of two domains (A and B) at controlled rates, but preserving the original class prevalence.</span>
@ -489,7 +660,7 @@
<span class="sd"> :param sample_size: integer, the number of instances in each sample; if None (default) then it is taken from</span> <span class="sd"> :param sample_size: integer, the number of instances in each sample; if None (default) then it is taken from</span>
<span class="sd"> qp.environ[&quot;SAMPLE_SIZE&quot;]. If this is not set, a ValueError exception is raised.</span> <span class="sd"> qp.environ[&quot;SAMPLE_SIZE&quot;]. If this is not set, a ValueError exception is raised.</span>
<span class="sd"> :param repeats: int, number of samples to draw for every mixture rate</span> <span class="sd"> :param repeats: int, number of samples to draw for every mixture rate</span>
<span class="sd"> :param prevalence: the prevalence to preserv along the mixtures. If specified, should be an array containing</span> <span class="sd"> :param prevalence: the prevalence to preserve along the mixtures. If specified, should be an array containing</span>
<span class="sd"> one prevalence value (positive float) for each class and summing up to one. If not specified, the prevalence</span> <span class="sd"> one prevalence value (positive float) for each class and summing up to one. If not specified, the prevalence</span>
<span class="sd"> will be taken from the domain A (default).</span> <span class="sd"> will be taken from the domain A (default).</span>
<span class="sd"> :param mixture_points: an integer indicating the number of points to take from a linear scale (e.g., 21 will</span> <span class="sd"> :param mixture_points: an integer indicating the number of points to take from a linear scale (e.g., 21 will</span>
@ -499,7 +670,7 @@
<span class="sd"> will be the same every time the protocol is called)</span> <span class="sd"> will be the same every time the protocol is called)</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span>
<span class="bp">self</span><span class="p">,</span> <span class="bp">self</span><span class="p">,</span>
<span class="n">domainA</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">domainA</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span>
<span class="n">domainB</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">domainB</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span>
@ -531,7 +702,9 @@
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
<span class="bp">self</span><span class="o">.</span><span class="n">collator</span> <span class="o">=</span> <span class="n">OnLabelledCollectionProtocol</span><span class="o">.</span><span class="n">get_collator</span><span class="p">(</span><span class="n">return_type</span><span class="p">)</span> <span class="bp">self</span><span class="o">.</span><span class="n">collator</span> <span class="o">=</span> <span class="n">OnLabelledCollectionProtocol</span><span class="o">.</span><span class="n">get_collator</span><span class="p">(</span><span class="n">return_type</span><span class="p">)</span>
<div class="viewcode-block" id="DomainMixer.samples_parameters"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.DomainMixer.samples_parameters">[docs]</a> <span class="k">def</span> <span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> <div class="viewcode-block" id="DomainMixer.samples_parameters">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.DomainMixer.samples_parameters">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Return all the necessary parameters to replicate the samples as according to the this protocol.</span> <span class="sd"> Return all the necessary parameters to replicate the samples as according to the this protocol.</span>
@ -548,7 +721,10 @@
<span class="n">indexesB</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sampleBidx</span><span class="p">)</span> <span class="n">indexesB</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sampleBidx</span><span class="p">)</span>
<span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">indexesA</span><span class="p">,</span> <span class="n">indexesB</span><span class="p">))</span></div> <span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">indexesA</span><span class="p">,</span> <span class="n">indexesB</span><span class="p">))</span></div>
<div class="viewcode-block" id="DomainMixer.sample"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.DomainMixer.sample">[docs]</a> <span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indexes</span><span class="p">):</span>
<div class="viewcode-block" id="DomainMixer.sample">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.DomainMixer.sample">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indexes</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Realizes the sample given a pair of indexes of the instances from A and B.</span> <span class="sd"> Realizes the sample given a pair of indexes of the instances from A and B.</span>
@ -560,13 +736,18 @@
<span class="n">sampleB</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">B</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">indexesB</span><span class="p">)</span> <span class="n">sampleB</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">B</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">indexesB</span><span class="p">)</span>
<span class="k">return</span> <span class="n">sampleA</span><span class="o">+</span><span class="n">sampleB</span></div> <span class="k">return</span> <span class="n">sampleA</span><span class="o">+</span><span class="n">sampleB</span></div>
<div class="viewcode-block" id="DomainMixer.total"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.DomainMixer.total">[docs]</a> <span class="k">def</span> <span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<div class="viewcode-block" id="DomainMixer.total">
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.DomainMixer.total">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the number of samples that will be generated (equals to &quot;repeats * mixture_points&quot;)</span> <span class="sd"> Returns the number of samples that will be generated (equals to &quot;repeats * mixture_points&quot;)</span>
<span class="sd"> :return: int</span> <span class="sd"> :return: int</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">repeats</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mixture_points</span><span class="p">)</span></div></div> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">repeats</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mixture_points</span><span class="p">)</span></div>
</div>
<span class="c1"># aliases</span> <span class="c1"># aliases</span>

213
docs/build/html/_modules/quapy/util.html vendored
View File

@ -1,23 +1,20 @@
<!DOCTYPE html> <!DOCTYPE html>
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<link rel="stylesheet" type="text/css" href="../../_static/pygments.css" /> <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=b86133f3" />
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<![endif]--> <script src="../../_static/documentation_options.js?v=37f418d5"></script>
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<script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js"></script> <script src="../../_static/sphinx_highlight.js?v=6ffebe34"></script>
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<link rel="search" title="Search" href="../../search.html" /> <link rel="search" title="Search" href="../../search.html" />
@ -43,7 +40,13 @@
</div> </div>
</div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu"> </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
<ul> <ul>
<li class="toctree-l1"><a class="reference internal" href="../../modules.html">quapy</a></li> <li class="toctree-l1"><a class="reference internal" href="../../index.html">Quickstart</a></li>
</ul>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../manuals.html">Manuals</a></li>
</ul>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../../quapy.html">API</a></li>
</ul> </ul>
</div> </div>
@ -71,23 +74,26 @@
<div itemprop="articleBody"> <div itemprop="articleBody">
<h1>Source code for quapy.util</h1><div class="highlight"><pre> <h1>Source code for quapy.util</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">contextlib</span> <span></span><span class="kn">import</span><span class="w"> </span><span class="nn">contextlib</span>
<span class="kn">import</span> <span class="nn">itertools</span> <span class="kn">import</span><span class="w"> </span><span class="nn">itertools</span>
<span class="kn">import</span> <span class="nn">multiprocessing</span> <span class="kn">import</span><span class="w"> </span><span class="nn">multiprocessing</span>
<span class="kn">import</span> <span class="nn">os</span> <span class="kn">import</span><span class="w"> </span><span class="nn">os</span>
<span class="kn">import</span> <span class="nn">pickle</span> <span class="kn">import</span><span class="w"> </span><span class="nn">pickle</span>
<span class="kn">import</span> <span class="nn">urllib</span> <span class="kn">import</span><span class="w"> </span><span class="nn">urllib</span>
<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span> <span class="kn">from</span><span class="w"> </span><span class="nn">pathlib</span><span class="w"> </span><span class="kn">import</span> <span class="n">Path</span>
<span class="kn">from</span> <span class="nn">contextlib</span> <span class="kn">import</span> <span class="n">ExitStack</span> <span class="kn">from</span><span class="w"> </span><span class="nn">contextlib</span><span class="w"> </span><span class="kn">import</span> <span class="n">ExitStack</span>
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
<span class="kn">from</span> <span class="nn">joblib</span> <span class="kn">import</span> <span class="n">Parallel</span><span class="p">,</span> <span class="n">delayed</span>
<span class="kn">from</span> <span class="nn">time</span> <span class="kn">import</span> <span class="n">time</span> <span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
