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added methods to doc

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Alejandro Moreo Fernandez 2026-07-05 23:20:56 +02:00
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@ -390,7 +391,9 @@
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<h1>Source code for quapy.classification.calibration</h1><div class="highlight"><pre>
<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>
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<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="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">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>
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<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 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="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="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">LabelEncoder</span>
<span class="kn">from</span> <span class="nn">sklearn.utils.validation</span> <span class="kn">import</span> <span class="n">check_X_y</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</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>
@ -88,20 +384,20 @@
<span class="c1"># see https://github.com/kundajelab/abstention</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_require_abstention_calibration</span><span class="p">():</span>
<span class="k">def</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="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="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="p">)</span> <span class="kn">from</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="k">class</span> <span class="nc">RecalibratedProbabilisticClassifier</span><span class="p">:</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"> `Alexandari, A., Kundaje, A., &amp; Shrikumar, A. (2020, November). Maximum likelihood with bias-corrected calibration</span>
@ -114,7 +410,7 @@
<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="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>
<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"> `Alexandari et al. paper &lt;http://proceedings.mlr.press/v119/alexandari20a.html&gt;`_.</span>
@ -130,7 +426,7 @@
<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="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="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="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">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
@ -139,7 +435,7 @@
<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="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="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Fits the calibration for the probabilistic classifier.</span>
@ -160,7 +456,7 @@
<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="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>
<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"> training instances via cross-validation, and then retrains the classifier on all training instances.</span>
@ -181,7 +477,7 @@
<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="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>
<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"> training and a validation set, and then uses the training samples to learn classifier which is then used</span>
@ -202,7 +498,7 @@
<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="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="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Predicts class labels for the data instances in `X`</span>
@ -214,7 +510,7 @@
<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="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="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Generates posterior probabilities for the data instances in `X`</span>
@ -226,7 +522,7 @@
<span class="nd">@property</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="k">def</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"> Returns the classes on which the classifier has been trained on</span>
@ -238,7 +534,7 @@
<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="k">class</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="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>
@ -252,7 +548,7 @@
<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="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="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="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">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>
@ -264,7 +560,7 @@
<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="k">class</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="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>
@ -278,7 +574,7 @@
<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="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="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="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">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>
@ -290,7 +586,7 @@
<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="k">class</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="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>
@ -304,7 +600,7 @@
<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="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="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="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">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>
@ -316,7 +612,7 @@
<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="k">class</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="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>
@ -330,7 +626,7 @@
<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="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="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="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">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>
@ -342,7 +638,7 @@
<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="k">class</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>
@ -357,13 +653,13 @@
<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="k">def</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="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="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Fits the logits calibrator.</span>
@ -398,7 +694,7 @@
<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="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="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Converts logits into calibrated posterior probabilities.</span>
@ -412,7 +708,7 @@
<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="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="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Predicts class labels after calibration.</span>
@ -427,31 +723,75 @@
</pre></div>
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<h1>Source code for quapy.classification.methods</h1><div class="highlight"><pre>
<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="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="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>
<span></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="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</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="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
<div class="viewcode-block" id="LowRankLogisticRegression">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression">[docs]</a>
<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="k">class</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="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>
@ -96,13 +392,13 @@
<span class="sd"> `Logistic Regression &lt;https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html&gt;`__ classifier</span>
<span class="sd"> &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">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="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">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">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.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="k">def</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="sd"> Get hyper-parameters for this estimator.</span>
@ -115,7 +411,7 @@
<div class="viewcode-block" id="LowRankLogisticRegression.set_params">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.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="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="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Set the parameters of this estimator.</span>
@ -132,7 +428,7 @@
<div class="viewcode-block" id="LowRankLogisticRegression.fit">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.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="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="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"> fitting `TruncatedSVD` and then `LogisticRegression` on the low-rank representation.</span>
@ -153,7 +449,7 @@
<div class="viewcode-block" id="LowRankLogisticRegression.predict">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.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">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="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>
@ -167,7 +463,7 @@
<div class="viewcode-block" id="LowRankLogisticRegression.predict_proba">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.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">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="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>
@ -180,7 +476,7 @@
<div class="viewcode-block" id="LowRankLogisticRegression.transform">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.transform">[docs]</a>
<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="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="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"> `n_components` &gt;= `X.shape[1]`.</span>
@ -197,7 +493,7 @@
<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="k">class</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>
@ -208,50 +504,94 @@
<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="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="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">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">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">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">return</span> <span class="n">X</span></div>
</div>
</pre></div>
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120
docs/build/html/_modules/quapy/classification/neural.html vendored
View File

@ -29,7 +29,7 @@
<link href="../../../_static/styles/theme.css?digest=a95f357e85573c9b56d5" rel="stylesheet" />
<link href="../../../_static/styles/pydata-sphinx-theme.css?digest=a95f357e85573c9b56d5" rel="stylesheet" />
<link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=8f2a1f02" />
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<link rel="stylesheet" type="text/css" href="../../../_static/custom.css?v=9f2a2228" />
@ -370,26 +370,27 @@
<article class="bd-article">
<h1>Source code for quapy.classification.neural</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">os</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="w"> </span><span class="nn">pathlib</span><span class="w"> </span><span class="kn">import</span> <span class="n">Path</span>
<span></span><span class="kn">import</span> <span class="nn">logging</span>
<span class="kn">import</span> <span class="nn">os</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">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</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="w"> </span><span class="nn">torch</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">torch.nn</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">nn</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">torch.nn.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">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">accuracy_score</span><span class="p">,</span> <span class="n">f1_score</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch.nn.utils.rnn</span><span class="w"> </span><span class="kn">import</span> <span class="n">pad_sequence</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">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">accuracy_score</span><span class="p">,</span> <span class="n">f1_score</span>
<span class="kn">from</span> <span class="nn">torch.nn.utils.rnn</span> <span class="kn">import</span> <span class="n">pad_sequence</span>
<span class="kn">from</span> <span class="nn">tqdm</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</span><span class="w"> </span><span class="kn">import</span> <span class="n">LabelledCollection</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">EarlyStop</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="nn">quapy.data</span> <span class="kn">import</span> <span class="n">LabelledCollection</span>
<span class="kn">from</span> <span class="nn">quapy.util</span> <span class="kn">import</span> <span class="n">EarlyStop</span>
<div class="viewcode-block" id="NeuralClassifierTrainer">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">NeuralClassifierTrainer</span><span class="p">:</span>
<span class="k">class</span> <span class="nc">NeuralClassifierTrainer</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Trains a neural network for text classification.</span>
@ -407,7 +408,7 @@
<span class="sd"> according to the evaluation in the held-out validation split (default &#39;../checkpoint/classifier_net.dat&#39;)</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="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
<span class="n">net</span><span class="p">:</span> <span class="s1">&#39;TextClassifierNet&#39;</span><span class="p">,</span>
<span class="n">lr</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">,</span>
<span class="n">weight_decay</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
@ -416,7 +417,7 @@
<span class="n">batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span>
<span class="n">batch_size_test</span><span class="o">=</span><span class="mi">512</span><span class="p">,</span>
<span class="n">padding_length</span><span class="o">=</span><span class="mi">300</span><span class="p">,</span>
<span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">,</span>
<span class="n">device</span><span class="o">=</span><span class="s1">&#39;cpu&#39;</span><span class="p">,</span>
<span class="n">checkpointpath</span><span class="o">=</span><span class="s1">&#39;../checkpoint/classifier_net.dat&#39;</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>
@ -437,12 +438,12 @@
<span class="bp">self</span><span class="o">.</span><span class="n">learner_hyperparams</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">get_params</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">checkpointpath</span> <span class="o">=</span> <span class="n">checkpointpath</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;[NeuralNetwork running on </span><span class="si">{</span><span class="n">device</span><span class="si">}</span><span class="s1">]&#39;</span><span class="p">)</span>
<span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;NeuralNetwork running on </span><span class="si">{</span><span class="n">device</span><span class="si">}</span><span class="s1">&#39;</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="n">Path</span><span class="p">(</span><span class="n">checkpointpath</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>
<div class="viewcode-block" id="NeuralClassifierTrainer.reset_net_params">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.reset_net_params">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">reset_net_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">reset_net_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Reinitialize the network parameters</span>
<span class="sd"> :param vocab_size: the size of the vocabulary</span>
@ -455,7 +456,7 @@
<div class="viewcode-block" id="NeuralClassifierTrainer.get_params">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.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="k">def</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;Get hyper-parameters for this estimator</span>
<span class="sd"> :return: a dictionary with parameter names mapped to their values</span>
@ -465,7 +466,7 @@
<div class="viewcode-block" id="NeuralClassifierTrainer.set_params">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.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="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="w"> </span><span class="sd">&quot;&quot;&quot;Set the parameters of this trainer and the learner it is training.</span>
<span class="sd"> In this current version, parameter names for the trainer and learner should</span>
<span class="sd"> be disjoint.</span>
@ -491,14 +492,14 @@
<span class="nd">@property</span>
<span class="k">def</span><span class="w"> </span><span class="nf">device</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">device</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot; Gets the device in which the network is allocated</span>
<span class="sd"> :return: device</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="nb">next</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span><span class="o">.</span><span class="n">device</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_train_epoch</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">status</span><span class="p">,</span> <span class="n">pbar</span><span class="p">,</span> <span class="n">epoch</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">_train_epoch</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">status</span><span class="p">,</span> <span class="n">pbar</span><span class="p">,</span> <span class="n">epoch</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
<span class="n">criterion</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">()</span>
<span class="n">losses</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">true_labels</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[]</span>
@ -518,7 +519,7 @@
<span class="n">status</span><span class="p">[</span><span class="s2">&quot;f1&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">f1_score</span><span class="p">(</span><span class="n">true_labels</span><span class="p">,</span> <span class="n">predictions</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>
<span class="bp">self</span><span class="o">.</span><span class="n">__update_progress_bar</span><span class="p">(</span><span class="n">pbar</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="nf">_test_epoch</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">status</span><span class="p">,</span> <span class="n">pbar</span><span class="p">,</span> <span class="n">epoch</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">_test_epoch</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">status</span><span class="p">,</span> <span class="n">pbar</span><span class="p">,</span> <span class="n">epoch</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="n">criterion</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">()</span>
<span class="n">losses</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">true_labels</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[]</span>
@ -536,7 +537,7 @@
<span class="n">status</span><span class="p">[</span><span class="s2">&quot;f1&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">f1_score</span><span class="p">(</span><span class="n">true_labels</span><span class="p">,</span> <span class="n">predictions</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>
<span class="bp">self</span><span class="o">.</span><span class="n">__update_progress_bar</span><span class="p">(</span><span class="n">pbar</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="nf">__update_progress_bar</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pbar</span><span class="p">,</span> <span class="n">epoch</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">__update_progress_bar</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pbar</span><span class="p">,</span> <span class="n">epoch</span><span class="p">):</span>
<span class="n">pbar</span><span class="o">.</span><span class="n">set_description</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="n">net</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">] training epoch=</span><span class="si">{</span><span class="n">epoch</span><span class="si">}</span><span class="s1"> &#39;</span>
<span class="sa">f</span><span class="s1">&#39;tr-loss=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s2">&quot;tr&quot;</span><span class="p">][</span><span class="s2">&quot;loss&quot;</span><span class="p">]</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;tr-acc=</span><span class="si">{</span><span class="mi">100</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s2">&quot;tr&quot;</span><span class="p">][</span><span class="s2">&quot;acc&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">% &#39;</span>
@ -548,7 +549,7 @@
<div class="viewcode-block" id="NeuralClassifierTrainer.fit">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.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">instances</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mf">0.3</span><span class="p">):</span>
<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">instances</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mf">0.3</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Fits the model according to the given training data.</span>
@ -583,21 +584,22 @@
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">early_stop</span><span class="o">.</span><span class="n">IMPROVED</span><span class="p">:</span>
<span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="n">checkpoint</span><span class="p">)</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">early_stop</span><span class="o">.</span><span class="n">STOP</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;training ended by patience exhasted; loading best model parameters in </span><span class="si">{</span><span class="n">checkpoint</span><span class="si">}</span><span class="s1"> &#39;</span>
<span class="sa">f</span><span class="s1">&#39;for epoch </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">early_stop</span><span class="o">.</span><span class="n">best_epoch</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
<span class="sa">f</span><span class="s1">&#39;training ended by patience exhausted; loading best model parameters in </span><span class="si">{</span><span class="n">checkpoint</span><span class="si">}</span><span class="s1"> &#39;</span>
<span class="sa">f</span><span class="s1">&#39;for epoch </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">early_stop</span><span class="o">.</span><span class="n">best_epoch</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">net</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">))</span>
<span class="k">break</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;performing one training pass over the validation set...&#39;</span><span class="p">)</span>
<span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">&#39;performing one training pass over the validation set...&#39;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_train_epoch</span><span class="p">(</span><span class="n">valid_generator</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s1">&#39;tr&#39;</span><span class="p">],</span> <span class="n">pbar</span><span class="p">,</span> <span class="n">epoch</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;[done]&#39;</span><span class="p">)</span>
<span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">&#39;done&#39;</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="NeuralClassifierTrainer.predict">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.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">instances</span><span class="p">):</span>
<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">instances</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Predicts labels for the instances</span>
@ -610,7 +612,7 @@
<div class="viewcode-block" id="NeuralClassifierTrainer.predict_proba">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.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">instances</span><span class="p">):</span>
<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">instances</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Predicts posterior probabilities for the instances</span>
@ -629,7 +631,7 @@
<div class="viewcode-block" id="NeuralClassifierTrainer.transform">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.transform">[docs]</a>
<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">instances</span><span class="p">):</span>
<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">instances</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the embeddings of the instances</span>
@ -651,7 +653,7 @@
<div class="viewcode-block" id="TorchDataset">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.TorchDataset">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">TorchDataset</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">Dataset</span><span class="p">):</span>
<span class="k">class</span> <span class="nc">TorchDataset</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">Dataset</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Transforms labelled instances into a Torch&#39;s :class:`torch.utils.data.DataLoader` object</span>
@ -659,19 +661,19 @@
<span class="sd"> :param labels: array-like of shape `(n_samples, n_classes)` with the class labels</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">instances</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="kc">None</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">instances</span><span class="p">,</span> <span class="n">labels</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">instances</span> <span class="o">=</span> <span class="n">instances</span>
<span class="bp">self</span><span class="o">.</span><span class="n">labels</span> <span class="o">=</span> <span class="n">labels</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="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</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">instances</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__getitem__</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="fm">__getitem__</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">return</span> <span class="p">{</span><span class="s1">&#39;doc&#39;</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="n">index</span><span class="p">],</span> <span class="s1">&#39;label&#39;</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">index</span><span class="p">]</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">labels</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="kc">None</span><span class="p">}</span>
<div class="viewcode-block" id="TorchDataset.asDataloader">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.TorchDataset.asDataloader">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">asDataloader</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="p">,</span> <span class="n">pad_length</span><span class="p">,</span> <span class="n">device</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">asDataloader</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="p">,</span> <span class="n">pad_length</span><span class="p">,</span> <span class="n">device</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Converts the labelled collection into a Torch DataLoader with dynamic padding for</span>
<span class="sd"> the batch</span>
@ -684,7 +686,7 @@
<span class="sd"> :param device: whether to allocate tensors in cpu or in cuda</span>
<span class="sd"> :return: a :class:`torch.utils.data.DataLoader` object</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span><span class="w"> </span><span class="nf">collate</span><span class="p">(</span><span class="n">batch</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">collate</span><span class="p">(</span><span class="n">batch</span><span class="p">):</span>
<span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">(</span><span class="n">item</span><span class="p">[</span><span class="s1">&#39;doc&#39;</span><span class="p">][:</span><span class="n">pad_length</span><span class="p">])</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">]</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">pad_sequence</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">batch_first</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">padding_value</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;PAD_INDEX&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
<span class="n">targets</span> <span class="o">=</span> <span class="p">[</span><span class="n">item</span><span class="p">[</span><span class="s1">&#39;label&#39;</span><span class="p">]</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">]</span>
@ -702,7 +704,7 @@
<div class="viewcode-block" id="TextClassifierNet">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.TextClassifierNet">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">TextClassifierNet</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</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="k">class</span> <span class="nc">TextClassifierNet</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</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="sd"> Abstract Text classifier (`torch.nn.Module`)</span>
<span class="sd"> &quot;&quot;&quot;</span>
@ -710,7 +712,7 @@
<div class="viewcode-block" id="TextClassifierNet.document_embedding">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.TextClassifierNet.document_embedding">[docs]</a>
<span class="nd">@abstractmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">document_embedding</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="nf">document_embedding</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;Embeds documents (i.e., performs the forward pass up to the</span>
<span class="sd"> next-to-last layer).</span>
@ -725,7 +727,7 @@
<div class="viewcode-block" id="TextClassifierNet.forward">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.TextClassifierNet.forward">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">forward</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="nf">forward</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;Performs the forward pass.</span>
<span class="sd"> :param x: a batch of instances, typically generated by a torch&#39;s `DataLoader`</span>
@ -739,7 +741,7 @@
<div class="viewcode-block" id="TextClassifierNet.dimensions">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.TextClassifierNet.dimensions">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">dimensions</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">dimensions</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Gets the number of dimensions of the embedding space</span>
<span class="sd"> :return: integer</span>
@ -749,7 +751,7 @@
<div class="viewcode-block" id="TextClassifierNet.predict_proba">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.TextClassifierNet.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">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="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Predicts posterior probabilities for the instances in `x`</span>
@ -764,7 +766,7 @@
<div class="viewcode-block" id="TextClassifierNet.xavier_uniform">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.TextClassifierNet.xavier_uniform">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">xavier_uniform</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">xavier_uniform</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"> Performs Xavier initialization of the network parameters</span>
<span class="sd"> &quot;&quot;&quot;</span>
@ -776,7 +778,7 @@
<div class="viewcode-block" id="TextClassifierNet.get_params">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.TextClassifierNet.get_params">[docs]</a>
<span class="nd">@abstractmethod</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="k">def</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="sd"> Get hyper-parameters for this estimator</span>
@ -786,7 +788,7 @@
<span class="nd">@property</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="k">def</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="sd"> Return the size of the vocabulary</span>
@ -798,7 +800,7 @@
<div class="viewcode-block" id="LSTMnet">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.LSTMnet">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">LSTMnet</span><span class="p">(</span><span class="n">TextClassifierNet</span><span class="p">):</span>
<span class="k">class</span> <span class="nc">LSTMnet</span><span class="p">(</span><span class="n">TextClassifierNet</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> An implementation of :class:`quapy.classification.neural.TextClassifierNet` based on</span>
<span class="sd"> Long Short Term Memory networks.</span>
@ -812,7 +814,7 @@
<span class="sd"> :param drop_p: drop probability for dropout (default 0.5)</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">vocabulary_size</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">embedding_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">hidden_size</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">repr_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">lstm_class_nlayers</span><span class="o">=</span><span class="mi">1</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">vocabulary_size</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">embedding_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">hidden_size</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">repr_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">lstm_class_nlayers</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">drop_p</span><span class="o">=</span><span class="mf">0.5</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>
@ -834,7 +836,7 @@
<span class="bp">self</span><span class="o">.</span><span class="n">doc_embedder</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">hidden_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">__init_hidden</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">set_size</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">__init_hidden</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">set_size</span><span class="p">):</span>
<span class="n">opt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">hyperparams</span>
<span class="n">var_hidden</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">opt</span><span class="p">[</span><span class="s1">&#39;lstm_class_nlayers&#39;</span><span class="p">],</span> <span class="n">set_size</span><span class="p">,</span> <span class="n">opt</span><span class="p">[</span><span class="s1">&#39;hidden_size&#39;</span><span class="p">])</span>
<span class="n">var_cell</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">opt</span><span class="p">[</span><span class="s1">&#39;lstm_class_nlayers&#39;</span><span class="p">],</span> <span class="n">set_size</span><span class="p">,</span> <span class="n">opt</span><span class="p">[</span><span class="s1">&#39;hidden_size&#39;</span><span class="p">])</span>
@ -844,7 +846,7 @@
<div class="viewcode-block" id="LSTMnet.document_embedding">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.LSTMnet.document_embedding">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">document_embedding</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="nf">document_embedding</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;Embeds documents (i.e., performs the forward pass up to the</span>
<span class="sd"> next-to-last layer).</span>
@ -863,7 +865,7 @@
<div class="viewcode-block" id="LSTMnet.get_params">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.LSTMnet.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="k">def</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="sd"> Get hyper-parameters for this estimator</span>
@ -873,7 +875,7 @@
<span class="nd">@property</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="k">def</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="sd"> Return the size of the vocabulary</span>
@ -885,7 +887,7 @@
<div class="viewcode-block" id="CNNnet">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.CNNnet">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">CNNnet</span><span class="p">(</span><span class="n">TextClassifierNet</span><span class="p">):</span>
<span class="k">class</span> <span class="nc">CNNnet</span><span class="p">(</span><span class="n">TextClassifierNet</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> An implementation of :class:`quapy.classification.neural.TextClassifierNet` based on</span>
<span class="sd"> Convolutional Neural Networks.</span>
@ -902,7 +904,7 @@
<span class="sd"> :param drop_p: drop probability for dropout (default 0.5)</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">vocabulary_size</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">embedding_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">hidden_size</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">repr_size</span><span class="o">=</span><span class="mi">100</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">vocabulary_size</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">embedding_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">hidden_size</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">repr_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
<span class="n">kernel_heights</span><span class="o">=</span><span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">drop_p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">):</span>
<span class="nb">super</span><span class="p">(</span><span class="n">CNNnet</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>
@ -927,7 +929,7 @@
<span class="bp">self</span><span class="o">.</span><span class="n">doc_embedder</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">kernel_heights</span><span class="p">)</span> <span class="o">*</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">output</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">__conv_block</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">conv_layer</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">__conv_block</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">conv_layer</span><span class="p">):</span>
<span class="n">conv_out</span> <span class="o">=</span> <span class="n">conv_layer</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span> <span class="c1"># conv_out.size() = (batch_size, out_channels, dim, 1)</span>
<span class="n">activation</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">conv_out</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span> <span class="c1"># activation.size() = (batch_size, out_channels, dim1)</span>
<span class="n">max_out</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">max_pool1d</span><span class="p">(</span><span class="n">activation</span><span class="p">,</span> <span class="n">activation</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">2</span><span class="p">])</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="c1"># maxpool_out.size() = (batch_size, out_channels)</span>
@ -935,7 +937,7 @@
<div class="viewcode-block" id="CNNnet.document_embedding">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.CNNnet.document_embedding">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">document_embedding</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">document_embedding</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Embeds documents (i.e., performs the forward pass up to the</span>
<span class="sd"> next-to-last layer).</span>
@ -960,7 +962,7 @@
<div class="viewcode-block" id="CNNnet.get_params">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.neural.CNNnet.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="k">def</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="sd"> Get hyper-parameters for this estimator</span>
@ -970,7 +972,7 @@
<span class="nd">@property</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="k">def</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="sd"> Return the size of the vocabulary</span>