<span class="kn">import</span> <span class="nn">signal</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">joblib</span><span class="w"> </span><span class="kn">import</span> <span class="n">Parallel</span><span class="p">,</span> <span class="n">delayed</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">time</span><span class="w"> </span><span class="kn">import</span> <span class="n">time</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">signal</span>
<span class="k">def</span> <span class="nf">_get_parallel_slices</span><span class="p">(</span><span class="n">n_tasks</span><span class="p">,</span> <span class="n">n_jobs</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">_get_parallel_slices</span><span class="p">(</span><span class="n">n_tasks</span><span class="p">,</span> <span class="n">n_jobs</span><span class="p">):</span>
<span class="k">if</span> <span class="n">n_jobs</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span> <span class="k">if</span> <span class="n">n_jobs</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
<span class="n">n_jobs</span> <span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">cpu_count</span><span class="p">()</span> <span class="n">n_jobs</span> <span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">cpu_count</span><span class="p">()</span>
<span class="n">batch</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">n_tasks</span> <span class="o">/</span> <span class="n">n_jobs</span><span class="p">)</span> <span class="n">batch</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">n_tasks</span> <span class="o">/</span> <span class="n">n_jobs</span><span class="p">)</span>
@ -95,7 +101,9 @@
<span class="k">return</span> <span class="p">[</span><span class="nb">slice</span><span class="p">(</span><span class="n">job</span> <span class="o">*</span> <span class="n">batch</span><span class="p">,</span> <span class="p">(</span><span class="n">job</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">batch</span> <span class="o">+</span> <span class="p">(</span><span class="n">remainder</span> <span class="k">if</span> <span class="n">job</span> <span class="o">==</span> <span class="n">n_jobs</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">else</span> <span class="mi">0</span><span class="p">))</span> <span class="k">for</span> <span class="n">job</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)]</span> <span class="k">return</span> <span class="p">[</span><span class="nb">slice</span><span class="p">(</span><span class="n">job</span> <span class="o">*</span> <span class="n">batch</span><span class="p">,</span> <span class="p">(</span><span class="n">job</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">batch</span> <span class="o">+</span> <span class="p">(</span><span class="n">remainder</span> <span class="k">if</span> <span class="n">job</span> <span class="o">==</span> <span class="n">n_jobs</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">else</span> <span class="mi">0</span><span class="p">))</span> <span class="k">for</span> <span class="n">job</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)]</span>
<div class="viewcode-block" id="map_parallel"><a class="viewcode-back" href="../../quapy.html#quapy.util.map_parallel">[docs]</a><span class="k">def</span> <span class="nf">map_parallel</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">n_jobs</span><span class="p">):</span> <div class="viewcode-block" id="map_parallel">
<a class="viewcode-back" href="../../quapy.html#quapy.util.map_parallel">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">map_parallel</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">n_jobs</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Applies func to n_jobs slices of args. E.g., if args is an array of 99 items and n_jobs=2, then</span> <span class="sd"> Applies func to n_jobs slices of args. E.g., if args is an array of 99 items and n_jobs=2, then</span>
<span class="sd"> func is applied in two parallel processes to args[0:50] and to args[50:99]. func is a function</span> <span class="sd"> func is applied in two parallel processes to args[0:50] and to args[50:99]. func is a function</span>
@ -113,7 +121,10 @@
<span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">chain</span><span class="o">.</span><span class="n">from_iterable</span><span class="p">(</span><span class="n">results</span><span class="p">))</span></div> <span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">chain</span><span class="o">.</span><span class="n">from_iterable</span><span class="p">(</span><span class="n">results</span><span class="p">))</span></div>
<div class="viewcode-block" id="parallel"><a class="viewcode-back" href="../../quapy.html#quapy.util.parallel">[docs]</a><span class="k">def</span> <span class="nf">parallel</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">n_jobs</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">asarray</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s1">&#39;loky&#39;</span><span class="p">):</span>
<div class="viewcode-block" id="parallel">
<a class="viewcode-back" href="../../quapy.html#quapy.util.parallel">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">parallel</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">n_jobs</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">asarray</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s1">&#39;loky&#39;</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> A wrapper of multiprocessing:</span> <span class="sd"> A wrapper of multiprocessing:</span>
@ -129,8 +140,9 @@
<span class="sd"> :param seed: the numeric seed</span> <span class="sd"> :param seed: the numeric seed</span>
<span class="sd"> :param asarray: set to True to return a np.ndarray instead of a list</span> <span class="sd"> :param asarray: set to True to return a np.ndarray instead of a list</span>
<span class="sd"> :param backend: indicates the backend used for handling parallel works</span> <span class="sd"> :param backend: indicates the backend used for handling parallel works</span>
<span class="sd"> :param open_args: if True, then the delayed function is called on *args_i, instead of on args_i</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">func_dec</span><span class="p">(</span><span class="n">environ</span><span class="p">,</span> <span class="n">seed</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">func_dec</span><span class="p">(</span><span class="n">environ</span><span class="p">,</span> <span class="n">seed</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span>
<span class="n">qp</span><span class="o">.</span><span class="n">environ</span> <span class="o">=</span> <span class="n">environ</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span> <span class="n">qp</span><span class="o">.</span><span class="n">environ</span> <span class="o">=</span> <span class="n">environ</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;N_JOBS&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span> <span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;N_JOBS&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
<span class="c1">#set a context with a temporal seed to ensure results are reproducibles in parallel</span> <span class="c1">#set a context with a temporal seed to ensure results are reproducibles in parallel</span>
@ -147,8 +159,48 @@
<span class="k">return</span> <span class="n">out</span></div> <span class="k">return</span> <span class="n">out</span></div>
<div class="viewcode-block" id="temp_seed"><a class="viewcode-back" href="../../quapy.html#quapy.util.temp_seed">[docs]</a><span class="nd">@contextlib</span><span class="o">.</span><span class="n">contextmanager</span>
<span class="k">def</span> <span class="nf">temp_seed</span><span class="p">(</span><span class="n">random_state</span><span class="p">):</span> <div class="viewcode-block" id="parallel_unpack">
<a class="viewcode-back" href="../../quapy.html#quapy.util.parallel_unpack">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">parallel_unpack</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">n_jobs</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">asarray</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s1">&#39;loky&#39;</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> A wrapper of multiprocessing:</span>
<span class="sd"> &gt;&gt;&gt; Parallel(n_jobs=n_jobs)(</span>
<span class="sd"> &gt;&gt;&gt; delayed(func)(*args_i) for args_i in args</span>
<span class="sd"> &gt;&gt;&gt; )</span>
<span class="sd"> that takes the `quapy.environ` variable as input silently.</span>
<span class="sd"> Seeds the child processes to ensure reproducibility when n_jobs&gt;1.</span>
<span class="sd"> :param func: callable</span>
<span class="sd"> :param args: args of func</span>
<span class="sd"> :param seed: the numeric seed</span>
<span class="sd"> :param asarray: set to True to return a np.ndarray instead of a list</span>
<span class="sd"> :param backend: indicates the backend used for handling parallel works</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span><span class="w"> </span><span class="nf">func_dec</span><span class="p">(</span><span class="n">environ</span><span class="p">,</span> <span class="n">seed</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span>
<span class="n">qp</span><span class="o">.</span><span class="n">environ</span> <span class="o">=</span> <span class="n">environ</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;N_JOBS&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
<span class="c1"># set a context with a temporal seed to ensure results are reproducibles in parallel</span>
<span class="k">with</span> <span class="n">ExitStack</span><span class="p">()</span> <span class="k">as</span> <span class="n">stack</span><span class="p">:</span>
<span class="k">if</span> <span class="n">seed</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">stack</span><span class="o">.</span><span class="n">enter_context</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">))</span>
<span class="k">return</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">Parallel</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="n">backend</span><span class="p">)(</span>
<span class="n">delayed</span><span class="p">(</span><span class="n">func_dec</span><span class="p">)(</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">,</span> <span class="kc">None</span> <span class="k">if</span> <span class="n">seed</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">seed</span> <span class="o">+</span> <span class="n">i</span><span class="p">,</span> <span class="o">*</span><span class="n">args_i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">args_i</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">args</span><span class="p">)</span>
<span class="p">)</span>
<span class="k">if</span> <span class="n">asarray</span><span class="p">:</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
<span class="k">return</span> <span class="n">out</span></div>
<div class="viewcode-block" id="temp_seed">
<a class="viewcode-back" href="../../quapy.html#quapy.util.temp_seed">[docs]</a>
<span class="nd">@contextlib</span><span class="o">.</span><span class="n">contextmanager</span>
<span class="k">def</span><span class="w"> </span><span class="nf">temp_seed</span><span class="p">(</span><span class="n">random_state</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Can be used in a &quot;with&quot; context to set a temporal seed without modifying the outer numpy&#39;s current state. E.g.:</span> <span class="sd"> Can be used in a &quot;with&quot; context to set a temporal seed without modifying the outer numpy&#39;s current state. E.g.:</span>
@ -169,14 +221,17 @@
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">set_state</span><span class="p">(</span><span class="n">state</span><span class="p">)</span></div> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">set_state</span><span class="p">(</span><span class="n">state</span><span class="p">)</span></div>
<div class="viewcode-block" id="download_file"><a class="viewcode-back" href="../../quapy.html#quapy.util.download_file">[docs]</a><span class="k">def</span> <span class="nf">download_file</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">archive_filename</span><span class="p">):</span>
<div class="viewcode-block" id="download_file">
<a class="viewcode-back" href="../../quapy.html#quapy.util.download_file">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">download_file</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">archive_filename</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Downloads a file from a url</span> <span class="sd"> Downloads a file from a url</span>
<span class="sd"> :param url: the url</span> <span class="sd"> :param url: the url</span>
<span class="sd"> :param archive_filename: destination filename</span> <span class="sd"> :param archive_filename: destination filename</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="nf">progress</span><span class="p">(</span><span class="n">blocknum</span><span class="p">,</span> <span class="n">bs</span><span class="p">,</span> <span class="n">size</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">progress</span><span class="p">(</span><span class="n">blocknum</span><span class="p">,</span> <span class="n">bs</span><span class="p">,</span> <span class="n">size</span><span class="p">):</span>
<span class="n">total_sz_mb</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">%.2f</span><span class="s1"> MB&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">size</span> <span class="o">/</span> <span class="mf">1e6</span><span class="p">)</span> <span class="n">total_sz_mb</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">%.2f</span><span class="s1"> MB&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">size</span> <span class="o">/</span> <span class="mf">1e6</span><span class="p">)</span>
<span class="n">current_sz_mb</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">%.2f</span><span class="s1"> MB&#39;</span> <span class="o">%</span> <span class="p">((</span><span class="n">blocknum</span> <span class="o">*</span> <span class="n">bs</span><span class="p">)</span> <span class="o">/</span> <span class="mf">1e6</span><span class="p">)</span> <span class="n">current_sz_mb</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">%.2f</span><span class="s1"> MB&#39;</span> <span class="o">%</span> <span class="p">((</span><span class="n">blocknum</span> <span class="o">*</span> <span class="n">bs</span><span class="p">)</span> <span class="o">/</span> <span class="mf">1e6</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\r</span><span class="s1">downloaded </span><span class="si">%s</span><span class="s1"> / </span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">current_sz_mb</span><span class="p">,</span> <span class="n">total_sz_mb</span><span class="p">),</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span> <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\r</span><span class="s1">downloaded </span><span class="si">%s</span><span class="s1"> / </span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">current_sz_mb</span><span class="p">,</span> <span class="n">total_sz_mb</span><span class="p">),</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
@ -185,7 +240,10 @@
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span></div> <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span></div>
<div class="viewcode-block" id="download_file_if_not_exists"><a class="viewcode-back" href="../../quapy.html#quapy.util.download_file_if_not_exists">[docs]</a><span class="k">def</span> <span class="nf">download_file_if_not_exists</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">archive_filename</span><span class="p">):</span>
<div class="viewcode-block" id="download_file_if_not_exists">
<a class="viewcode-back" href="../../quapy.html#quapy.util.download_file_if_not_exists">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">download_file_if_not_exists</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">archive_filename</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Dowloads a function (using :meth:`download_file`) if the file does not exist.</span> <span class="sd"> Dowloads a function (using :meth:`download_file`) if the file does not exist.</span>
@ -198,7 +256,10 @@
<span class="n">download_file</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">archive_filename</span><span class="p">)</span></div> <span class="n">download_file</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">archive_filename</span><span class="p">)</span></div>
<div class="viewcode-block" id="create_if_not_exist"><a class="viewcode-back" href="../../quapy.html#quapy.util.create_if_not_exist">[docs]</a><span class="k">def</span> <span class="nf">create_if_not_exist</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
<div class="viewcode-block" id="create_if_not_exist">
<a class="viewcode-back" href="../../quapy.html#quapy.util.create_if_not_exist">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">create_if_not_exist</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> An alias to `os.makedirs(path, exist_ok=True)` that also returns the path. This is useful in cases like, e.g.:</span> <span class="sd"> An alias to `os.makedirs(path, exist_ok=True)` that also returns the path. This is useful in cases like, e.g.:</span>
@ -211,7 +272,10 @@
<span class="k">return</span> <span class="n">path</span></div> <span class="k">return</span> <span class="n">path</span></div>
<div class="viewcode-block" id="get_quapy_home"><a class="viewcode-back" href="../../quapy.html#quapy.util.get_quapy_home">[docs]</a><span class="k">def</span> <span class="nf">get_quapy_home</span><span class="p">():</span>
<div class="viewcode-block" id="get_quapy_home">
<a class="viewcode-back" href="../../quapy.html#quapy.util.get_quapy_home">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">get_quapy_home</span><span class="p">():</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Gets the home directory of QuaPy, i.e., the directory where QuaPy saves permanent data, such as dowloaded datasets.</span> <span class="sd"> Gets the home directory of QuaPy, i.e., the directory where QuaPy saves permanent data, such as dowloaded datasets.</span>
<span class="sd"> This directory is `~/quapy_data`</span> <span class="sd"> This directory is `~/quapy_data`</span>
@ -223,7 +287,10 @@
<span class="k">return</span> <span class="n">home</span></div> <span class="k">return</span> <span class="n">home</span></div>
<div class="viewcode-block" id="create_parent_dir"><a class="viewcode-back" href="../../quapy.html#quapy.util.create_parent_dir">[docs]</a><span class="k">def</span> <span class="nf">create_parent_dir</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
<div class="viewcode-block" id="create_parent_dir">
<a class="viewcode-back" href="../../quapy.html#quapy.util.create_parent_dir">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">create_parent_dir</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Creates the parent dir (if any) of a given path, if not exists. E.g., for `./path/to/file.txt`, the path `./path/to`</span> <span class="sd"> Creates the parent dir (if any) of a given path, if not exists. E.g., for `./path/to/file.txt`, the path `./path/to`</span>
<span class="sd"> is created.</span> <span class="sd"> is created.</span>
@ -235,7 +302,10 @@
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">parentdir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div> <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">parentdir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>
<div class="viewcode-block" id="save_text_file"><a class="viewcode-back" href="../../quapy.html#quapy.util.save_text_file">[docs]</a><span class="k">def</span> <span class="nf">save_text_file</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">text</span><span class="p">):</span>