560
docs/build/html/_modules/quapy/classification/svmperf.html vendored
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@ -1,94 +1,391 @@
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<h1>Source code for quapy.classification.svmperf</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">random</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">shutil</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">subprocess</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">tempfile</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="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="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="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.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="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <span class="n">dump_svmlight_file</span>
<span></span><span class="kn">import</span> <span class="nn">logging</span>
<span class="kn">import</span> <span class="nn">random</span>
<span class="kn">import</span> <span class="nn">shutil</span>
<span class="kn">import</span> <span class="nn">subprocess</span>
<span class="kn">import</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="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="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">import</span> <span class="nn">numpy</span> <span class="k">as</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="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">dump_svmlight_file</span>
<div class="viewcode-block" id="SVMperf">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.svmperf.SVMperf">[docs]</a>
<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="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="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"> the `installation documentation &lt;https://hlt-isti.github.io/QuaPy/build/html/Installation.html#svm-perf-with-quantification-oriented-losses&gt;`__</span>
@ -110,7 +407,7 @@
<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="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">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">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>
@ -123,7 +420,7 @@
<div class="viewcode-block" id="SVMperf.fit">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.svmperf.SVMperf.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="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="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Trains the SVM for the multivariate performance loss</span>
@ -146,8 +443,7 @@
<span class="c1"># this would allow to run parallel instances of predict</span>
<span class="n">random_code</span> <span class="o">=</span> <span class="s1">&#39;svmperfprocess&#39;</span><span class="o">+</span><span class="s1">&#39;-&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">local_random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1000000</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="mi">5</span><span class="p">))</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">host_folder</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="c1"># tmp dir are removed after the fit terminates in multiprocessing...</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span> <span class="o">=</span> <span class="n">tempfile</span><span class="o">.</span><span class="n">TemporaryDirectory</span><span class="p">(</span><span class="n">suffix</span><span class="o">=</span><span class="n">random_code</span><span class="p">)</span><span class="o">.</span><span class="n">name</span>
<span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">tempfile</span><span class="o">.</span><span class="n">gettempdir</span><span class="p">(),</span> <span class="n">random_code</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">tmpdir</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">host_folder</span><span class="p">,</span> <span class="s1">&#39;.&#39;</span> <span class="o">+</span> <span class="n">random_code</span><span class="p">)</span>
<span class="n">makedirs</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">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
@ -159,21 +455,21 @@
<span class="n">cmd</span> <span class="o">=</span> <span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">svmperf_learn</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">c_cmd</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss_cmd</span><span class="p">,</span> <span class="n">traindat</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">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;[Running]&#39;</span><span class="p">,</span> <span class="n">cmd</span><span class="p">)</span>
<span class="n">p</span> <span class="o">=</span> <span class="n">subprocess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">cmd</span><span class="o">.</span><span class="n">split</span><span class="p">(),</span> <span class="n">stdout</span><span class="o">=</span><span class="n">PIPE</span><span class="p">,</span> <span class="n">stderr</span><span class="o">=</span><span class="n">STDOUT</span><span class="p">)</span>
<span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;[Running] </span><span class="si">{</span><span class="n">cmd</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="n">p</span> <span class="o">=</span> <span class="n">subprocess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">cmd</span><span class="o">.</span><span class="n">split</span><span class="p">(),</span> <span class="n">stdout</span><span class="o">=</span><span class="n">PIPE</span><span class="p">,</span> <span class="n">stderr</span><span class="o">=</span><span class="n">PIPE</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">exists</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="nb">print</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">stderr</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">&#39;utf-8&#39;</span><span class="p">))</span>
<span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">stderr</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">&#39;utf-8&#39;</span><span class="p">))</span>
<span class="n">remove</span><span class="p">(</span><span class="n">traindat</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="n">p</span><span class="o">.</span><span class="n">stdout</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">&#39;utf-8&#39;</span><span class="p">))</span>
<span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">stdout</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">&#39;utf-8&#39;</span><span class="p">))</span>
<span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="SVMperf.predict">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.svmperf.SVMperf.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">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="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Predicts labels for the instances `X`</span>
@ -188,7 +484,7 @@
<div class="viewcode-block" id="SVMperf.decision_function">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.svmperf.SVMperf.decision_function">[docs]</a>
<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="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="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Evaluate the decision function for the samples in `X`.</span>
@ -211,11 +507,11 @@
<span class="n">cmd</span> <span class="o">=</span> <span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">svmperf_classify</span><span class="p">,</span> <span class="n">testdat</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">predictions_path</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;[Running]&#39;</span><span class="p">,</span> <span class="n">cmd</span><span class="p">)</span>
<span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;[Running] </span><span class="si">{</span><span class="n">cmd</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="n">p</span> <span class="o">=</span> <span class="n">subprocess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">cmd</span><span class="o">.</span><span class="n">split</span><span class="p">(),</span> <span class="n">stdout</span><span class="o">=</span><span class="n">PIPE</span><span class="p">,</span> <span class="n">stderr</span><span class="o">=</span><span class="n">STDOUT</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="n">p</span><span class="o">.</span><span class="n">stdout</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">&#39;utf-8&#39;</span><span class="p">))</span>
<span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">stdout</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">&#39;utf-8&#39;</span><span class="p">))</span>
<span class="n">scores</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="n">predictions_path</span><span class="p">)</span>
<span class="n">remove</span><span class="p">(</span><span class="n">testdat</span><span class="p">)</span>
@ -224,38 +520,82 @@
<span class="k">return</span> <span class="n">scores</span></div>
<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">def</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="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>
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@ -1,96 +1,392 @@
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<h1>Source code for quapy.data.base</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">itertools</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="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Iterable</span>
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<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">scipy.sparse</span><span class="w"> </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">vstack</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="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="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="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>
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<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 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="nn">typing</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">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="nn">scipy.sparse</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="nn">numpy.random</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="nn">quapy.util</span> <span class="kn">import</span> <span class="n">temp_seed</span>
<span class="kn">import</span> <span class="nn">quapy.functional</span> <span class="k">as</span> <span class="nn">F</span>
<div class="viewcode-block" id="LabelledCollection">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">LabelledCollection</span><span class="p">:</span>
<span class="k">class</span> <span class="nc">LabelledCollection</span><span class="p">:</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"> This class implements several sampling routines and other utilities.</span>
@ -102,7 +398,7 @@
<span class="sd"> (i.e., a prevalence of 0)</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">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="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="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>
@ -121,7 +417,7 @@
<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">def</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>
@ -129,7 +425,7 @@
<div class="viewcode-block" id="LabelledCollection.load">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.load">[docs]</a>
<span class="nd">@classmethod</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="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="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="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>
@ -146,7 +442,7 @@
<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">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="k">def</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="sd"> Returns the length of this collection (number of labelled instances)</span>
@ -156,7 +452,7 @@
<div class="viewcode-block" id="LabelledCollection.prevalence">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.prevalence">[docs]</a>
<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="k">def</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="sd"> Returns the prevalence, or relative frequency, of the classes in the codeframe.</span>
@ -168,7 +464,7 @@
<div class="viewcode-block" id="LabelledCollection.counts">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.counts">[docs]</a>
<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="k">def</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="sd"> Returns the number of instances for each of the classes in the codeframe.</span>
@ -179,7 +475,7 @@
<span class="nd">@property</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="k">def</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="sd"> The number of classes</span>
@ -188,7 +484,7 @@
<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">classes_</span><span class="p">)</span>
<span class="nd">@property</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="k">def</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>
@ -197,7 +493,7 @@
<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">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="k">def</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="sd"> Returns True if the number of classes is 2</span>
@ -207,7 +503,7 @@
<div class="viewcode-block" id="LabelledCollection.sampling_index">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.sampling_index">[docs]</a>
<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="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="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"> prevalence values are not specified, then returns the index of a uniform sampling.</span>
@ -270,7 +566,7 @@
<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>
<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="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="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"> with replacement.</span>
@ -288,7 +584,7 @@
<div class="viewcode-block" id="LabelledCollection.sampling">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.sampling">[docs]</a>
<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="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="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"> values. For each class, the sampling is drawn with replacement.</span>
@ -308,7 +604,7 @@
<div class="viewcode-block" id="LabelledCollection.uniform_sampling">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.uniform_sampling">[docs]</a>
<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="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="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"> with replacement.</span>
@ -323,7 +619,7 @@
<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>
<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="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="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"> index.</span>
@ -338,7 +634,7 @@
<div class="viewcode-block" id="LabelledCollection.split_stratified">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.split_stratified">[docs]</a>
<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="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="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"> proportion.</span>
@ -360,7 +656,7 @@
<div class="viewcode-block" id="LabelledCollection.split_random">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.split_random">[docs]</a>
<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="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="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"> proportion.</span>
@ -387,7 +683,7 @@
<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="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="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="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"> Both labelled collections must have the same classes.</span>
@ -403,7 +699,7 @@
<div class="viewcode-block" id="LabelledCollection.join">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.join">[docs]</a>
<span class="nd">@classmethod</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="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="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>
@ -439,12 +735,14 @@
<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="s1">&#39;unsupported operation for collection types&#39;</span><span class="p">)</span>
<span class="n">labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">lc</span><span class="o">.</span><span class="n">labels</span> <span class="k">for</span> <span class="n">lc</span> <span class="ow">in</span> <span class="n">args</span><span class="p">])</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">labels</span><span class="p">)</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
<span class="c1"># union of each collection&#39;s own classes_, so a class declared but absent from</span>
<span class="c1"># this particular join (e.g. an empty fold) is preserved at zero prevalence</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">concatenate</span><span class="p">([</span><span class="n">lc</span><span class="o">.</span><span class="n">classes_</span> <span class="k">for</span> <span class="n">lc</span> <span class="ow">in</span> <span class="n">args</span><span class="p">]))</span>
<span class="k">return</span> <span class="n">LabelledCollection</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="n">classes</span><span class="p">)</span></div>
<span class="nd">@property</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="k">def</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>
@ -453,7 +751,7 @@
<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="k">def</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="sd"> Gets the instances and labels. This is useful when working with `sklearn` estimators, e.g.:</span>
@ -464,7 +762,7 @@
<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="nd">@property</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="k">def</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="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>
@ -474,7 +772,7 @@
<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="nd">@property</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="k">def</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="sd"> An alias to self.instances</span>
@ -483,7 +781,7 @@
<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="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="k">def</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="sd"> An alias to self.labels</span>
@ -492,7 +790,7 @@
<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="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="k">def</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="sd"> An alias to self.prevalence()</span>
@ -503,7 +801,7 @@
<div class="viewcode-block" id="LabelledCollection.stats">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.stats">[docs]</a>
<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="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="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>
@ -538,7 +836,7 @@
<div class="viewcode-block" id="LabelledCollection.kFCV">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.kFCV">[docs]</a>
<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="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="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>