<div class="viewcode-block" id="save_text_file">
<a class="viewcode-back" href="../../quapy.html#quapy.util.save_text_file">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">save_text_file</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">text</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Saves a text file to disk, given its full path, and creates the parent directory if missing.</span> <span class="sd"> Saves a text file to disk, given its full path, and creates the parent directory if missing.</span>
@ -243,11 +313,14 @@
<span class="sd"> :param text: text to save.</span> <span class="sd"> :param text: text to save.</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="n">create_parent_dir</span><span class="p">(</span><span class="n">path</span><span class="p">)</span> <span class="n">create_parent_dir</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">text</span><span class="p">,</span> <span class="s1">&#39;wt&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fout</span><span class="p">:</span> <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;wt&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fout</span><span class="p">:</span>
<span class="n">fout</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">text</span><span class="p">)</span></div> <span class="n">fout</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">text</span><span class="p">)</span></div>
<div class="viewcode-block" id="pickled_resource"><a class="viewcode-back" href="../../quapy.html#quapy.util.pickled_resource">[docs]</a><span class="k">def</span> <span class="nf">pickled_resource</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">:</span><span class="nb">str</span><span class="p">,</span> <span class="n">generation_func</span><span class="p">:</span><span class="n">callable</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span>
<div class="viewcode-block" id="pickled_resource">
<a class="viewcode-back" href="../../quapy.html#quapy.util.pickled_resource">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">pickled_resource</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">:</span><span class="nb">str</span><span class="p">,</span> <span class="n">generation_func</span><span class="p">:</span><span class="nb">callable</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Allows for fast reuse of resources that are generated only once by calling generation_func(\\*args). The next times</span> <span class="sd"> Allows for fast reuse of resources that are generated only once by calling generation_func(\\*args). The next times</span>
<span class="sd"> this function is invoked, it loads the pickled resource. Example:</span> <span class="sd"> this function is invoked, it loads the pickled resource. Example:</span>
@ -266,15 +339,18 @@
<span class="k">return</span> <span class="n">generation_func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span> <span class="k">return</span> <span class="n">generation_func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span> <span class="k">else</span><span class="p">:</span>
<span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">):</span> <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">):</span>
<span class="k">return</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">))</span> <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fin</span><span class="p">:</span>
<span class="n">instance</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fin</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span> <span class="k">else</span><span class="p">:</span>
<span class="n">instance</span> <span class="o">=</span> <span class="n">generation_func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span> <span class="n">instance</span> <span class="o">=</span> <span class="n">generation_func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span>
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">Path</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">)</span><span class="o">.</span><span class="n">parent</span><span class="p">),</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">Path</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">)</span><span class="o">.</span><span class="n">parent</span><span class="p">),</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">instance</span><span class="p">,</span> <span class="nb">open</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">,</span> <span class="s1">&#39;wb&#39;</span><span class="p">),</span> <span class="n">pickle</span><span class="o">.</span><span class="n">HIGHEST_PROTOCOL</span><span class="p">)</span> <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">,</span> <span class="s1">&#39;wb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">foo</span><span class="p">:</span>
<span class="k">return</span> <span class="n">instance</span></div> <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">instance</span><span class="p">,</span> <span class="n">foo</span><span class="p">,</span> <span class="n">pickle</span><span class="o">.</span><span class="n">HIGHEST_PROTOCOL</span><span class="p">)</span>
<span class="k">return</span> <span class="n">instance</span></div>
<span class="k">def</span> <span class="nf">_check_sample_size</span><span class="p">(</span><span class="n">sample_size</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_check_sample_size</span><span class="p">(</span><span class="n">sample_size</span><span class="p">):</span>
<span class="k">if</span> <span class="n">sample_size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="k">if</span> <span class="n">sample_size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">assert</span> <span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;SAMPLE_SIZE&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> \ <span class="k">assert</span> <span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;SAMPLE_SIZE&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> \
<span class="s1">&#39;error: sample_size set to None, and cannot be resolved from the environment&#39;</span> <span class="s1">&#39;error: sample_size set to None, and cannot be resolved from the environment&#39;</span>
@ -284,7 +360,34 @@
<span class="k">return</span> <span class="n">sample_size</span> <span class="k">return</span> <span class="n">sample_size</span>
<div class="viewcode-block" id="EarlyStop"><a class="viewcode-back" href="../../quapy.html#quapy.util.EarlyStop">[docs]</a><span class="k">class</span> <span class="nc">EarlyStop</span><span class="p">:</span> <div class="viewcode-block" id="load_report">
<a class="viewcode-back" href="../../quapy.html#quapy.util.load_report">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">load_report</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">as_dict</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="k">def</span><span class="w"> </span><span class="nf">str2prev_arr</span><span class="p">(</span><span class="n">strprev</span><span class="p">):</span>
<span class="n">within</span> <span class="o">=</span> <span class="n">strprev</span><span class="o">.</span><span class="n">strip</span><span class="p">(</span><span class="s1">&#39;[]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
<span class="n">float_list</span> <span class="o">=</span> <span class="p">[</span><span class="nb">float</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">within</span><span class="p">]</span>
<span class="n">float_list</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.</span> <span class="o">-</span> <span class="nb">sum</span><span class="p">(</span><span class="n">float_list</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">float_list</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">df</span><span class="p">[</span><span class="s1">&#39;true-prev&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;true-prev&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">str2prev_arr</span><span class="p">)</span>
<span class="n">df</span><span class="p">[</span><span class="s1">&#39;estim-prev&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;estim-prev&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">str2prev_arr</span><span class="p">)</span>
<span class="k">if</span> <span class="n">as_dict</span><span class="p">:</span>
<span class="n">d</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="p">:</span>
<span class="n">vals</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
<span class="k">if</span> <span class="n">col</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;true-prev&#39;</span><span class="p">,</span> <span class="s1">&#39;estim-prev&#39;</span><span class="p">]:</span>
<span class="n">vals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span><span class="n">vals</span><span class="p">)</span>
<span class="n">d</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">vals</span>
<span class="k">return</span> <span class="n">d</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">df</span></div>
<div class="viewcode-block" id="EarlyStop">
<a class="viewcode-back" href="../../quapy.html#quapy.util.EarlyStop">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">EarlyStop</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> A class implementing the early-stopping condition typically used for training neural networks.</span> <span class="sd"> A class implementing the early-stopping condition typically used for training neural networks.</span>
@ -309,7 +412,7 @@
<span class="sd"> :ivar IMPROVED: flag (boolean) indicating whether there was an improvement in the last call</span> <span class="sd"> :ivar IMPROVED: flag (boolean) indicating whether there was an improvement in the last call</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">patience</span><span class="p">,</span> <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">patience</span><span class="p">,</span> <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">PATIENCE_LIMIT</span> <span class="o">=</span> <span class="n">patience</span> <span class="bp">self</span><span class="o">.</span><span class="n">PATIENCE_LIMIT</span> <span class="o">=</span> <span class="n">patience</span>
<span class="bp">self</span><span class="o">.</span><span class="n">better</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">a</span><span class="p">,</span><span class="n">b</span><span class="p">:</span> <span class="n">a</span><span class="o">&lt;</span><span class="n">b</span> <span class="k">if</span> <span class="n">lower_is_better</span> <span class="k">else</span> <span class="n">a</span><span class="o">&gt;</span><span class="n">b</span> <span class="bp">self</span><span class="o">.</span><span class="n">better</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">a</span><span class="p">,</span><span class="n">b</span><span class="p">:</span> <span class="n">a</span><span class="o">&lt;</span><span class="n">b</span> <span class="k">if</span> <span class="n">lower_is_better</span> <span class="k">else</span> <span class="n">a</span><span class="o">&gt;</span><span class="n">b</span>
@ -319,7 +422,7 @@
<span class="bp">self</span><span class="o">.</span><span class="n">STOP</span> <span class="o">=</span> <span class="kc">False</span> <span class="bp">self</span><span class="o">.</span><span class="n">STOP</span> <span class="o">=</span> <span class="kc">False</span>
<span class="bp">self</span><span class="o">.</span><span class="n">IMPROVED</span> <span class="o">=</span> <span class="kc">False</span> <span class="bp">self</span><span class="o">.</span><span class="n">IMPROVED</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">watch_score</span><span class="p">,</span> <span class="n">epoch</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">watch_score</span><span class="p">,</span> <span class="n">epoch</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Commits the new score found in epoch `epoch`. If the score improves over the best score found so far, then</span> <span class="sd"> Commits the new score found in epoch `epoch`. If the score improves over the best score found so far, then</span>
<span class="sd"> the patiente counter gets reset. If otherwise, the patience counter is decreased, and in case it reachs 0,</span> <span class="sd"> the patiente counter gets reset. If otherwise, the patience counter is decreased, and in case it reachs 0,</span>
@ -339,8 +442,11 @@
<span class="bp">self</span><span class="o">.</span><span class="n">STOP</span> <span class="o">=</span> <span class="kc">True</span></div> <span class="bp">self</span><span class="o">.</span><span class="n">STOP</span> <span class="o">=</span> <span class="kc">True</span></div>
<div class="viewcode-block" id="timeout"><a class="viewcode-back" href="../../quapy.html#quapy.util.timeout">[docs]</a><span class="nd">@contextlib</span><span class="o">.</span><span class="n">contextmanager</span>
<span class="k">def</span> <span class="nf">timeout</span><span class="p">(</span><span class="n">seconds</span><span class="p">):</span> <div class="viewcode-block" id="timeout">
<a class="viewcode-back" href="../../quapy.html#quapy.util.timeout">[docs]</a>
<span class="nd">@contextlib</span><span class="o">.</span><span class="n">contextmanager</span>
<span class="k">def</span><span class="w"> </span><span class="nf">timeout</span><span class="p">(</span><span class="n">seconds</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span> <span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Opens a context that will launch an exception if not closed after a given number of seconds</span> <span class="sd"> Opens a context that will launch an exception if not closed after a given number of seconds</span>
@ -359,7 +465,7 @@
<span class="sd"> :param seconds: number of seconds, set to &lt;=0 to ignore the timer</span> <span class="sd"> :param seconds: number of seconds, set to &lt;=0 to ignore the timer</span>
<span class="sd"> &quot;&quot;&quot;</span> <span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">seconds</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span> <span class="k">if</span> <span class="n">seconds</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">handler</span><span class="p">(</span><span class="n">signum</span><span class="p">,</span> <span class="n">frame</span><span class="p">):</span> <span class="k">def</span><span class="w"> </span><span class="nf">handler</span><span class="p">(</span><span class="n">signum</span><span class="p">,</span> <span class="n">frame</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">TimeoutError</span><span class="p">()</span> <span class="k">raise</span> <span class="ne">TimeoutError</span><span class="p">()</span>
<span class="n">signal</span><span class="o">.</span><span class="n">signal</span><span class="p">(</span><span class="n">signal</span><span class="o">.</span><span class="n">SIGALRM</span><span class="p">,</span> <span class="n">handler</span><span class="p">)</span> <span class="n">signal</span><span class="o">.</span><span class="n">signal</span><span class="p">(</span><span class="n">signal</span><span class="o">.</span><span class="n">SIGALRM</span><span class="p">,</span> <span class="n">handler</span><span class="p">)</span>
@ -370,6 +476,7 @@
<span class="k">if</span> <span class="n">seconds</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span> <span class="k">if</span> <span class="n">seconds</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">signal</span><span class="o">.</span><span class="n">alarm</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span></div> <span class="n">signal</span><span class="o">.</span><span class="n">alarm</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span></div>
</pre></div> </pre></div>
</div> </div>

17
docs/build/html/_static/_sphinx_javascript_frameworks_compat.js vendored
View File

@ -1,20 +1,9 @@
/* /* Compatability shim for jQuery and underscores.js.
* _sphinx_javascript_frameworks_compat.js
* ~~~~~~~~~~
*
* Compatability shim for jQuery and underscores.js.
*
* WILL BE REMOVED IN Sphinx 6.0
* xref RemovedInSphinx60Warning
* *
* Copyright Sphinx contributors
* Released under the two clause BSD licence
*/ */
/**
* select a different prefix for underscore
*/
$u = _.noConflict();
/** /**
* small helper function to urldecode strings * small helper function to urldecode strings
* *

2
docs/build/html/_static/css/badge_only.css vendored
View File

@ -1 +1 @@
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a{display:inline-block;padding:6px;color:#fcfcfc}.rst-versions .rst-other-versions .rtd-current-item{font-weight:700}.rst-versions.rst-badge{width:auto;bottom:20px;right:20px;left:auto;border:none;max-width:300px;max-height:90%}.rst-versions.rst-badge .fa-book,.rst-versions.rst-badge .icon-book{float:none;line-height:30px}.rst-versions.rst-badge.shift-up .rst-current-version{text-align:right}.rst-versions.rst-badge.shift-up .rst-current-version .fa-book,.rst-versions.rst-badge.shift-up .rst-current-version .icon-book{float:left}.rst-versions.rst-badge>.rst-current-version{width:auto;height:30px;line-height:30px;padding:0 6px;display:block;text-align:center}@media screen and (max-width:768px){.rst-versions{width:85%;display:none}.rst-versions.shift{display:block}}#flyout-search-form{padding:6px}

2
docs/build/html/_static/css/theme.css vendored
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File diff suppressed because one or more lines are too long

81
docs/build/html/_static/sphinx_highlight.js vendored
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@ -1,7 +1,7 @@
/* Highlighting utilities for Sphinx HTML documentation. */ /* Highlighting utilities for Sphinx HTML documentation. */
"use strict"; "use strict";
const SPHINX_HIGHLIGHT_ENABLED = true const SPHINX_HIGHLIGHT_ENABLED = true;
/** /**
* highlight a given string on a node by wrapping it in * highlight a given string on a node by wrapping it in
@ -13,9 +13,9 @@ const _highlight = (node, addItems, text, className) => {
const parent = node.parentNode; const parent = node.parentNode;
const pos = val.toLowerCase().indexOf(text); const pos = val.toLowerCase().indexOf(text);
if ( if (
pos >= 0 && pos >= 0
!parent.classList.contains(className) && && !parent.classList.contains(className)
!parent.classList.contains("nohighlight") && !parent.classList.contains("nohighlight")
) { ) {
let span; let span;
@ -29,19 +29,18 @@ const _highlight = (node, addItems, text, className) => {
} }
span.appendChild(document.createTextNode(val.substr(pos, text.length))); span.appendChild(document.createTextNode(val.substr(pos, text.length)));
parent.insertBefore( const rest = document.createTextNode(val.substr(pos + text.length));
span, parent.insertBefore(span, parent.insertBefore(rest, node.nextSibling));
parent.insertBefore(
document.createTextNode(val.substr(pos + text.length)),
node.nextSibling
)
);
node.nodeValue = val.substr(0, pos); node.nodeValue = val.substr(0, pos);
/* There may be more occurrences of search term in this node. So call this
* function recursively on the remaining fragment.
*/
_highlight(rest, addItems, text, className);
if (isInSVG) { if (isInSVG) {
const rect = document.createElementNS( const rect = document.createElementNS(
"http://www.w3.org/2000/svg", "http://www.w3.org/2000/svg",
"rect" "rect",
); );
const bbox = parent.getBBox(); const bbox = parent.getBBox();
rect.x.baseVal.value = bbox.x; rect.x.baseVal.value = bbox.x;
@ -60,7 +59,7 @@ const _highlightText = (thisNode, text, className) => {
let addItems = []; let addItems = [];
_highlight(thisNode, addItems, text, className); _highlight(thisNode, addItems, text, className);
addItems.forEach((obj) => addItems.forEach((obj) =>
obj.parent.insertAdjacentElement("beforebegin", obj.target) obj.parent.insertAdjacentElement("beforebegin", obj.target),
); );
}; };
@ -68,25 +67,31 @@ const _highlightText = (thisNode, text, className) => {
* Small JavaScript module for the documentation. * Small JavaScript module for the documentation.