@ -554,7 +852,7 @@
<span class="k">yield</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span></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">def</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>
@ -563,7 +861,7 @@
<div class="viewcode-block" id="Dataset">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.Dataset">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">Dataset</span><span class="p">:</span>
<span class="k">class</span> <span class="nc">Dataset</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Abstraction of training and test :class:`LabelledCollection` objects.</span>
@ -573,7 +871,7 @@
<span class="sd"> :param name: a string representing the name of the dataset</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">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="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="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>
@ -583,7 +881,7 @@
<div class="viewcode-block" id="Dataset.SplitStratified">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.Dataset.SplitStratified">[docs]</a>
<span class="nd">@classmethod</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="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="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"> See :meth:`LabelledCollection.split_stratified`</span>
@ -596,7 +894,7 @@
<span class="nd">@property</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="k">def</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"> The classes according to which the training collection is labelled</span>
@ -605,7 +903,7 @@
<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="nd">@property</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="k">def</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="sd"> The number of classes according to which the training collection is labelled</span>
@ -614,7 +912,7 @@
<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="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="k">def</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="sd"> Returns True if the training collection is labelled according to two classes</span>
@ -625,7 +923,7 @@
<div class="viewcode-block" id="Dataset.load">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.Dataset.load">[docs]</a>
<span class="nd">@classmethod</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="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="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="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>
@ -647,7 +945,7 @@
<span class="nd">@property</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="k">def</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="sd"> If the dataset is textual, and the vocabulary was indicated, returns the size of the vocabulary</span>
@ -656,7 +954,7 @@
<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="nd">@property</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="k">def</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="sd"> Alias to `self.training` and `self.test`</span>
@ -667,7 +965,7 @@
<div class="viewcode-block" id="Dataset.stats">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.Dataset.stats">[docs]</a>
<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="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="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>
@ -694,7 +992,7 @@
<div class="viewcode-block" id="Dataset.kFCV">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.Dataset.kFCV">[docs]</a>
<span class="nd">@classmethod</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="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="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"> :meth:`LabelledCollection.kFCV` that returns :class:`Dataset` instances made of training and test folds.</span>
@ -711,7 +1009,7 @@
<div class="viewcode-block" id="Dataset.reduce">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.Dataset.reduce">[docs]</a>
<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="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="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="sd"> Reduce the number of instances in place for quick experiments. Preserves the prevalence of each set.</span>
@ -732,36 +1030,80 @@
<span class="k">return</span> <span class="bp">self</span></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">def</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>
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@ -370,27 +370,28 @@
<article class="bd-article">
<h1>Source code for quapy.data.datasets</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">os</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">contextmanager</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">zipfile</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="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="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="p">,</span> <span class="n">LabelledCollection</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data.preprocessing</span><span class="w"> </span><span class="kn">import</span> <span class="n">text2tfidf</span><span class="p">,</span> <span class="n">reduce_columns</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data.preprocessing</span><span class="w"> </span><span class="kn">import</span> <span class="n">standardize</span> <span class="k">as</span> <span class="n">standardizer</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data.reader</span><span class="w"> </span><span class="kn">import</span> <span class="o">*</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">download_file_if_not_exists</span><span class="p">,</span> <span class="n">download_file</span><span class="p">,</span> <span class="n">get_quapy_home</span><span class="p">,</span> <span class="n">pickled_resource</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></span><span class="kn">import</span> <span class="nn">logging</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">from</span> <span class="nn">contextlib</span> <span class="kn">import</span> <span class="n">contextmanager</span>
<span class="kn">import</span> <span class="nn">zipfile</span>
<span class="kn">from</span> <span class="nn">os.path</span> <span class="kn">import</span> <span class="n">join</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</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="p">,</span> <span class="n">LabelledCollection</span>
<span class="kn">from</span> <span class="nn">quapy.data.preprocessing</span> <span class="kn">import</span> <span class="n">text2tfidf</span><span class="p">,</span> <span class="n">reduce_columns</span>
<span class="kn">from</span> <span class="nn">quapy.data.preprocessing</span> <span class="kn">import</span> <span class="n">standardize</span> <span class="k">as</span> <span class="n">standardizer</span>
<span class="kn">from</span> <span class="nn">quapy.data.reader</span> <span class="kn">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">quapy.util</span> <span class="kn">import</span> <span class="n">download_file_if_not_exists</span><span class="p">,</span> <span class="n">download_file</span><span class="p">,</span> <span class="n">get_quapy_home</span><span class="p">,</span> <span class="n">pickled_resource</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_fetch_ucirepo</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="nf">_fetch_ucirepo</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">try</span><span class="p">:</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">ucimlrepo</span><span class="w"> </span><span class="kn">import</span> <span class="n">fetch_ucirepo</span>
<span class="kn">from</span> <span class="nn">ucimlrepo</span> <span class="kn">import</span> <span class="n">fetch_ucirepo</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;UCI dataset fetching requires the optional &#39;ucimlrepo&#39; package. &quot;</span>
<span class="s2">&quot;Install it to use fetch_UCIBinaryDataset or fetch_UCIMulticlassDataset.&quot;</span>
<span class="p">)</span> <span class="kn">from</span><span class="w"> </span><span class="nn">exc</span>
<span class="p">)</span> <span class="kn">from</span> <span class="nn">exc</span>
<span class="k">return</span> <span class="n">fetch_ucirepo</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>
@ -497,7 +498,7 @@
<div class="viewcode-block" id="fetch_reviews">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.datasets.fetch_reviews">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">fetch_reviews</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">tfidf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">pickle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Dataset</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">fetch_reviews</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">tfidf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">pickle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Dataset</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Loads a Reviews dataset as a Dataset instance, as used in</span>
<span class="sd"> `Esuli, A., Moreo, A., and Sebastiani, F. &quot;A recurrent neural network for sentiment quantification.&quot;</span>
@ -547,7 +548,7 @@
<div class="viewcode-block" id="fetch_twitter">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.datasets.fetch_twitter">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">fetch_twitter</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">for_model_selection</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">pickle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Dataset</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">fetch_twitter</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">for_model_selection</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">pickle</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Dataset</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Loads a Twitter dataset as a :class:`quapy.data.base.Dataset` instance, as used in:</span>
<span class="sd"> `Gao, W., Sebastiani, F.: From classification to quantification in tweet sentiment analysis.</span>
@ -589,8 +590,9 @@
<span class="k">if</span> <span class="n">dataset_name</span> <span class="ow">in</span> <span class="p">{</span><span class="s1">&#39;semeval13&#39;</span><span class="p">,</span> <span class="s1">&#39;semeval14&#39;</span><span class="p">,</span> <span class="s1">&#39;semeval15&#39;</span><span class="p">}:</span>
<span class="n">trainset_name</span> <span class="o">=</span> <span class="s1">&#39;semeval&#39;</span>
<span class="n">testset_name</span> <span class="o">=</span> <span class="s1">&#39;semeval&#39;</span> <span class="k">if</span> <span class="n">for_model_selection</span> <span class="k">else</span> <span class="n">dataset_name</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;the training and development sets for datasets &#39;semeval13&#39;, &#39;semeval14&#39;, &#39;semeval15&#39; are common &quot;</span>
<span class="sa">f</span><span class="s2">&quot;(called &#39;semeval&#39;); returning trainin-set=&#39;</span><span class="si">{</span><span class="n">trainset_name</span><span class="si">}</span><span class="s2">&#39; and test-set=</span><span class="si">{</span><span class="n">testset_name</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">&quot;the training and development sets for datasets &#39;semeval13&#39;, &#39;semeval14&#39;, &#39;semeval15&#39; are common &quot;</span>
<span class="sa">f</span><span class="s2">&quot;(called &#39;semeval&#39;); returning trainin-set=&#39;</span><span class="si">{</span><span class="n">trainset_name</span><span class="si">}</span><span class="s2">&#39; and test-set=</span><span class="si">{</span><span class="n">testset_name</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">if</span> <span class="n">dataset_name</span> <span class="o">==</span> <span class="s1">&#39;semeval&#39;</span> <span class="ow">and</span> <span class="n">for_model_selection</span><span class="o">==</span><span class="kc">False</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;dataset &quot;semeval&quot; can only be used for model selection. &#39;</span>
@ -624,7 +626,7 @@
<div class="viewcode-block" id="fetch_UCIBinaryDataset">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.datasets.fetch_UCIBinaryDataset">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">fetch_UCIBinaryDataset</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">test_split</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">standardize</span><span class="o">=</span><span class="kc">True</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="o">-&gt;</span> <span class="n">Dataset</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">fetch_UCIBinaryDataset</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">test_split</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">standardize</span><span class="o">=</span><span class="kc">True</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="o">-&gt;</span> <span class="n">Dataset</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Loads a UCI dataset as an instance of :class:`quapy.data.base.Dataset`, as used in</span>
<span class="sd"> `Pérez-Gállego, P., Quevedo, J. R., &amp; del Coz, J. J. (2017).</span>
@ -658,7 +660,7 @@
<div class="viewcode-block" id="fetch_UCIBinaryLabelledCollection">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.datasets.fetch_UCIBinaryLabelledCollection">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">fetch_UCIBinaryLabelledCollection</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">standardize</span><span class="o">=</span><span class="kc">True</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="o">-&gt;</span> <span class="n">LabelledCollection</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">fetch_UCIBinaryLabelledCollection</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">standardize</span><span class="o">=</span><span class="kc">True</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="o">-&gt;</span> <span class="n">LabelledCollection</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Loads a UCI collection as an instance of :class:`quapy.data.base.LabelledCollection`, as used in</span>
<span class="sd"> `Pérez-Gállego, P., Quevedo, J. R., &amp; del Coz, J. J. (2017).</span>
@ -851,7 +853,7 @@
<span class="n">file</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">data_home</span><span class="p">,</span> <span class="s2">&quot;uci_datasets&quot;</span><span class="p">,</span> <span class="n">dataset_group</span> <span class="o">+</span> <span class="s2">&quot;.pkl&quot;</span><span class="p">)</span>
<span class="nd">@contextmanager</span>
<span class="k">def</span><span class="w"> </span><span class="nf">download_tmp_file</span><span class="p">(</span><span class="n">url_group</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">filename</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">download_tmp_file</span><span class="p">(</span><span class="n">url_group</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">filename</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Download a data file for a group of datasets temporarely.</span>
<span class="sd"> When used as a context, the file is removed once the context exits.</span>
@ -869,7 +871,7 @@
<span class="k">finally</span><span class="p">:</span>
<span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">data_path</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">download</span><span class="p">(</span><span class="nb">id</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="kc">None</span><span class="p">,</span> <span class="n">group</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">download</span><span class="p">(</span><span class="nb">id</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="kc">None</span><span class="p">,</span> <span class="n">group</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Download the data to be pickled for a dataset group. Use the `fetch_ucirepo` api when possible.</span>
@ -935,7 +937,7 @@
<span class="k">return</span> <span class="n">data</span>
<span class="k">def</span><span class="w"> </span><span class="nf">binarize_data</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="nb">dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">LabelledCollection</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">binarize_data</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="nb">dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">LabelledCollection</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Filter and transform data to extract a binary dataset.</span>
@ -980,7 +982,7 @@
<div class="viewcode-block" id="fetch_UCIMulticlassDataset">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.datasets.fetch_UCIMulticlassDataset">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">fetch_UCIMulticlassDataset</span><span class="p">(</span>
<span class="k">def</span> <span class="nf">fetch_UCIMulticlassDataset</span><span class="p">(</span>
<span class="n">dataset_name</span><span class="p">,</span>
<span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">min_test_split</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span>
@ -1043,7 +1045,7 @@
<div class="viewcode-block" id="fetch_UCIMulticlassLabelledCollection">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.datasets.fetch_UCIMulticlassLabelledCollection">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">fetch_UCIMulticlassLabelledCollection</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">min_class_support</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">standardize</span><span class="o">=</span><span class="kc">True</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="o">-&gt;</span> <span class="n">LabelledCollection</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">fetch_UCIMulticlassLabelledCollection</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">min_class_support</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">standardize</span><span class="o">=</span><span class="kc">True</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="o">-&gt;</span> <span class="n">LabelledCollection</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Loads a UCI multiclass collection as an instance of :class:`quapy.data.base.LabelledCollection`.</span>
@ -1146,7 +1148,7 @@
<span class="n">file</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">data_home</span><span class="p">,</span> <span class="s1">&#39;uci_multiclass&#39;</span><span class="p">,</span> <span class="n">dataset_name</span><span class="o">+</span><span class="s1">&#39;.pkl&#39;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">dummify_categorical_features</span><span class="p">(</span><span class="n">df_features</span><span class="p">,</span> <span class="n">dataset_id</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">dummify_categorical_features</span><span class="p">(</span><span class="n">df_features</span><span class="p">,</span> <span class="n">dataset_id</span><span class="p">):</span>
<span class="n">categorical_features</span> <span class="o">=</span> <span class="p">{</span>