*/ */
const SphinxHighlight = { const SphinxHighlight = {
/** /**
* highlight the search words provided in localstorage in the text * highlight the search words provided in localstorage in the text
*/ */
highlightSearchWords: () => { highlightSearchWords: () => {
if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight
// get and clear terms from localstorage // get and clear terms from localstorage
const url = new URL(window.location); const url = new URL(window.location);
const highlight = const highlight =
localStorage.getItem("sphinx_highlight_terms") localStorage.getItem("sphinx_highlight_terms")
|| url.searchParams.get("highlight") || url.searchParams.get("highlight")
|| ""; || "";
localStorage.removeItem("sphinx_highlight_terms") localStorage.removeItem("sphinx_highlight_terms");
url.searchParams.delete("highlight"); // Update history only if '?highlight' is present; otherwise it
window.history.replaceState({}, "", url); // clears text fragments (not set in window.location by the browser)
if (url.searchParams.has("highlight")) {
url.searchParams.delete("highlight");
window.history.replaceState({}, "", url);
}
// get individual terms from highlight string // get individual terms from highlight string
const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); const terms = highlight
.toLowerCase()
.split(/\s+/)
.filter((x) => x);
if (terms.length === 0) return; // nothing to do if (terms.length === 0) return; // nothing to do
// There should never be more than one element matching "div.body" // There should never be more than one element matching "div.body"
@ -102,11 +107,11 @@ const SphinxHighlight = {
document document
.createRange() .createRange()
.createContextualFragment( .createContextualFragment(
'<p class="highlight-link">' + '<p class="highlight-link">'
'<a href="javascript:SphinxHighlight.hideSearchWords()">' + + '<a href="javascript:SphinxHighlight.hideSearchWords()">'
_("Hide Search Matches") + + _("Hide Search Matches")
"</a></p>" + "</a></p>",
) ),
); );
}, },
@ -120,7 +125,7 @@ const SphinxHighlight = {
document document
.querySelectorAll("span.highlighted") .querySelectorAll("span.highlighted")
.forEach((el) => el.classList.remove("highlighted")); .forEach((el) => el.classList.remove("highlighted"));
localStorage.removeItem("sphinx_highlight_terms") localStorage.removeItem("sphinx_highlight_terms");
}, },
initEscapeListener: () => { initEscapeListener: () => {
@ -129,10 +134,15 @@ const SphinxHighlight = {
document.addEventListener("keydown", (event) => { document.addEventListener("keydown", (event) => {
// bail for input elements // bail for input elements
if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName))
return;
// bail with special keys // bail with special keys
if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey)
if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { return;
if (
DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
&& event.key === "Escape"
) {
SphinxHighlight.hideSearchWords(); SphinxHighlight.hideSearchWords();
event.preventDefault(); event.preventDefault();
} }
@ -140,5 +150,10 @@ const SphinxHighlight = {
}, },
}; };
_ready(SphinxHighlight.highlightSearchWords); _ready(() => {
_ready(SphinxHighlight.initEscapeListener); /* Do not call highlightSearchWords() when we are on the search page.
* It will highlight words from the *previous* search query.
*/
if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
SphinxHighlight.initEscapeListener();
});

3
docs/source/conf.py
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@ -61,7 +61,8 @@ exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
# -- Options for HTML output ------------------------------------------------- # -- Options for HTML output -------------------------------------------------
# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
html_theme = 'sphinx_rtd_theme' #html_theme = 'sphinx_rtd_theme'
html_theme = 'pydata_sphinx_theme'
# html_theme = 'furo' # html_theme = 'furo'
# need to be installed: pip install furo (not working...) # need to be installed: pip install furo (not working...)
# html_static_path = ['_static'] # html_static_path = ['_static']

9
quapy/error.py
View File

@ -177,6 +177,11 @@ def msre(prevs_true, prevs_hat, prevs_train, eps=0.):
def aitchisondist(prevs_true, prevs_hat): def aitchisondist(prevs_true, prevs_hat):
""" """
Computes the Aitchison distance between two prevalence vectors. Computes the Aitchison distance between two prevalence vectors.
The Aitchison distance between prevalence vectors :math:`p` and
:math:`\\hat{p}` is computed as
:math:`d_A(p,\\hat{p})=\\|\\mathrm{clr}(p)-\\mathrm{clr}(\\hat{p})\\|_2`,
where :math:`\\mathrm{clr}(p)_i=\\log p_i-\\frac{1}{|\\mathcal{Y}|}
\\sum_{j \\in \\mathcal{Y}} \\log p_j`.
:param prevs_true: array-like with the true prevalence values :param prevs_true: array-like with the true prevalence values
:param prevs_hat: array-like with the predicted prevalence values :param prevs_hat: array-like with the predicted prevalence values
@ -191,7 +196,9 @@ def aitchisondist(prevs_true, prevs_hat):
def maitchisondist(prevs_true, prevs_hat): def maitchisondist(prevs_true, prevs_hat):
""" """
Computes the mean Aitchison distance (see :meth:`quapy.error.aitchisondist`) Computes the mean Aitchison distance (see :meth:`quapy.error.aitchisondist`)
across the sample pairs. across the sample pairs, i.e.,
:math:`\\mathrm{mAitchisonDist}=\\frac{1}{n}\\sum_{i=1}^n
d_A(p_i,\\hat{p}_i)`.
:param prevs_true: array-like with the true prevalence values :param prevs_true: array-like with the true prevalence values
:param prevs_hat: array-like with the predicted prevalence values :param prevs_hat: array-like with the predicted prevalence values

31
quapy/functional.py
View File

@ -430,7 +430,7 @@ def argmin_prevalence(loss: Callable,
:param method: string indicating the search strategy. Possible values are:: :param method: string indicating the search strategy. Possible values are::
'optim_minimize': uses scipy.optim 'optim_minimize': uses scipy.optim
'linear_search': carries out a linear search for binary problems in the space [0, 0.01, 0.02, ..., 1] 'linear_search': carries out a linear search for binary problems in the space [0, 0.01, 0.02, ..., 1]
'ternary_search': implements the ternary search (not yet implemented) 'ternary_search': carries out a ternary search for binary problems in the interval [0,1]
:return: np.ndarray, a prevalence vector :return: np.ndarray, a prevalence vector
""" """
if method == 'optim_minimize': if method == 'optim_minimize':
@ -438,7 +438,7 @@ def argmin_prevalence(loss: Callable,
elif method == 'linear_search': elif method == 'linear_search':
return linear_search(loss, n_classes) return linear_search(loss, n_classes)
elif method == 'ternary_search': elif method == 'ternary_search':
ternary_search(loss, n_classes) return ternary_search(loss, n_classes)
else: else:
raise NotImplementedError() raise NotImplementedError()
@ -493,7 +493,32 @@ def linear_search(loss: Callable, n_classes: int):
def ternary_search(loss: Callable, n_classes: int): def ternary_search(loss: Callable, n_classes: int):
raise NotImplementedError() """
Performs a ternary search for the best prevalence value in binary problems.
This search assumes the loss is unimodal over the interval [0,1].
:param loss: (callable) the function to minimize
:param n_classes: (int) the number of classes, i.e., the dimensionality of the prevalence vector
:return: (ndarray) the best prevalence vector found
"""
assert n_classes == 2, 'ternary search is only available for binary problems'
left, right = 0., 1.
tol = 1e-5
while abs(right - left) >= tol:
left_third = left + (right - left) / 3
right_third = right - (right - left) / 3
left_loss = loss(np.asarray([1 - left_third, left_third]))
right_loss = loss(np.asarray([1 - right_third, right_third]))
if left_loss < right_loss:
right = right_third
else:
left = left_third
prev = (left + right) / 2
return np.asarray([1 - prev, prev])
# ------------------------------------------------------------------------------------------ # ------------------------------------------------------------------------------------------

3
quapy/method/__init__.py
View File

@ -15,6 +15,7 @@ AGGREGATIVE_METHODS = {
aggregative.ACC, aggregative.ACC,
aggregative.PCC, aggregative.PCC,
aggregative.PACC, aggregative.PACC,
aggregative.RLLS,
aggregative.EMQ, aggregative.EMQ,
aggregative.HDy, aggregative.HDy,
aggregative.DyS, aggregative.DyS,
@ -52,6 +53,7 @@ MULTICLASS_METHODS = {
aggregative.ACC, aggregative.ACC,
aggregative.PCC, aggregative.PCC,
aggregative.PACC, aggregative.PACC,
aggregative.RLLS,
aggregative.EMQ, aggregative.EMQ,
aggregative.KDEyML, aggregative.KDEyML,
aggregative.KDEyCS, aggregative.KDEyCS,
@ -75,4 +77,3 @@ QUANTIFICATION_METHODS = AGGREGATIVE_METHODS | NON_AGGREGATIVE_METHODS | META_ME

7
quapy/method/_kdey.py
View File

@ -4,6 +4,7 @@ from sklearn.base import BaseEstimator
from sklearn.neighbors import KernelDensity from sklearn.neighbors import KernelDensity
import quapy as qp import quapy as qp
from quapy.method._helper import _labels_to_indices
from quapy.method.aggregative import AggregativeSoftQuantifier from quapy.method.aggregative import AggregativeSoftQuantifier
import quapy.functional as F import quapy.functional as F
from scipy.special import logsumexp from scipy.special import logsumexp
@ -374,13 +375,11 @@ class KDEyCS(AggregativeSoftQuantifier):
P, y = classif_predictions, labels P, y = classif_predictions, labels
n = len(self.classes_) n = len(self.classes_)
y = _labels_to_indices(y, self.classes_)
assert all(sorted(np.unique(y)) == np.arange(n)), \
'label name gaps not allowed in current implementation'
# counts_inv keeps track of the relative weight of each datapoint within its class # counts_inv keeps track of the relative weight of each datapoint within its class
# (i.e., the weight in its KDE model) # (i.e., the weight in its KDE model)
counts_inv = 1 / (F.counts_from_labels(y, classes=self.classes_)) counts_inv = 1 / (F.counts_from_labels(y, classes=np.arange(n)))
# tr_tr_sums corresponds to symbol \overline{B} in the paper # tr_tr_sums corresponds to symbol \overline{B} in the paper
tr_tr_sums = np.zeros(shape=(n,n), dtype=float) tr_tr_sums = np.zeros(shape=(n,n), dtype=float)

155
quapy/method/aggregative.py
View File

@ -3,7 +3,6 @@ from argparse import ArgumentError
from copy import deepcopy from copy import deepcopy
from typing import Callable, Literal, Union from typing import Callable, Literal, Union
import numpy as np import numpy as np
from numpy.f2py.crackfortran import true_intent_list
from sklearn.base import BaseEstimator from sklearn.base import BaseEstimator
from sklearn.calibration import CalibratedClassifierCV from sklearn.calibration import CalibratedClassifierCV
from sklearn.exceptions import NotFittedError from sklearn.exceptions import NotFittedError
@ -17,25 +16,17 @@ from quapy.functional import get_divergence
from quapy.classification.svmperf import SVMperf from quapy.classification.svmperf import SVMperf
from quapy.data import LabelledCollection from quapy.data import LabelledCollection
from quapy.method.base import BaseQuantifier, BinaryQuantifier, OneVsAllGeneric from quapy.method.base import BaseQuantifier, BinaryQuantifier, OneVsAllGeneric
from quapy.method._helper import (
_get_abstention_calibrators,
_get_cvxpy,
_rlls_check_mode,
_rlls_joint_distribution,
_rlls_predicted_marginal,
_rlls_compute_3deltaC,
_rlls_compute_weights,
_labels_to_indices,
)
# import warnings
# from sklearn.exceptions import ConvergenceWarning
# warnings.filterwarnings("ignore", category=ConvergenceWarning)
def _get_abstention_calibrators():
try:
from abstention.calibration import NoBiasVectorScaling, TempScaling, VectorScaling
except ImportError as exc:
raise ImportError(
"Posterior calibration for EMQ requires the optional 'abstention' package."
) from exc
return {
'nbvs': NoBiasVectorScaling(),
'bcts': TempScaling(bias_positions='all'),
'ts': TempScaling(),
'vs': VectorScaling(),
}
# Abstract classes # Abstract classes
@ -683,6 +674,108 @@ class PACC(AggregativeSoftQuantifier):
return confusion.T return confusion.T
class RLLS(AggregativeSoftQuantifier):
"""
`Regularized Learning for Domain Adaptation under Label Shifts
<https://arxiv.org/abs/1903.09734>`_, used here as an aggregative
quantifier.
This implementation ports the regularized weight-estimation component of
RLLS to QuaPy's aggregative interface. It estimates label-shift ratios from
validation posteriors and source labels, then rescales the source
prevalence to obtain target prevalence estimates.
This method relies on the optional `cvxpy` dependency.
:param classifier: a scikit-learn's BaseEstimator, or None, in which case
the classifier is taken to be the one indicated in
`qp.environ['DEFAULT_CLS']`
:param fit_classifier: whether to train the learner (default is True). Set
to False if the learner has been trained outside the quantifier.
:param val_split: specifies the data used for generating classifier
predictions. This specification can be made as float in (0, 1)
indicating the proportion of stratified held-out validation set to be
extracted from the training set; or as an integer (default 5),
indicating that the predictions are to be generated in a `k`-fold
cross-validation manner; or as a tuple `(X, y)` defining the specific
set of data to use for validation. This method requires source
predictions and therefore needs `val_split` whenever
`fit_classifier=True`.
:param mode: whether source- and target-domain quantities are estimated
from posterior probabilities (`soft`, default) or from argmax
predictions (`hard`)
:param alpha: multiplicative factor for the regularization level (default
0.01)
:param delta: confidence parameter used in the finite-sample regularizer
(default 0.05)
:param clip_weights: if True, clips negative importance weights to zero
before converting them into prevalence estimates
:param norm: the normalization method passed to
:func:`quapy.functional.normalize_prevalence`
"""
def __init__(
self,
classifier: BaseEstimator = None,
fit_classifier=True,
val_split=5,
mode: Literal['soft', 'hard'] = 'soft',
alpha: float = 0.01,
delta: float = 0.05,
clip_weights: bool = True,
norm: Literal['clip', 'mapsimplex', 'condsoftmax'] = 'clip',
):
super().__init__(classifier, fit_classifier, val_split)
self.mode = mode
self.alpha = alpha
self.delta = delta
self.clip_weights = clip_weights
self.norm = norm
self.last_w_ = None
def _check_init_parameters(self):
_get_cvxpy()
_rlls_check_mode(self.mode)
if not isinstance(self.alpha, (int, float)) or self.alpha < 0:
raise ValueError(f'expected a non-negative real value for alpha; found {self.alpha!r}')
if not isinstance(self.delta, (int, float)) or not (0 < self.delta < 1):
raise ValueError(f'expected delta to be in (0,1); found {self.delta!r}')
if self.norm not in ACC.NORMALIZATIONS:
raise ValueError(f"unknown normalization; valid ones are {ACC.NORMALIZATIONS}")
if self.fit_classifier and self.val_split is None:
raise ValueError(
'RLLS requires validation predictions for aggregation_fit; '
'please set val_split to an integer, float, or validation tuple'
)
def aggregation_fit(self, classif_predictions, labels):
if classif_predictions is None or labels is None:
raise ValueError('RLLS requires source posterior predictions and source labels')
self.train_prevalence_ = F.prevalence_from_labels(labels, classes=self.classes_)
self.C_zy_ = _rlls_joint_distribution(
classif_predictions,
labels,
self.classes_,
mode=self.mode,
)
self.pz_ = _rlls_predicted_marginal(classif_predictions, mode=self.mode)
self.rho_ = _rlls_compute_3deltaC(len(self.classes_), len(labels), self.delta)
def aggregate(self, classif_posteriors):
qz = _rlls_predicted_marginal(classif_posteriors, mode=self.mode)
w = _rlls_compute_weights(
self.C_zy_,
qz,
self.pz_,
rho=self.alpha * self.rho_,
clip=self.clip_weights,
)
self.last_w_ = w
estimate = self.train_prevalence_ * w
return F.normalize_prevalence(estimate, method=self.norm)
class EMQ(AggregativeSoftQuantifier): class EMQ(AggregativeSoftQuantifier):
""" """
`Expectation Maximization for Quantification <https://ieeexplore.ieee.org/abstract/document/6789744>`_ (EMQ), `Expectation Maximization for Quantification <https://ieeexplore.ieee.org/abstract/document/6789744>`_ (EMQ),
@ -991,9 +1084,6 @@ class HDy(AggregativeSoftQuantifier, BinaryAggregativeQuantifier):
Px = classif_posteriors[:, self.pos_label] # takes only the P(y=+1|x) Px = classif_posteriors[:, self.pos_label] # takes only the P(y=+1|x)
prev_estimations = [] prev_estimations = []
# for bins in np.linspace(10, 110, 11, dtype=int): #[10, 20, 30, ..., 100, 110]
# Pxy0_density, _ = np.histogram(self.Pxy0, bins=bins, range=(0, 1), density=True)
# Pxy1_density, _ = np.histogram(self.Pxy1, bins=bins, range=(0, 1), density=True)
for bins in self.bins: for bins in self.bins:
Pxy0_density = self.Pxy0_density[bins] Pxy0_density = self.Pxy0_density[bins]
Pxy1_density = self.Pxy1_density[bins] Pxy1_density = self.Pxy1_density[bins]
@ -1003,13 +1093,12 @@ class HDy(AggregativeSoftQuantifier, BinaryAggregativeQuantifier):
# the authors proposed to search for the prevalence yielding the best matching as a linear search # the authors proposed to search for the prevalence yielding the best matching as a linear search
# at small steps (modern implementations resort to an optimization procedure, # at small steps (modern implementations resort to an optimization procedure,
# see class DistributionMatching) # see class DistributionMatching)
prev_selected, min_dist = None, None def loss(prev):
for prev in F.prevalence_linspace(grid_points=101, repeats=1, smooth_limits_epsilon=0.0): class1_prev = prev[1]
Px_train = prev * Pxy1_density + (1 - prev) * Pxy0_density Px_train = class1_prev * Pxy1_density + (1 - class1_prev) * Pxy0_density
hdy = F.HellingerDistance(Px_train, Px_test) return F.HellingerDistance(Px_train, Px_test)
if prev_selected is None or hdy < min_dist:
prev_selected, min_dist = prev, hdy prev_estimations.append(F.linear_search(loss, n_classes=2)[1])
prev_estimations.append(prev_selected)
class1_prev = np.median(prev_estimations) class1_prev = np.median(prev_estimations)
return F.as_binary_prevalence(class1_prev) return F.as_binary_prevalence(class1_prev)
@ -1168,6 +1257,10 @@ class DMy(AggregativeSoftQuantifier):
:param cdf: whether to use CDF instead of PDF (default False) :param cdf: whether to use CDF instead of PDF (default False)
:param search: string indicating the search strategy used to estimate the prevalence values.