<span class="mi">158</span><span class="p">:</span> <span class="p">[</span><span class="s2">&quot;S1&quot;</span><span class="p">,</span> <span class="s2">&quot;C1&quot;</span><span class="p">,</span> <span class="s2">&quot;S2&quot;</span><span class="p">,</span> <span class="s2">&quot;C2&quot;</span><span class="p">,</span> <span class="s2">&quot;S3&quot;</span><span class="p">,</span> <span class="s2">&quot;C3&quot;</span><span class="p">,</span> <span class="s2">&quot;S4&quot;</span><span class="p">,</span> <span class="s2">&quot;C4&quot;</span><span class="p">,</span> <span class="s2">&quot;S5&quot;</span><span class="p">,</span> <span class="s2">&quot;C5&quot;</span><span class="p">],</span> <span class="c1"># poker_hand</span>
<span class="p">}</span>
@ -1160,7 +1162,7 @@
<span class="k">return</span> <span class="n">X</span>
<span class="k">def</span><span class="w"> </span><span class="nf">download</span><span class="p">(</span><span class="nb">id</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">download</span><span class="p">(</span><span class="nb">id</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">_fetch_ucirepo</span><span class="p">(</span><span class="nb">id</span><span class="o">=</span><span class="nb">id</span><span class="p">)</span>
<span class="n">X_df</span> <span class="o">=</span> <span class="n">dummify_categorical_features</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">features</span><span class="p">,</span> <span class="nb">id</span><span class="p">)</span>
@ -1173,7 +1175,7 @@
<span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">searchsorted</span><span class="p">(</span><span class="n">classes</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="k">return</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">def</span><span class="w"> </span><span class="nf">filter_classes</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">min_class_support</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">filter_classes</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">min_class_support</span><span class="p">):</span>
<span class="k">if</span> <span class="n">min_class_support</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">min_class_support</span> <span class="o">==</span> <span class="mf">0.</span><span class="p">:</span>
<span class="k">return</span> <span class="n">data</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">min_class_support</span><span class="p">,</span> <span class="nb">float</span><span class="p">):</span>
@ -1207,18 +1209,18 @@
<span class="k">def</span><span class="w"> </span><span class="nf">_df_replace</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">col</span><span class="p">,</span> <span class="n">repl</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;yes&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;no&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">},</span> <span class="n">astype</span><span class="o">=</span><span class="nb">float</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">_df_replace</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">col</span><span class="p">,</span> <span class="n">repl</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;yes&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;no&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">},</span> <span class="n">astype</span><span class="o">=</span><span class="nb">float</span><span class="p">):</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">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span><span class="n">repl</span><span class="p">[</span><span class="n">x</span><span class="p">])</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">astype</span><span class="p">,</span> <span class="n">copy</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">_array_replace</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">repl</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;yes&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;no&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">}):</span>
<span class="k">def</span> <span class="nf">_array_replace</span><span class="p">(</span><span class="n">arr</span><span class="p">,</span> <span class="n">repl</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;yes&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">&quot;no&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">}):</span>
<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">repl</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">arr</span><span class="p">[</span><span class="n">arr</span> <span class="o">==</span> <span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span>
<div class="viewcode-block" id="fetch_lequa2022">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.datasets.fetch_lequa2022">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">fetch_lequa2022</span><span class="p">(</span><span class="n">task</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">fetch_lequa2022</span><span class="p">(</span><span class="n">task</span><span class="p">,</span> <span class="n">data_home</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="sd"> Loads the official datasets provided for the `LeQua 2022 &lt;https://lequa2022.github.io/index&gt;`_ competition.</span>
<span class="sd"> In brief, there are 4 tasks (T1A, T1B, T2A, T2B) having to do with text quantification</span>
@ -1244,7 +1246,7 @@
<span class="sd"> that return a series of samples stored in a directory which are labelled by prevalence.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data._lequa</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_raw_documents</span><span class="p">,</span> <span class="n">load_vector_documents_2022</span><span class="p">,</span> <span class="n">SamplesFromDir</span>
<span class="kn">from</span> <span class="nn">quapy.data._lequa</span> <span class="kn">import</span> <span class="n">load_raw_documents</span><span class="p">,</span> <span class="n">load_vector_documents_2022</span><span class="p">,</span> <span class="n">SamplesFromDir</span>
<span class="k">assert</span> <span class="n">task</span> <span class="ow">in</span> <span class="n">LEQUA2022_TASKS</span><span class="p">,</span> \
<span class="sa">f</span><span class="s1">&#39;Unknown task </span><span class="si">{</span><span class="n">task</span><span class="si">}</span><span class="s1">. Valid ones are </span><span class="si">{</span><span class="n">LEQUA2022_TASKS</span><span class="si">}</span><span class="s1">&#39;</span>
@ -1258,7 +1260,7 @@
<span class="n">lequa_dir</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">data_home</span><span class="p">,</span> <span class="s1">&#39;lequa2022&#39;</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="n">lequa_dir</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="k">def</span><span class="w"> </span><span class="nf">download_unzip_and_remove</span><span class="p">(</span><span class="n">unzipped_path</span><span class="p">,</span> <span class="n">url</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">download_unzip_and_remove</span><span class="p">(</span><span class="n">unzipped_path</span><span class="p">,</span> <span class="n">url</span><span class="p">):</span>
<span class="n">tmp_path</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">lequa_dir</span><span class="p">,</span> <span class="n">task</span> <span class="o">+</span> <span class="s1">&#39;_tmp.zip&#39;</span><span class="p">)</span>
<span class="n">download_file_if_not_exists</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">tmp_path</span><span class="p">)</span>
<span class="k">with</span> <span class="n">zipfile</span><span class="o">.</span><span class="n">ZipFile</span><span class="p">(</span><span class="n">tmp_path</span><span class="p">)</span> <span class="k">as</span> <span class="n">file</span><span class="p">:</span>
@ -1294,7 +1296,7 @@
<div class="viewcode-block" id="fetch_lequa2024">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.datasets.fetch_lequa2024">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">fetch_lequa2024</span><span class="p">(</span><span class="n">task</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">merge_T3</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">fetch_lequa2024</span><span class="p">(</span><span class="n">task</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">merge_T3</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"> Loads the official datasets provided for the `LeQua 2024 &lt;https://lequa2024.github.io/index&gt;`_ competition.</span>
<span class="sd"> LeQua 2024 defines four tasks (T1, T2, T3, T4) related to the problem of quantification;</span>
@ -1325,7 +1327,7 @@
<span class="sd"> that return a series of samples stored in a directory which are labelled by prevalence.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data._lequa</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_vector_documents_2024</span><span class="p">,</span> <span class="n">SamplesFromDir</span><span class="p">,</span> <span class="n">LabelledCollectionsFromDir</span>
<span class="kn">from</span> <span class="nn">quapy.data._lequa</span> <span class="kn">import</span> <span class="n">load_vector_documents_2024</span><span class="p">,</span> <span class="n">SamplesFromDir</span><span class="p">,</span> <span class="n">LabelledCollectionsFromDir</span>
<span class="k">assert</span> <span class="n">task</span> <span class="ow">in</span> <span class="n">LEQUA2024_TASKS</span><span class="p">,</span> \
<span class="sa">f</span><span class="s1">&#39;Unknown task </span><span class="si">{</span><span class="n">task</span><span class="si">}</span><span class="s1">. Valid ones are </span><span class="si">{</span><span class="n">LEQUA2024_TASKS</span><span class="si">}</span><span class="s1">&#39;</span>
@ -1344,7 +1346,7 @@
<span class="n">lequa_dir</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">data_home</span><span class="p">,</span> <span class="s1">&#39;lequa2024&#39;</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="n">lequa_dir</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="k">def</span><span class="w"> </span><span class="nf">download_unzip_and_remove</span><span class="p">(</span><span class="n">unzipped_path</span><span class="p">,</span> <span class="n">url</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">download_unzip_and_remove</span><span class="p">(</span><span class="n">unzipped_path</span><span class="p">,</span> <span class="n">url</span><span class="p">):</span>
<span class="n">tmp_path</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">lequa_dir</span><span class="p">,</span> <span class="n">task</span> <span class="o">+</span> <span class="s1">&#39;_tmp.zip&#39;</span><span class="p">)</span>
<span class="n">download_file_if_not_exists</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">tmp_path</span><span class="p">)</span>
<span class="k">with</span> <span class="n">zipfile</span><span class="o">.</span><span class="n">ZipFile</span><span class="p">(</span><span class="n">tmp_path</span><span class="p">)</span> <span class="k">as</span> <span class="n">file</span><span class="p">:</span>
@ -1384,7 +1386,7 @@
<div class="viewcode-block" id="fetch_IFCB">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.datasets.fetch_IFCB">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">fetch_IFCB</span><span class="p">(</span><span class="n">single_sample_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">for_model_selection</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">fetch_IFCB</span><span class="p">(</span><span class="n">single_sample_train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">for_model_selection</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">data_home</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="sd"> Loads the IFCB dataset for quantification from `Zenodo &lt;https://zenodo.org/records/10036244&gt;`_ (for more</span>
<span class="sd"> information on this dataset, please follow the zenodo link).</span>
@ -1409,7 +1411,7 @@
<span class="sd"> i.e., a sampling protocol that returns a series of samples labelled by prevalence.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data._ifcb</span><span class="w"> </span><span class="kn">import</span> <span class="n">IFCBTrainSamplesFromDir</span><span class="p">,</span> <span class="n">IFCBTestSamples</span><span class="p">,</span> <span class="n">get_sample_list</span><span class="p">,</span> <span class="n">generate_modelselection_split</span>
<span class="kn">from</span> <span class="nn">quapy.data._ifcb</span> <span class="kn">import</span> <span class="n">IFCBTrainSamplesFromDir</span><span class="p">,</span> <span class="n">IFCBTestSamples</span><span class="p">,</span> <span class="n">get_sample_list</span><span class="p">,</span> <span class="n">generate_modelselection_split</span>
<span class="k">if</span> <span class="n">data_home</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">data_home</span> <span class="o">=</span> <span class="n">get_quapy_home</span><span class="p">()</span>
@ -1421,7 +1423,7 @@
<span class="n">ifcb_dir</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">data_home</span><span class="p">,</span> <span class="s1">&#39;ifcb&#39;</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="n">ifcb_dir</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="k">def</span><span class="w"> </span><span class="nf">download_unzip_and_remove</span><span class="p">(</span><span class="n">unzipped_path</span><span class="p">,</span> <span class="n">url</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">download_unzip_and_remove</span><span class="p">(</span><span class="n">unzipped_path</span><span class="p">,</span> <span class="n">url</span><span class="p">):</span>
<span class="n">tmp_path</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">ifcb_dir</span><span class="p">,</span> <span class="s1">&#39;ifcb_tmp.zip&#39;</span><span class="p">)</span>
<span class="n">download_file_if_not_exists</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">tmp_path</span><span class="p">)</span>
<span class="k">with</span> <span class="n">zipfile</span><span class="o">.</span><span class="n">ZipFile</span><span class="p">(</span><span class="n">tmp_path</span><span class="p">)</span> <span class="k">as</span> <span class="n">file</span><span class="p">:</span>
@ -1466,7 +1468,7 @@
<span class="k">def</span><span class="w"> </span><span class="nf">_fetch_image_embedding_splits</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">embedding</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">LabelledCollection</span><span class="p">,</span><span class="n">LabelledCollection</span><span class="p">,</span><span class="n">LabelledCollection</span><span class="p">]:</span>
<span class="k">def</span> <span class="nf">_fetch_image_embedding_splits</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">embedding</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">LabelledCollection</span><span class="p">,</span><span class="n">LabelledCollection</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="sd"> Loads a pre-generated embedding set (train, val, or test) of an image dataset from `Zenodo &lt;https://zenodo.org/records/21131944&gt;`_.</span>
<span class="sd"> </span>
@ -1496,7 +1498,7 @@
<span class="n">trained_network</span> <span class="o">=</span> <span class="n">dataset_network</span><span class="p">[</span><span class="n">dataset_name</span><span class="p">]</span>
<span class="k">def</span><span class="w"> </span><span class="nf">download_embedding_npz</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">trained_network</span><span class="p">,</span> <span class="n">embedding</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">download_embedding_npz</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">trained_network</span><span class="p">,</span> <span class="n">embedding</span><span class="p">):</span>
<span class="n">target_file</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">dataset_name</span><span class="si">}</span><span class="s1">_</span><span class="si">{</span><span class="n">trained_network</span><span class="si">}</span><span class="s1">_</span><span class="si">{</span><span class="n">embedding</span><span class="si">}</span><span class="s1">.npz&#39;</span>
<span class="n">URL</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39;https://zenodo.org/records/21131944/files/</span><span class="si">{</span><span class="n">target_file</span><span class="si">}</span><span class="s1">&#39;</span>
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">join</span><span class="p">(</span><span class="n">data_home</span><span class="p">,</span> <span class="s1">&#39;image&#39;</span><span class="p">),</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
@ -1512,14 +1514,12 @@
<span class="n">val</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">embedding_dict</span><span class="p">[</span><span class="s1">&#39;val&#39;</span><span class="p">],</span> <span class="n">labels_dict</span><span class="p">[</span><span class="s1">&#39;val&#39;</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">embedding_dict</span><span class="p">[</span><span class="s1">&#39;test&#39;</span><span class="p">],</span> <span class="n">labels_dict</span><span class="p">[</span><span class="s1">&#39;test&#39;</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="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">train</span><span class="p">)</span><span class="si">}</span><span class="s1"> | </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">val</span><span class="p">)</span><span class="si">}</span><span class="s1"> | </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">test</span><span class="p">)</span><span class="si">}</span><span class="s1"> | </span><span class="si">{</span><span class="n">train</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="si">}</span><span class="s1"> | </span><span class="si">{</span><span class="n">train</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="n">train</span><span class="o">.</span><span class="n">n_classes</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="k">return</span> <span class="n">train</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="n">test</span>
<div class="viewcode-block" id="fetch_image_embeddings">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.datasets.fetch_image_embeddings">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">fetch_image_embeddings</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">embedding</span><span class="p">,</span> <span class="n">heldout_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Dataset</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">fetch_image_embeddings</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">embedding</span><span class="p">,</span> <span class="n">heldout_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">data_home</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Dataset</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Loads an image dataset with pre-generated embeddings. Available datasets include:</span>
@ -1556,14 +1556,6 @@
<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="c1">#train, val, test = _fetch_image_embedding_splits(dataset_name=&#39;mnist&#39;, embedding=&#39;logits&#39;)</span>
<span class="c1">#print(train)</span>
<span class="c1">#print(val)</span>
<span class="c1">#print(test)</span>
<span class="n">dataset</span> <span class="o">=</span> <span class="n">fetch_image_embeddings</span><span class="p">(</span><span class="n">dataset_name</span><span class="o">=</span><span class="s1">&#39;svhn&#39;</span><span class="p">,</span> <span class="n">embedding</span><span class="o">=</span><span class="s1">&#39;features&#39;</span><span class="p">,</span> <span class="n">heldout_only</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span>
</pre></div>
</article>

534
docs/build/html/_modules/quapy/data/reader.html vendored
View File

@ -1,87 +1,385 @@
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<h1>Source code for quapy.data.reader</h1><div class="highlight"><pre>
<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="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="w"> </span><span class="nn">tqdm</span><span class="w"> </span><span class="kn">import</span> <span class="n">tqdm</span>
<span></span><span class="kn">import</span> <span class="nn">logging</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="nn">scipy.sparse</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>
<div class="viewcode-block" id="from_text">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.reader.from_text">[docs]</a>
<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="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="w"> </span><span class="sd">&quot;&quot;&quot;</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>
@ -108,14 +406,14 @@
<span class="n">all_sentences</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sentence</span><span class="p">)</span>
<span class="n">all_labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">label</span><span class="p">)</span>
<span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;format error in </span><span class="si">{</span><span class="n">line</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;format error in </span><span class="si">{</span><span class="n">line</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="k">return</span> <span class="n">all_sentences</span><span class="p">,</span> <span class="n">all_labels</span></div>
<div class="viewcode-block" id="from_sparse">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.reader.from_sparse">[docs]</a>
<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="k">def</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="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>
@ -124,7 +422,7 @@
<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="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="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="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="k">return</span> <span class="n">col</span><span class="p">,</span> <span class="n">val</span>
@ -152,7 +450,7 @@
<div class="viewcode-block" id="from_csv">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.reader.from_csv">[docs]</a>
<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="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="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"> File format &lt;label&gt;,&lt;feat1&gt;,&lt;feat2&gt;,...,&lt;featn&gt;\n</span>
@ -175,7 +473,7 @@
<div class="viewcode-block" id="reindex_labels">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.reader.reindex_labels">[docs]</a>
<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="k">def</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="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>
@ -198,7 +496,7 @@
<div class="viewcode-block" id="binarize">
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.reader.binarize">[docs]</a>
<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="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="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>
@ -218,31 +516,75 @@
</pre></div>
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<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 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.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">f1_score</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">numpy</span> <span class="k">as</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">import</span> <span class="nn">quapy</span> <span class="k">as</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="w"> </span><span class="nf">from_name</span><span class="p">(</span><span class="n">err_name</span><span class="p">):</span>
<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>
<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>
@ -98,7 +394,7 @@
<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="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>
<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"> defined as :math:`\\frac{2tp}{2tp+fp+fn}`, with `tp`, `fp`, and `fn` standing</span>
@ -116,7 +412,7 @@
<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="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>
<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"> for true positives, false positives, false negatives, and true negatives,</span>
@ -132,7 +428,7 @@
<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="k">def</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="sd"> :param prevs_true: array-like of shape `(n_samples, n_classes,)` with the true prevalence values</span>
@ -146,7 +442,7 @@
<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="k">def</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="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>
@ -165,7 +461,7 @@
<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="k">def</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="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>
@ -185,7 +481,7 @@
<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="k">def</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="sd"> :param prevs_true: array-like of shape `(n_samples, n_classes,)` with the true prevalence values</span>
@ -199,7 +495,7 @@
<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="k">def</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="sd"> :param prevs_true: array-like of shape `(n_samples, n_classes,)` with the</span>
@ -214,7 +510,7 @@
<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="k">def</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="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>
@ -233,7 +529,7 @@
<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="k">def</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>
@ -269,7 +565,7 @@
<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="k">def</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>
@ -285,7 +581,7 @@
<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="k">def</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>
@ -298,7 +594,7 @@
<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="kn">from</span> <span class="nn">quapy.functional</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>
@ -307,7 +603,7 @@
<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="k">def</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>
@ -324,7 +620,7 @@
<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="k">def</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="sd"> sample pairs. The distributions are smoothed using the `eps` factor</span>
<span class="sd"> (see :meth:`quapy.error.smooth`).</span>
@ -345,7 +641,7 @@
<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="k">def</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="sd"> Kullback-Leibler divergence between two prevalence distributions :math:`p` and :math:`\\hat{p}`</span>
<span class="sd"> is computed as</span>
@ -371,7 +667,7 @@
<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="k">def</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="sd"> across the sample pairs. The distributions are smoothed using the `eps` factor</span>
<span class="sd"> (see :meth:`quapy.error.smooth`).</span>
@ -391,7 +687,7 @@
<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="k">def</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="sd"> Normalized Kullback-Leibler divergence between two prevalence distributions :math:`p` and</span>
<span class="sd"> :math:`\\hat{p}` is computed as</span>
@ -415,7 +711,7 @@
<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="k">def</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="sd"> the sample pairs. The distributions are smoothed using the `eps` factor (see</span>
<span class="sd"> :meth:`quapy.error.smooth`).</span>
@ -436,7 +732,7 @@
<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="k">def</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="sd"> Relative absolute error between two prevalence vectors :math:`p` and :math:`\\hat{p}`</span>
<span class="sd"> is computed as</span>
@ -462,7 +758,7 @@
<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="k">def</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="sd"> Relative absolute error between two prevalence vectors :math:`p` and :math:`\\hat{p}`</span>
<span class="sd"> is computed as</span>
@ -490,7 +786,7 @@
<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="k">def</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="sd"> the sample pairs. The distributions are smoothed using the `eps` factor (see</span>
<span class="sd"> :meth:`quapy.error.smooth`).</span>
@ -511,7 +807,7 @@
<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="k">def</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>
@ -529,7 +825,7 @@
<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="k">def</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>
@ -549,7 +845,7 @@
<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="k">def</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>
@ -562,7 +858,7 @@
<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="k">def</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>
@ -582,7 +878,7 @@
<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="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>
<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"> \\displaystyle\\sum_{y\\in \\mathcal{Y}}p(y)}`</span>
@ -597,7 +893,7 @@
<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">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">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="k">if</span> <span class="n">sample_size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
@ -634,31 +930,75 @@
<span class="n">match_distance</span> <span class="o">=</span> <span class="n">md</span>
</pre></div>
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@ -114,8 +116,14 @@
<img src="../../_static/quapy_logo.png" class="logo__image only-light" alt="QuaPy: A Python-based open-source framework for quantification 0.2.1 documentation - Home"/>
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@ -131,7 +139,7 @@
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@ -332,17 +370,17 @@
<article class="bd-article">
<h1>Source code for quapy.functional</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">warnings</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">ABC</span><span class="p">,</span> <span class="n">abstractmethod</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">from</span><span class="w"> </span><span class="nn">functools</span><span class="w"> </span><span class="kn">import</span> <span class="n">lru_cache</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">Literal</span><span class="p">,</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">numpy.typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">ArrayLike</span>
<span></span><span class="kn">import</span> <span class="nn">warnings</span>
<span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">ABC</span><span class="p">,</span> <span class="n">abstractmethod</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">lru_cache</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Literal</span><span class="p">,</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Callable</span>
<span class="kn">from</span> <span class="nn">numpy.typing</span> <span class="kn">import</span> <span class="n">ArrayLike</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">scipy</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">scipy</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">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</span> <span class="k">as</span> <span class="nn">qp</span>
<span class="c1"># ------------------------------------------------------------------------------------------</span>
@ -351,7 +389,7 @@
<div class="viewcode-block" id="classes_from_labels">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.classes_from_labels">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">classes_from_labels</span><span class="p">(</span><span class="n">labels</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">classes_from_labels</span><span class="p">(</span><span class="n">labels</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Obtains a np.ndarray with the (sorted) classes</span>
<span class="sd"> :param labels: array-like with the instances&#39; labels</span>
@ -365,7 +403,7 @@
<div class="viewcode-block" id="num_classes_from_labels">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.num_classes_from_labels">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">num_classes_from_labels</span><span class="p">(</span><span class="n">labels</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">num_classes_from_labels</span><span class="p">(</span><span class="n">labels</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Obtains the number of classes from an array-like of instance&#39;s labels</span>
<span class="sd"> :param labels: array-like with the instances&#39; labels</span>
@ -380,7 +418,7 @@
<div class="viewcode-block" id="counts_from_labels">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.counts_from_labels">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">counts_from_labels</span><span class="p">(</span><span class="n">labels</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span> <span class="n">classes</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">counts_from_labels</span><span class="p">(</span><span class="n">labels</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span> <span class="n">classes</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">)</span> <span class="o">-&gt;</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="sd"> Computes the raw count values from a vector of labels.</span>
@ -401,7 +439,7 @@
<div class="viewcode-block" id="prevalence_from_labels">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.prevalence_from_labels">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">prevalence_from_labels</span><span class="p">(</span><span class="n">labels</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span> <span class="n">classes</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">prevalence_from_labels</span><span class="p">(</span><span class="n">labels</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span> <span class="n">classes</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Computes the prevalence values from a vector of labels.</span>
@ -419,7 +457,7 @@
<div class="viewcode-block" id="prevalence_from_probabilities">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.prevalence_from_probabilities">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">prevalence_from_probabilities</span><span class="p">(</span><span class="n">posteriors</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span> <span class="n">binarize</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">prevalence_from_probabilities</span><span class="p">(</span><span class="n">posteriors</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span> <span class="n">binarize</span><span class="p">:</span> <span class="nb">bool</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"> Returns a vector of prevalence values from a matrix of posterior probabilities.</span>
@ -443,7 +481,7 @@
<div class="viewcode-block" id="num_prevalence_combinations">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.num_prevalence_combinations">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">num_prevalence_combinations</span><span class="p">(</span><span class="n">n_prevpoints</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">n_repeats</span><span class="p">:</span><span class="nb">int</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">num_prevalence_combinations</span><span class="p">(</span><span class="n">n_prevpoints</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">n_repeats</span><span class="p">:</span><span class="nb">int</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Computes the number of valid prevalence combinations in the n_classes-dimensional simplex if `n_prevpoints` equally</span>
<span class="sd"> distant prevalence values are generated and `n_repeats` repetitions are requested.</span>
@ -472,7 +510,7 @@
<div class="viewcode-block" id="get_nprevpoints_approximation">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.get_nprevpoints_approximation">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">get_nprevpoints_approximation</span><span class="p">(</span><span class="n">combinations_budget</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">n_repeats</span><span class="p">:</span><span class="nb">int</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">get_nprevpoints_approximation</span><span class="p">(</span><span class="n">combinations_budget</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">n_repeats</span><span class="p">:</span><span class="nb">int</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Searches for the largest number of (equidistant) prevalence points to define for each of the `n_classes` classes so</span>
<span class="sd"> that the number of valid prevalence values generated as combinations of prevalence points (points in a</span>
@ -500,7 +538,7 @@
<div class="viewcode-block" id="as_binary_prevalence">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.as_binary_prevalence">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">as_binary_prevalence</span><span class="p">(</span><span class="n">positive_prevalence</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="n">ArrayLike</span><span class="p">],</span> <span class="n">clip_if_necessary</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">as_binary_prevalence</span><span class="p">(</span><span class="n">positive_prevalence</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="n">ArrayLike</span><span class="p">],</span> <span class="n">clip_if_necessary</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</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="sd"> Helper that, given a float representing the prevalence for the positive class, returns a np.ndarray of two</span>
<span class="sd"> values representing a binary distribution.</span>
@ -522,7 +560,7 @@
<div class="viewcode-block" id="strprev">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.strprev">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">strprev</span><span class="p">(</span><span class="n">prevalences</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span> <span class="n">prec</span><span class="p">:</span> <span class="nb">int</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">strprev</span><span class="p">(</span><span class="n">prevalences</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span> <span class="n">prec</span><span class="p">:</span> <span class="nb">int</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns a string representation for a prevalence vector. E.g.,</span>
@ -539,7 +577,7 @@
<div class="viewcode-block" id="check_prevalence_vector">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.check_prevalence_vector">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">check_prevalence_vector</span><span class="p">(</span><span class="n">prevalences</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span> <span class="n">raise_exception</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">tolerance</span><span class="p">:</span> <span class="nb">float</span><span class="o">=</span><span class="mf">1e-08</span><span class="p">,</span> <span class="n">aggr</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">check_prevalence_vector</span><span class="p">(</span><span class="n">prevalences</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span> <span class="n">raise_exception</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">tolerance</span><span class="p">:</span> <span class="nb">float</span><span class="o">=</span><span class="mf">1e-08</span><span class="p">,</span> <span class="n">aggr</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="sd"> Checks that `prevalences` is a valid prevalence vector, i.e., it contains values in [0,1] and</span>
<span class="sd"> the values sum up to 1. In other words, verifies that the `prevalences` vectors lies in the</span>
@ -581,7 +619,7 @@
<div class="viewcode-block" id="uniform_prevalence">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.uniform_prevalence">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">uniform_prevalence</span><span class="p">(</span><span class="n">n_classes</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">uniform_prevalence</span><span class="p">(</span><span class="n">n_classes</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns a vector representing the uniform distribution for `n_classes`</span>
@ -597,7 +635,7 @@
<div class="viewcode-block" id="normalize_prevalence">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.normalize_prevalence">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">normalize_prevalence</span><span class="p">(</span><span class="n">prevalences</span><span class="p">:</span> <span class="n">ArrayLike</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">def</span> <span class="nf">normalize_prevalence</span><span class="p">(</span><span class="n">prevalences</span><span class="p">:</span> <span class="n">ArrayLike</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="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Normalizes a vector or matrix of prevalence values. The normalization consists of applying a L1 normalization in</span>
<span class="sd"> cases in which the prevalence values are not all-zeros, and to convert the prevalence values into `1/n_classes` in</span>
@ -640,7 +678,7 @@
<div class="viewcode-block" id="l1_norm">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.l1_norm">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">l1_norm</span><span class="p">(</span><span class="n">prevalences</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">l1_norm</span><span class="p">(</span><span class="n">prevalences</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">)</span> <span class="o">-&gt;</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="sd"> Applies L1 normalization to the `unnormalized_arr` so that it becomes a valid prevalence</span>
<span class="sd"> vector. Zero vectors are mapped onto the uniform distribution. Raises an exception if</span>
@ -666,7 +704,7 @@
<div class="viewcode-block" id="clip">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.clip">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">clip</span><span class="p">(</span><span class="n">prevalences</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">clip</span><span class="p">(</span><span class="n">prevalences</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">)</span> <span class="o">-&gt;</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="sd"> Clips the values in [0,1] and then applies the L1 normalization.</span>