Valid options are `optim_minimize` (default, works for binary and multiclass problems),
`linear_search` (binary only), and `ternary_search` (binary only)
:param n_jobs: number of parallel workers (default None) :param n_jobs: number of parallel workers (default None)
""" """
@ -1218,7 +1311,9 @@ class DMy(AggregativeSoftQuantifier):
:param labels: array-like with the true labels associated to each posterior :param labels: array-like with the true labels associated to each posterior
""" """
posteriors, true_labels = classif_predictions, labels posteriors, true_labels = classif_predictions, labels
n_classes = len(self.classifier.classes_) classes = self.classifier.classes_
n_classes = len(classes)
true_labels = _labels_to_indices(true_labels, classes)
self.validation_distribution = qp.util.parallel( self.validation_distribution = qp.util.parallel(
func=self._get_distributions, func=self._get_distributions,

8
quapy/method/non_aggregative.py
View File

@ -9,6 +9,7 @@ from sklearn.preprocessing import normalize
from quapy.method.confidence import WithConfidenceABC, ConfidenceRegionABC from quapy.method.confidence import WithConfidenceABC, ConfidenceRegionABC
from quapy.functional import get_divergence from quapy.functional import get_divergence
from quapy.method.base import BaseQuantifier, BinaryQuantifier from quapy.method.base import BaseQuantifier, BinaryQuantifier
from quapy.method._helper import _labels_to_indices
import quapy.functional as F import quapy.functional as F
from scipy.optimize import lsq_linear from scipy.optimize import lsq_linear
from scipy import sparse from scipy import sparse
@ -58,6 +59,9 @@ class DMx(BaseQuantifier):
or a callable function taking two ndarrays of the same dimension as input (default "HD", meaning Hellinger or a callable function taking two ndarrays of the same dimension as input (default "HD", meaning Hellinger
Distance) Distance)
:param cdf: whether to use CDF instead of PDF (default False) :param cdf: whether to use CDF instead of PDF (default False)
:param search: string indicating the search strategy used to estimate the prevalence values.
Valid options are `optim_minimize` (default, works for binary and multiclass problems),
`linear_search` (binary only), and `ternary_search` (binary only)
:param n_jobs: number of parallel workers (default None) :param n_jobs: number of parallel workers (default None)
""" """
@ -122,7 +126,9 @@ class DMx(BaseQuantifier):
""" """
self.nfeats = X.shape[1] self.nfeats = X.shape[1]
self.feat_ranges = _get_features_range(X) self.feat_ranges = _get_features_range(X)
n_classes = len(np.unique(y)) classes = np.unique(y)
y = _labels_to_indices(y, classes)
n_classes = len(classes)
self.validation_distribution = np.asarray( self.validation_distribution = np.asarray(
[self.__get_distributions(X[y==cat]) for cat in range(n_classes)] [self.__get_distributions(X[y==cat]) for cat in range(n_classes)]

54
quapy/tests/test_methods.py
View File

@ -2,10 +2,13 @@ import itertools
import inspect import inspect
import unittest import unittest
import numpy as np
from sklearn.linear_model import LogisticRegression from sklearn.linear_model import LogisticRegression
from quapy.method import AGGREGATIVE_METHODS, BINARY_METHODS, NON_AGGREGATIVE_METHODS from quapy.method import AGGREGATIVE_METHODS, BINARY_METHODS, NON_AGGREGATIVE_METHODS
from quapy.method.aggregative import ACC from quapy.method.non_aggregative import DMx
from quapy.method.aggregative import ACC, DMy, KDEyCS, RLLS
from quapy.method.meta import Ensemble from quapy.method.meta import Ensemble
from quapy.functional import check_prevalence_vector from quapy.functional import check_prevalence_vector
from quapy.tests._synthetic import make_dataset from quapy.tests._synthetic import make_dataset
@ -16,6 +19,7 @@ OPTIONAL_AGGREGATIVE_METHODS = {
'BayesianKDEy', 'BayesianKDEy',
'BayesianMAPLS', 'BayesianMAPLS',
'PQ', 'PQ',
'RLLS',
} }
@ -122,6 +126,54 @@ class TestMethods(unittest.TestCase):
from quapy.method.composable import __old_version_message from quapy.method.composable import __old_version_message
print(__old_version_message) print(__old_version_message)
def test_rlls(self):
try:
import cvxpy # noqa: F401
except ImportError:
return
dataset = TestMethods.tiny_dataset_multiclass
q = RLLS(LogisticRegression(max_iter=2000), val_split=3)
q.fit(*dataset.training.Xy)
estim_prevalences = q.predict(dataset.test.X)
self.assertTrue(check_prevalence_vector(estim_prevalences))
def test_dmy_noncanonical_labels(self):
dataset = TestMethods.tiny_dataset_multiclass
label_names = np.asarray(['class-a', 'class-c', 'class-z'])
y_train = label_names[dataset.training.y]
y_test = label_names[dataset.test.y]
q = DMy(LogisticRegression(max_iter=2000), val_split=3)
q.fit(dataset.training.X, y_train)
estim_prevalences = q.predict(dataset.test.X)
self.assertTrue(check_prevalence_vector(estim_prevalences))
self.assertEqual(len(estim_prevalences), len(np.unique(y_test)))
def test_dmx_noncanonical_labels(self):
dataset = TestMethods.tiny_dataset_multiclass
label_names = np.asarray(['class-a', 'class-c', 'class-z'])
y_train = label_names[dataset.training.y]
q = DMx()
q.fit(dataset.training.X, y_train)
estim_prevalences = q.predict(dataset.test.X)
self.assertTrue(check_prevalence_vector(estim_prevalences))
self.assertEqual(len(estim_prevalences), len(np.unique(y_train)))
def test_kdeycs_noncanonical_labels(self):
dataset = TestMethods.tiny_dataset_multiclass
label_names = np.asarray(['class-a', 'class-c', 'class-z'])
y_train = label_names[dataset.training.y]
q = KDEyCS(LogisticRegression(max_iter=2000), val_split=3)
q.fit(dataset.training.X, y_train)
estim_prevalences = q.predict(dataset.test.X)
self.assertTrue(check_prevalence_vector(estim_prevalences))
self.assertEqual(len(estim_prevalences), len(np.unique(y_train)))
if __name__ == '__main__': if __name__ == '__main__':
unittest.main() unittest.main()