@ -681,7 +719,7 @@
<div class="viewcode-block" id="projection_simplex_sort">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.projection_simplex_sort">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">projection_simplex_sort</span><span class="p">(</span><span class="n">unnormalized_arr</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">projection_simplex_sort</span><span class="p">(</span><span class="n">unnormalized_arr</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">)</span> <span class="o">-&gt;</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;Projects a point onto the probability simplex.</span>
<span class="sd"> The code is adapted from Mathieu Blondel&#39;s BSD-licensed</span>
@ -709,7 +747,7 @@
<div class="viewcode-block" id="softmax">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.softmax">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">softmax</span><span class="p">(</span><span class="n">prevalences</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">softmax</span><span class="p">(</span><span class="n">prevalences</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">)</span> <span class="o">-&gt;</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="sd"> Applies the softmax function to all vectors even if the original vectors were valid distributions.</span>
<span class="sd"> If you want to leave valid vectors untouched, use condsoftmax instead.</span>
@ -724,7 +762,7 @@
<div class="viewcode-block" id="condsoftmax">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.condsoftmax">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">condsoftmax</span><span class="p">(</span><span class="n">prevalences</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">condsoftmax</span><span class="p">(</span><span class="n">prevalences</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">)</span> <span class="o">-&gt;</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="sd"> Applies the softmax function only to vectors that do not represent valid distributions.</span>
@ -749,7 +787,7 @@
<div class="viewcode-block" id="HellingerDistance">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.HellingerDistance">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">HellingerDistance</span><span class="p">(</span><span class="n">P</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">Q</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="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">HellingerDistance</span><span class="p">(</span><span class="n">P</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">Q</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="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="sd"> Computes the Hellingher Distance (HD) between (discretized) distributions `P` and `Q`.</span>
<span class="sd"> The HD for two discrete distributions of `k` bins is defined as:</span>
@ -767,7 +805,7 @@
<div class="viewcode-block" id="TopsoeDistance">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.TopsoeDistance">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">TopsoeDistance</span><span class="p">(</span><span class="n">P</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">Q</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">epsilon</span><span class="p">:</span> <span class="nb">float</span><span class="o">=</span><span class="mf">1e-20</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">TopsoeDistance</span><span class="p">(</span><span class="n">P</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">Q</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">epsilon</span><span class="p">:</span> <span class="nb">float</span><span class="o">=</span><span class="mf">1e-20</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Topsoe distance between two (discretized) distributions `P` and `Q`.</span>
<span class="sd"> The Topsoe distance for two discrete distributions of `k` bins is defined as:</span>
@ -786,7 +824,7 @@
<div class="viewcode-block" id="get_divergence">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.get_divergence">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">get_divergence</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="k">def</span> <span class="nf">get_divergence</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="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Guarantees that the divergence received as argument is a function. That is, if this argument is already</span>
<span class="sd"> a callable, then it is returned, if it is instead a string, then tries to instantiate the corresponding</span>
@ -815,7 +853,7 @@
<div class="viewcode-block" id="argmin_prevalence">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.argmin_prevalence">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">argmin_prevalence</span><span class="p">(</span><span class="n">loss</span><span class="p">:</span> <span class="n">Callable</span><span class="p">,</span>
<span class="k">def</span> <span class="nf">argmin_prevalence</span><span class="p">(</span><span class="n">loss</span><span class="p">:</span> <span class="n">Callable</span><span class="p">,</span>
<span class="n">n_classes</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
<span class="n">method</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;optim_minimize&quot;</span><span class="p">,</span> <span class="s2">&quot;linear_search&quot;</span><span class="p">,</span> <span class="s2">&quot;ternary_search&quot;</span><span class="p">]</span><span class="o">=</span><span class="s1">&#39;optim_minimize&#39;</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
@ -842,7 +880,7 @@
<div class="viewcode-block" id="optim_minimize">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.optim_minimize">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">optim_minimize</span><span class="p">(</span><span class="n">loss</span><span class="p">:</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">return_loss</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">optim_minimize</span><span class="p">(</span><span class="n">loss</span><span class="p">:</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">return_loss</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"> Searches for the optimal prevalence values, i.e., an `n_classes`-dimensional vector of the (`n_classes`-1)-simplex</span>
<span class="sd"> that yields the smallest lost. This optimization is carried out by means of a constrained search using scipy&#39;s</span>
@ -854,7 +892,7 @@
<span class="sd"> :return: (ndarray) the best prevalence vector found or a tuple which also contains the value of the loss</span>
<span class="sd"> if return_loss=True</span>
<span class="sd"> &quot;&quot;&quot;</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">optimize</span>
<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">optimize</span>
<span class="c1"># the initial point is set as the uniform distribution</span>
<span class="n">uniform_distribution</span> <span class="o">=</span> <span class="n">uniform_prevalence</span><span class="p">(</span><span class="n">n_classes</span><span class="o">=</span><span class="n">n_classes</span><span class="p">)</span>
@ -873,7 +911,7 @@
<div class="viewcode-block" id="linear_search">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.linear_search">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">linear_search</span><span class="p">(</span><span class="n">loss</span><span class="p">:</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">linear_search</span><span class="p">(</span><span class="n">loss</span><span class="p">:</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Performs a linear search for the best prevalence value in binary problems. The search is carried out by exploring</span>
<span class="sd"> the range [0,1] stepping by 0.01. This search is inefficient, and is added only for completeness (some of the</span>
@ -897,7 +935,7 @@
<div class="viewcode-block" id="ternary_search">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.ternary_search">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">ternary_search</span><span class="p">(</span><span class="n">loss</span><span class="p">:</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">ternary_search</span><span class="p">(</span><span class="n">loss</span><span class="p">:</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Performs a ternary search for the best prevalence value in binary problems.</span>
<span class="sd"> This search assumes the loss is unimodal over the interval [0,1].</span>
@ -933,7 +971,7 @@
<div class="viewcode-block" id="prevalence_linspace">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.prevalence_linspace">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">prevalence_linspace</span><span class="p">(</span><span class="n">grid_points</span><span class="p">:</span><span class="nb">int</span><span class="o">=</span><span class="mi">21</span><span class="p">,</span> <span class="n">repeats</span><span class="p">:</span><span class="nb">int</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">smooth_limits_epsilon</span><span class="p">:</span><span class="nb">float</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">prevalence_linspace</span><span class="p">(</span><span class="n">grid_points</span><span class="p">:</span><span class="nb">int</span><span class="o">=</span><span class="mi">21</span><span class="p">,</span> <span class="n">repeats</span><span class="p">:</span><span class="nb">int</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">smooth_limits_epsilon</span><span class="p">:</span><span class="nb">float</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span> <span class="o">-&gt;</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="sd"> Produces an array of uniformly separated values of prevalence.</span>
<span class="sd"> By default, produces an array of 21 prevalence values, with</span>
@ -958,7 +996,7 @@
<div class="viewcode-block" id="uniform_prevalence_sampling">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.uniform_prevalence_sampling">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">uniform_prevalence_sampling</span><span class="p">(</span><span class="n">n_classes</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">size</span><span class="p">:</span> <span class="nb">int</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
<span class="k">def</span> <span class="nf">uniform_prevalence_sampling</span><span class="p">(</span><span class="n">n_classes</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">size</span><span class="p">:</span> <span class="nb">int</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">-&gt;</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="sd"> Implements the `Kraemer algorithm &lt;http://www.cs.cmu.edu/~nasmith/papers/smith+tromble.tr04.pdf&gt;`_</span>
<span class="sd"> for sampling uniformly at random from the unit simplex. This implementation is adapted from this</span>
@ -994,7 +1032,7 @@
<div class="viewcode-block" id="solve_adjustment_binary">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.solve_adjustment_binary">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">solve_adjustment_binary</span><span class="p">(</span><span class="n">prevalence_estim</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span> <span class="n">tpr</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">fpr</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">clip</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">solve_adjustment_binary</span><span class="p">(</span><span class="n">prevalence_estim</span><span class="p">:</span> <span class="n">ArrayLike</span><span class="p">,</span> <span class="n">tpr</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">fpr</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">clip</span><span class="p">:</span> <span class="nb">bool</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="sd"> Implements the adjustment of ACC and PACC for the binary case. The adjustment for a prevalence estimate of the</span>
<span class="sd"> positive class `p` comes down to computing:</span>
@ -1021,7 +1059,7 @@
<div class="viewcode-block" id="solve_adjustment">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.solve_adjustment">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">solve_adjustment</span><span class="p">(</span>
<span class="k">def</span> <span class="nf">solve_adjustment</span><span class="p">(</span>
<span class="n">class_conditional_rates</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">unadjusted_counts</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">method</span><span class="p">:</span> <span class="n">Literal</span><span class="p">[</span><span class="s2">&quot;inversion&quot;</span><span class="p">,</span> <span class="s2">&quot;invariant-ratio&quot;</span><span class="p">],</span>
@ -1067,14 +1105,18 @@
<span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s2">&quot;inversion&quot;</span><span class="p">:</span>
<span class="k">pass</span> <span class="c1"># We leave A and B unchanged</span>
<span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s2">&quot;invariant-ratio&quot;</span><span class="p">:</span>
<span class="c1"># Change the last equation to replace it with the normalization condition</span>
<span class="c1"># Change the last equation to replace it with the normalization condition;</span>
<span class="c1"># copy first so this does not mutate the caller&#39;s arrays (np.asarray above</span>
<span class="c1"># returns the same object, not a copy, when the input is already float64)</span>
<span class="n">A</span> <span class="o">=</span> <span class="n">A</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">B</span> <span class="o">=</span> <span class="n">B</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="n">A</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="mf">1.0</span>
<span class="n">B</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">1.0</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="s2">&quot;unknown </span><span class="si">{</span><span class="n">method</span><span class="si">=}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="k">if</span> <span class="n">solver</span> <span class="o">==</span> <span class="s2">&quot;minimize&quot;</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="k">def</span> <span class="nf">loss</span><span class="p">(</span><span class="n">prev</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">A</span> <span class="o">@</span> <span class="n">prev</span> <span class="o">-</span> <span class="n">B</span><span class="p">)</span>
<span class="k">return</span> <span class="n">optim_minimize</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">n_classes</span><span class="o">=</span><span class="n">A</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="k">elif</span> <span class="n">solver</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;exact-raise&quot;</span><span class="p">,</span> <span class="s2">&quot;exact-cc&quot;</span><span class="p">]:</span>
@ -1102,7 +1144,7 @@
<div class="viewcode-block" id="CompositionalTransformation">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.CompositionalTransformation">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">CompositionalTransformation</span><span class="p">(</span><span class="n">ABC</span><span class="p">):</span>
<span class="k">class</span> <span class="nc">CompositionalTransformation</span><span class="p">(</span><span class="n">ABC</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Abstract class of transformations for compositional data.</span>
<span class="sd"> &quot;&quot;&quot;</span>
@ -1110,13 +1152,13 @@
<span class="n">EPSILON</span> <span class="o">=</span> <span class="mf">1e-12</span>
<span class="nd">@abstractmethod</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">X</span><span class="p">):</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">X</span><span class="p">):</span>
<span class="o">...</span>
<div class="viewcode-block" id="CompositionalTransformation.inverse">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.CompositionalTransformation.inverse">[docs]</a>
<span class="nd">@abstractmethod</span>
<span class="k">def</span><span class="w"> </span><span class="nf">inverse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">Z</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">inverse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">Z</span><span class="p">):</span>
<span class="o">...</span></div>
</div>
@ -1124,12 +1166,12 @@
<div class="viewcode-block" id="CLRtransformation">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.CLRtransformation">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">CLRtransformation</span><span class="p">(</span><span class="n">CompositionalTransformation</span><span class="p">):</span>
<span class="k">class</span> <span class="nc">CLRtransformation</span><span class="p">(</span><span class="n">CompositionalTransformation</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Centered log-ratio (CLR) transformation.</span>
<span class="sd"> &quot;&quot;&quot;</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">X</span><span class="p">):</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">X</span><span class="p">):</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">X</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">X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">EPSILON</span><span class="p">)</span>
<span class="n">geometric_mean</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">np</span><span class="o">.</span><span class="n">mean</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">X</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">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
@ -1137,7 +1179,7 @@
<div class="viewcode-block" id="CLRtransformation.inverse">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.CLRtransformation.inverse">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">inverse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">Z</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">inverse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">Z</span><span class="p">):</span>
<span class="k">return</span> <span class="n">scipy</span><span class="o">.</span><span class="n">special</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">Z</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>
@ -1145,12 +1187,12 @@
<div class="viewcode-block" id="ILRtransformation">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.ILRtransformation">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">ILRtransformation</span><span class="p">(</span><span class="n">CompositionalTransformation</span><span class="p">):</span>
<span class="k">class</span> <span class="nc">ILRtransformation</span><span class="p">(</span><span class="n">CompositionalTransformation</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Isometric log-ratio (ILR) transformation.</span>
<span class="sd"> &quot;&quot;&quot;</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">X</span><span class="p">):</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">X</span><span class="p">):</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">X</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">X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">EPSILON</span><span class="p">)</span>
<span class="n">basis</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_V</span><span class="p">(</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>
@ -1158,7 +1200,7 @@
<div class="viewcode-block" id="ILRtransformation.inverse">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.ILRtransformation.inverse">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">inverse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">Z</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">inverse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">Z</span><span class="p">):</span>
<span class="n">Z</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">Z</span><span class="p">)</span>
<span class="n">basis</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_V</span><span class="p">(</span><span class="n">Z</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="p">)</span>
<span class="n">logp</span> <span class="o">=</span> <span class="n">Z</span> <span class="o">@</span> <span class="n">basis</span>
@ -1169,7 +1211,7 @@
<div class="viewcode-block" id="ILRtransformation.get_V">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.ILRtransformation.get_V">[docs]</a>
<span class="nd">@lru_cache</span><span class="p">(</span><span class="n">maxsize</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">get_V</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">get_V</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
<span class="n">helmert</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">k</span><span class="p">,</span> <span class="n">k</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="mi">1</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
<span class="n">helmert</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
@ -1182,7 +1224,7 @@
<div class="viewcode-block" id="normalized_entropy">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.normalized_entropy">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">normalized_entropy</span><span class="p">(</span><span class="n">p</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">normalized_entropy</span><span class="p">(</span><span class="n">p</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Computes the normalized Shannon entropy of a prevalence vector.</span>
@ -1198,7 +1240,7 @@
<div class="viewcode-block" id="antagonistic_prevalence">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.antagonistic_prevalence">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">antagonistic_prevalence</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">strength</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">antagonistic_prevalence</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">strength</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Reflects a prevalence vector in ILR space and maps it back to the simplex.</span>
@ -1215,7 +1257,7 @@
<div class="viewcode-block" id="in_simplex">
<a class="viewcode-back" href="../../quapy.html#quapy.functional.in_simplex">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">in_simplex</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="mf">1e-8</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">in_simplex</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">atol</span><span class="o">=</span><span class="mf">1e-8</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Checks whether points lie in the probability simplex.</span>

126
docs/build/html/_modules/quapy/method/_kdey.html vendored
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@ -29,8 +29,9 @@
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@ -41,6 +42,7 @@
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<script src="../../../_static/sphinx_highlight.js?v=6ffebe34"></script>
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<script>DOCUMENTATION_OPTIONS.search_as_you_type = false;</script>
<link rel="index" title="Index" href="../../../genindex.html" />
@ -114,8 +116,14 @@
<img src="../../../_static/quapy_logo.png" class="logo__image only-light" alt="QuaPy: A Python-based open-source framework for quantification 0.2.1 documentation - Home"/>
<img src="../../../_static/quapy_logo_dark.png" class="logo__image only-dark pst-js-only" alt="QuaPy: A Python-based open-source framework for quantification 0.2.1 documentation - Home"/>
<p class="title logo__title">QuaPy</p>
</a></div>
@ -131,7 +139,7 @@
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<a class="nav-link nav-internal" href="../../../index.html">
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@ -183,6 +191,21 @@
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aria-label="Icon Links">
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@ -229,7 +252,7 @@
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@ -272,6 +295,21 @@
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aria-label="Icon Links">
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@ -332,22 +370,22 @@
<article class="bd-article">
<h1>Source code for quapy.method._kdey</h1><div class="highlight"><pre>
<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="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="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="w"> </span><span class="nn">sklearn.neighbors</span><span class="w"> </span><span class="kn">import</span> <span class="n">KernelDensity</span>
<span></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="nn">numbers</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="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">KernelDensity</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.method._helper</span><span class="w"> </span><span class="kn">import</span> <span class="n">_labels_to_indices</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">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.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">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.method._helper</span> <span class="kn">import</span> <span class="n">_labels_to_indices</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="nn">quapy.functional</span> <span class="k">as</span> <span class="nn">F</span>
<span class="kn">from</span> <span class="nn">scipy.special</span> <span class="kn">import</span> <span class="n">logsumexp</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="w"> </span><span class="nc">KDEBase</span><span class="p">:</span>
<span class="k">class</span> <span class="nc">KDEBase</span><span class="p">:</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"> &quot;&quot;&quot;</span>
@ -356,7 +394,7 @@
<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="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="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="n">kernel</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Checks that the bandwidth parameter is correct</span>
@ -370,13 +408,13 @@
<span class="k">return</span> <span class="n">bandwidth</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">def</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="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="n">kernel</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Wraps the KDE function from scikit-learn.</span>
@ -392,7 +430,7 @@
<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="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> <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="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>
@ -411,7 +449,7 @@
<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="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">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="sd"> Returns an array containing the mixture components, i.e., the KDE functions for each class.</span>
@ -433,7 +471,7 @@
<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">def</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>
@ -445,7 +483,7 @@
<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="k">def</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>
@ -457,7 +495,7 @@
<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="k">def</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>
@ -466,7 +504,7 @@
<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">def</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>
@ -474,7 +512,7 @@
<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">def</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>
@ -484,7 +522,7 @@
<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="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>
<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"> the divergence measure to be minimized. This method was first proposed in the paper</span>
@ -526,7 +564,7 @@
<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="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="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="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">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="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">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>
@ -539,7 +577,7 @@
<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="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">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">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>
@ -552,7 +590,7 @@
<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="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>
<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"> of the data (i.e., that minimizes the negative log-likelihood)</span>
@ -569,14 +607,14 @@
<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="w"> </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="nf">neg_loglikelihood</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">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">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">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">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="k">def</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>
@ -588,7 +626,7 @@
<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="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>
<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"> the divergence measure to be minimized. This method was first proposed in the paper</span>
@ -633,7 +671,7 @@
<span class="sd"> :param montecarlo_trials: number of Monte Carlo trials (default 10000)</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">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="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="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">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="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>
@ -644,7 +682,7 @@
<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="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">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">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>
@ -663,7 +701,7 @@
<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="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>
<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="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>
@ -671,7 +709,7 @@
<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="s1">&#39;gaussian&#39;</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">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">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>
@ -687,7 +725,7 @@
<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="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="k">def</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="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>
@ -699,7 +737,7 @@
<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="k">class</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="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>
@ -736,13 +774,13 @@
<span class="sd"> :param bandwidth: float, the bandwidth of the Kernel</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">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="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="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="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">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>
<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="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>
<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"># two &quot;scalar matrices&quot; (h^2)*I each, so Sigma1+Sigma2 has scalar 2(h^2) (h is the bandwidth)</span>
@ -757,7 +795,7 @@
<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="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">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">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>
@ -789,7 +827,7 @@
<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="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>
<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">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ytr</span>
@ -809,7 +847,7 @@
<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="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="k">def</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="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>

68
docs/build/html/_modules/quapy/method/_neural.html vendored
View File

@ -29,7 +29,7 @@
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@ -370,23 +370,24 @@
<article class="bd-article">
<h1>Source code for quapy.method._neural</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">os</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">import</span><span class="w"> </span><span class="nn">random</span>
<span></span><span class="kn">import</span> <span class="nn">logging</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
<span class="kn">import</span> <span class="nn">random</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch.nn</span><span class="w"> </span><span class="kn">import</span> <span class="n">MSELoss</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">torch.nn.functional</span><span class="w"> </span><span class="kn">import</span> <span class="n">relu</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">MSELoss</span>
<span class="kn">from</span> <span class="nn">torch.nn.functional</span> <span class="kn">import</span> <span class="n">relu</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">UPP</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="o">*</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">EarlyStop</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="nn">quapy.protocol</span> <span class="kn">import</span> <span class="n">UPP</span>
<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">quapy.util</span> <span class="kn">import</span> <span class="n">EarlyStop</span>
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
<div class="viewcode-block" id="QuaNetTrainer">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetTrainer">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">QuaNetTrainer</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
<span class="k">class</span> <span class="nc">QuaNetTrainer</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="sd"> Implementation of `QuaNet &lt;https://dl.acm.org/doi/abs/10.1145/3269206.3269287&gt;`_, a neural network for</span>
<span class="sd"> quantification. This implementation uses `PyTorch &lt;https://pytorch.org/&gt;`_ and can take advantage of GPU</span>
@ -438,7 +439,7 @@
<span class="sd"> :param device: string, indicate &quot;cpu&quot; or &quot;cuda&quot;</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="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">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">sample_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
@ -491,7 +492,7 @@
<div class="viewcode-block" id="QuaNetTrainer.fit">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetTrainer.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="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="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Trains QuaNet.</span>
@ -549,7 +550,7 @@
<span class="n">order_by</span><span class="o">=</span><span class="mi">0</span> <span class="k">if</span> <span class="n">data</span><span class="o">.</span><span class="n">binary</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>
<span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">quanet_params</span>
<span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">quanet</span><span class="p">)</span>
<span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">quanet</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">optim</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">quanet</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lr</span><span class="p">)</span>
<span class="n">early_stop</span> <span class="o">=</span> <span class="n">EarlyStop</span><span class="p">(</span><span class="bp">self</span><span class="o">.</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>
@ -564,15 +565,16 @@
<span class="k">if</span> <span class="n">early_stop</span><span class="o">.</span><span class="n">IMPROVED</span><span class="p">:</span>
<span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">quanet</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="n">checkpoint</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">early_stop</span><span class="o">.</span><span class="n">STOP</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;training ended by patience exhausted; loading best model parameters in </span><span class="si">{</span><span class="n">checkpoint</span><span class="si">}</span><span class="s1"> &#39;</span>
<span class="sa">f</span><span class="s1">&#39;for epoch </span><span class="si">{</span><span class="n">early_stop</span><span class="o">.</span><span class="n">best_epoch</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
<span class="sa">f</span><span class="s1">&#39;training ended by patience exhausted; loading best model parameters in </span><span class="si">{</span><span class="n">checkpoint</span><span class="si">}</span><span class="s1"> &#39;</span>
<span class="sa">f</span><span class="s1">&#39;for epoch </span><span class="si">{</span><span class="n">early_stop</span><span class="o">.</span><span class="n">best_epoch</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">quanet</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">))</span>
<span class="k">break</span>
<span class="k">return</span> <span class="bp">self</span></div>
<span class="k">def</span><span class="w"> </span><span class="nf">_get_aggregative_estims</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="k">def</span> <span class="nf">_get_aggregative_estims</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">label_predictions</span> <span class="o">=</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>
<span class="n">prevs_estim</span> <span class="o">=</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="o">.</span><span class="n">values</span><span class="p">():</span>
@ -585,7 +587,7 @@
<div class="viewcode-block" id="QuaNetTrainer.predict">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetTrainer.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">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="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">embeddings</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">transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="n">quant_estims</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_aggregative_estims</span><span class="p">(</span><span class="n">posteriors</span><span class="p">)</span>
@ -598,7 +600,7 @@
<span class="k">return</span> <span class="n">prevalence</span></div>
<span class="k">def</span><span class="w"> </span><span class="nf">_epoch</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">posteriors</span><span class="p">,</span> <span class="n">iterations</span><span class="p">,</span> <span class="n">epoch</span><span class="p">,</span> <span class="n">early_stop</span><span class="p">,</span> <span class="n">train</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">_epoch</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">posteriors</span><span class="p">,</span> <span class="n">iterations</span><span class="p">,</span> <span class="n">epoch</span><span class="p">,</span> <span class="n">early_stop</span><span class="p">,</span> <span class="n">train</span><span class="p">):</span>
<span class="n">mse_loss</span> <span class="o">=</span> <span class="n">MSELoss</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">quanet</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="n">train</span><span class="p">)</span>
@ -650,7 +652,7 @@
<div class="viewcode-block" id="QuaNetTrainer.get_params">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetTrainer.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">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>
<span class="n">classifier_params</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">get_params</span><span class="p">()</span>
<span class="n">classifier_params</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;classifier__&#39;</span><span class="o">+</span><span class="n">k</span><span class="p">:</span><span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span><span class="n">v</span> <span class="ow">in</span> <span class="n">classifier_params</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
<span class="k">return</span> <span class="p">{</span><span class="o">**</span><span class="n">classifier_params</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">quanet_params</span><span class="p">}</span></div>
@ -658,7 +660,7 @@
<div class="viewcode-block" id="QuaNetTrainer.set_params">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetTrainer.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="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>
<span class="n">learner_params</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">parameters</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">quanet_params</span><span class="p">:</span>
@ -670,7 +672,7 @@
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">learner_params</span><span class="p">)</span></div>
<span class="k">def</span><span class="w"> </span><span class="nf">__check_params_colision</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">quanet_params</span><span class="p">,</span> <span class="n">learner_params</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">__check_params_colision</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">quanet_params</span><span class="p">,</span> <span class="n">learner_params</span><span class="p">):</span>
<span class="n">quanet_keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">quanet_params</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
<span class="n">learner_keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">learner_params</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
<span class="n">intersection</span> <span class="o">=</span> <span class="n">quanet_keys</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="n">learner_keys</span><span class="p">)</span>
@ -680,7 +682,7 @@
<div class="viewcode-block" id="QuaNetTrainer.clean_checkpoint">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetTrainer.clean_checkpoint">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">clean_checkpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">clean_checkpoint</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"> Removes the checkpoint</span>
<span class="sd"> &quot;&quot;&quot;</span>
@ -689,23 +691,23 @@
<div class="viewcode-block" id="QuaNetTrainer.clean_checkpoint_dir">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetTrainer.clean_checkpoint_dir">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">clean_checkpoint_dir</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">clean_checkpoint_dir</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"> Removes anything contained in the checkpoint directory</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">shutil</span>
<span class="kn">import</span> <span class="nn">shutil</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">checkpointdir</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="nd">@property</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="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="bp">self</span><span class="o">.</span><span class="n">_classes_</span></div>
<div class="viewcode-block" id="mae_loss">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.mae_loss">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">mae_loss</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">mae_loss</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Torch-like wrapper for the Mean Absolute Error</span>
@ -719,7 +721,7 @@
<div class="viewcode-block" id="QuaNetModule">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetModule">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">QuaNetModule</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="k">class</span> <span class="nc">QuaNetModule</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Implements the `QuaNet &lt;https://dl.acm.org/doi/abs/10.1145/3269206.3269287&gt;`_ forward pass.</span>
<span class="sd"> See :class:`QuaNetTrainer` for training QuaNet.</span>
@ -736,7 +738,7 @@
<span class="sd"> :param order_by: integer, class for which the document embeddings are to be sorted</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="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
<span class="n">doc_embedding_size</span><span class="p">,</span>
<span class="n">n_classes</span><span class="p">,</span>
<span class="n">stats_size</span><span class="p">,</span>
@ -771,10 +773,10 @@
<span class="bp">self</span><span class="o">.</span><span class="n">output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">prev_size</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span><span class="w"> </span><span class="nf">device</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">device</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">&#39;cuda&#39;</span><span class="p">)</span> <span class="k">if</span> <span class="nb">next</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span><span class="o">.</span><span class="n">is_cuda</span> <span class="k">else</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">&#39;cpu&#39;</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_init_hidden</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">_init_hidden</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">directions</span> <span class="o">=</span> <span class="mi">2</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">bidirectional</span> <span class="k">else</span> <span class="mi">1</span>
<span class="n">var_hidden</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nlayers</span> <span class="o">*</span> <span class="n">directions</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">hidden_size</span><span class="p">)</span>
<span class="n">var_cell</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nlayers</span> <span class="o">*</span> <span class="n">directions</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">hidden_size</span><span class="p">)</span>
@ -784,7 +786,7 @@
<div class="viewcode-block" id="QuaNetModule.forward">
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetModule.forward">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">doc_embeddings</span><span class="p">,</span> <span class="n">doc_posteriors</span><span class="p">,</span> <span class="n">statistics</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">doc_embeddings</span><span class="p">,</span> <span class="n">doc_posteriors</span><span class="p">,</span> <span class="n">statistics</span><span class="p">):</span>
<span class="n">device</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span>
<span class="n">doc_embeddings</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">doc_embeddings</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
<span class="n">doc_posteriors</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">doc_posteriors</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>

590
docs/build/html/_modules/quapy/method/_threshold_optim.html vendored
View File

@ -1,92 +1,388 @@
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<h1>Source code for quapy.method._threshold_optim</h1><div class="highlight"><pre>
<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>
</nav></div>
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<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.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseEstimator</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="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">quapy.data</span><span class="w"> </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">BinaryAggregativeQuantifier</span>
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<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 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="nn">sklearn.base</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="nn">quapy.functional</span> <span class="k">as</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="nn">quapy.method.aggregative</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="w"> </span><span class="nc">ThresholdOptimization</span><span class="p">(</span><span class="n">BinaryAggregativeQuantifier</span><span class="p">):</span>
<span class="k">class</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="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>
@ -111,14 +407,14 @@
<span class="sd"> :param n_jobs: number of parallel workers</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">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="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="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="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">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>
<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">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="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"> This function should return the (float) score to be minimized.</span>
@ -132,7 +428,7 @@
<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="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="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Indicates whether a combination of tpr and fpr should be discarded</span>
@ -144,7 +440,7 @@
<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="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="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"> decision thresholds. The scoring function is implemented in function `_condition`.</span>
@ -181,7 +477,7 @@
<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="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>
<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="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>
@ -190,26 +486,26 @@
<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="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="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="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">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="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="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">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">FN</span><span class="p">):</span>
<span class="k">if</span> <span class="n">TP</span> <span class="o">+</span> <span class="n">FN</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="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">FN</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">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">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="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="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">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">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="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>
@ -218,7 +514,7 @@
<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="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>
<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>
@ -227,7 +523,7 @@
<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="k">class</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="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>
@ -249,12 +545,12 @@
<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">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">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="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="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="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">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">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>
@ -262,7 +558,7 @@
<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="k">class</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="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>
@ -282,12 +578,12 @@
<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">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">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="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="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="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="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>
@ -296,7 +592,7 @@
<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="k">class</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="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>
@ -316,12 +612,12 @@
<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">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">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="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="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="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">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">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>
@ -329,7 +625,7 @@
<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="k">class</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="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>
@ -348,18 +644,18 @@
<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="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">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="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">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">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="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">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">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="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>
@ -371,7 +667,7 @@
<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="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>
<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="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>
@ -382,7 +678,7 @@
<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="k">class</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="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>
@ -402,42 +698,86 @@
<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="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">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="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="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">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">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>
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383
docs/build/html/_modules/quapy/method/aggregative.html vendored
View File

File diff suppressed because it is too large Load Diff

561
docs/build/html/_modules/quapy/method/base.html vendored
View File

@ -1,95 +1,392 @@
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<h1>Source code for quapy.method.base</h1><div class="highlight"><pre>
<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="w"> </span><span class="nn">copy</span><span class="w"> </span><span class="kn">import</span> <span class="n">deepcopy</span>
<span></span><span class="kn">import</span> <span class="nn">warnings</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">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">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">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">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">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</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</span><span class="w"> </span><span class="kn">import</span> <span class="n">LabelledCollection</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">quapy</span> <span class="k">as</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">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="c1"># Base Quantifier abstract class</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="w"> </span><span class="nc">BaseQuantifier</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">):</span>
<span class="k">class</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="sd"> Abstract Quantifier. A quantifier is defined as an object of a class that implements the method :meth:`fit` on</span>
<span class="sd"> a pair X, y, the method :meth:`predict`, and the :meth:`set_params` and</span>
@ -99,7 +396,7 @@
<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>
<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="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="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Generates a quantifier.</span>
@ -113,7 +410,7 @@
<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="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="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Generate class prevalence estimates for the sample&#39;s instances</span>
@ -125,7 +422,7 @@
<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="k">def</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>
@ -139,13 +436,13 @@
<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="k">class</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="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"> &quot;&quot;&quot;</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">def</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="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>
@ -154,14 +451,14 @@
<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">class</span> <span class="nc">OneVsAll</span><span class="p">:</span>
<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="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">def</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="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>
@ -173,24 +470,24 @@
<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="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>
<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"> 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="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">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">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="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="nb">print</span><span class="p">(</span><span class="s1">&#39;[warning] the quantifier seems to be an instance of qp.method.aggregative.AggregativeQuantifier; &#39;</span>
<span class="sa">f</span><span class="s1">&#39;you might prefer instantiating </span><span class="si">{</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">OneVsAllAggregative</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;the quantifier seems to be an instance of qp.method.aggregative.AggregativeQuantifier; &#39;</span>
<span class="sa">f</span><span class="s1">&#39;you might prefer instantiating </span><span class="si">{</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">OneVsAllAggregative</span><span class="o">.</span><span class="vm">__name__</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">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>
<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="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="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="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>
@ -199,7 +496,7 @@
<span class="k">return</span> <span class="bp">self</span></div>
<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">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">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">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>
@ -208,7 +505,7 @@
<div class="viewcode-block" id="OneVsAllGeneric.predict">
<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="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="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>
@ -217,40 +514,84 @@
<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">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="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="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">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>
</pre></div>
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723
docs/build/html/_modules/quapy/method/meta.html vendored
View File

File diff suppressed because it is too large Load Diff

124
docs/build/html/_modules/quapy/method/non_aggregative.html vendored
View File

@ -29,8 +29,9 @@
<link href="../../../_static/styles/theme.css?digest=a95f357e85573c9b56d5" rel="stylesheet" />
<link href="../../../_static/styles/pydata-sphinx-theme.css?digest=a95f357e85573c9b56d5" rel="stylesheet" />
<link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=8f2a1f02" />
<link rel="stylesheet" type="text/css" href="../../../_static/custom.css?v=874c1152" />
<link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=a746c00c" />
<link rel="stylesheet" type="text/css" href="../../../_static/sphinx-design.min.css?v=95c83b7e" />
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<!-- So that users can add custom icons -->
<script defer src="../../../_static/scripts/fontawesome.js?digest=a95f357e85573c9b56d5"></script>
@ -41,6 +42,7 @@
<script src="../../../_static/documentation_options.js?v=37f418d5"></script>
<script src="../../../_static/doctools.js?v=fd6eb6e6"></script>
<script src="../../../_static/sphinx_highlight.js?v=6ffebe34"></script>
<script src="../../../_static/design-tabs.js?v=f930bc37"></script>
<script>DOCUMENTATION_OPTIONS.pagename = '_modules/quapy/method/non_aggregative';</script>
<script>DOCUMENTATION_OPTIONS.search_as_you_type = false;</script>
<link rel="index" title="Index" href="../../../genindex.html" />
@ -114,8 +116,14 @@
<img src="../../../_static/quapy_logo.png" class="logo__image only-light" alt="QuaPy: A Python-based open-source framework for quantification 0.2.1 documentation - Home"/>
<img src="../../../_static/quapy_logo_dark.png" class="logo__image only-dark pst-js-only" alt="QuaPy: A Python-based open-source framework for quantification 0.2.1 documentation - Home"/>
<p class="title logo__title">QuaPy</p>
</a></div>
@ -131,7 +139,7 @@
<li class="nav-item ">
<a class="nav-link nav-internal" href="../../../index.html">
Quickstart
Home
</a>
</li>
@ -183,6 +191,21 @@
</ul>
</div></div>
<div class="navbar-item"><ul class="navbar-icon-links"
aria-label="Icon Links">
<li class="nav-item">
<a href="https://github.com/HLT-ISTI/QuaPy" title="GitHub" class="nav-link pst-navbar-icon" rel="noopener" target="_blank" data-bs-toggle="tooltip" data-bs-placement="bottom"><i class="fa-brands fa-github fa-lg" aria-hidden="true"></i><span class="visually-hidden">GitHub</span></a>
</li>
</ul></div>
</div>
</div>
@ -229,7 +252,7 @@
<li class="nav-item ">
<a class="nav-link nav-internal" href="../../../index.html">
Quickstart
Home
</a>
</li>
@ -272,6 +295,21 @@
</ul>
</div></div>
<div class="navbar-item"><ul class="navbar-icon-links"
aria-label="Icon Links">
<li class="nav-item">
<a href="https://github.com/HLT-ISTI/QuaPy" title="GitHub" class="nav-link pst-navbar-icon" rel="noopener" target="_blank" data-bs-toggle="tooltip" data-bs-placement="bottom"><i class="fa-brands fa-github fa-lg" aria-hidden="true"></i><span class="visually-hidden">GitHub</span></a>
</li>
</ul></div>
</div>
</div>
@ -332,26 +370,26 @@
<article class="bd-article">
<h1>Source code for quapy.method.non_aggregative</h1><div class="highlight"><pre>
<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">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></span><span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">product</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="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">Counter</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="nn">sklearn.feature_extraction.text</span> <span class="kn">import</span> <span class="n">CountVectorizer</span>
<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">resample</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">normalize</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="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="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="w"> </span><span class="nn">quapy.method._helper</span><span class="w"> </span><span class="kn">import</span> <span class="n">_labels_to_indices</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>
<span class="kn">from</span> <span class="nn">quapy.method.confidence</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.functional</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="nn">quapy.method._helper</span> <span class="kn">import</span> <span class="n">_labels_to_indices</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="nn">scipy.optimize</span> <span class="kn">import</span> <span class="n">lsq_linear</span>
<span class="kn">from</span> <span class="nn">scipy</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="w"> </span><span class="nc">MaximumLikelihoodPrevalenceEstimation</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
<span class="k">class</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="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>
@ -360,12 +398,12 @@
<span class="sd"> any quantification method should beat.</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="k">def</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>
<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="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="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Computes the training prevalence and stores it.</span>
@ -380,7 +418,7 @@
<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="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="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>
@ -394,7 +432,7 @@
<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="k">class</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="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>
@ -410,7 +448,7 @@
<span class="sd"> :param n_jobs: number of parallel workers (default None)</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">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="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">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>
@ -420,7 +458,7 @@
<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>
<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="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>
<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"> HDx is a method for training binary quantifiers, that models quantification as the problem of</span>
@ -436,7 +474,7 @@
<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"> &quot;&quot;&quot;</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="kn">from</span> <span class="nn">quapy.method.meta</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">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>
@ -444,7 +482,7 @@
<span class="k">return</span> <span class="n">hdx</span></div>
<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="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="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="n">feature</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="n">feat_idx</span><span class="p">]</span>
@ -461,7 +499,7 @@
<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="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="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"> The validation distributions have shape `(n, nfeats, nbins)`, with `n` the number of classes, `nfeats`</span>
@ -488,7 +526,7 @@
<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="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="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"> (the mixture) that best matches the test distribution, in terms of the divergence measure of choice.</span>
@ -504,7 +542,7 @@
<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">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="w"> </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="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">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>
@ -517,7 +555,7 @@
<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="k">class</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>
@ -558,7 +596,7 @@
<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="k">def</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>
@ -580,7 +618,7 @@
<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="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="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>
@ -600,7 +638,7 @@
<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="k">def</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>
@ -627,12 +665,12 @@
<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="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="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="k">def</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>
@ -641,17 +679,17 @@
<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="k">def</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="k">def</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">def</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>
@ -659,7 +697,7 @@
<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">def</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>
@ -677,14 +715,14 @@
<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="k">def</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="k">def</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">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>

607
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@ -1,101 +1,398 @@
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<h1>Source code for quapy.model_selection</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">itertools</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">signal</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="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="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="w"> </span><span class="nn">functools</span><span class="w"> </span><span class="kn">import</span> <span class="n">wraps</span>
<span></span><span class="kn">import</span> <span class="nn">itertools</span>
<span class="kn">import</span> <span class="nn">logging</span>
<span class="kn">import</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="nn">enum</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="nn">functools</span> <span class="kn">import</span> <span class="n">wraps</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</span><span class="w"> </span><span class="kn">import</span> <span class="n">clone</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="nn">sklearn</span> <span class="kn">import</span> <span class="n">clone</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</span><span class="w"> </span><span class="kn">import</span> <span class="n">evaluation</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="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="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="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="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="nn">quapy</span> <span class="k">as</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="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="nn">quapy.data.base</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="nn">quapy.util</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>
<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="k">class</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">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>
@ -105,28 +402,28 @@
<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">class</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="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>
<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">msg</span> <span class="o">=</span> <span class="n">msg</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">def</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">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">def</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>
<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">def</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>
<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">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>
@ -134,7 +431,7 @@
<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="k">class</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="sd"> Optimizes the hyperparameters of a quantification method, based on an evaluation method and on an evaluation</span>
@ -157,7 +454,7 @@
<span class="sd"> :param verbose: set to True to get information through the stdout</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="k">def</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">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>
@ -179,11 +476,11 @@
<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">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">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">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="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">info</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="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">def</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="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>
@ -194,10 +491,10 @@
<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="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="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="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="w"> </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="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">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>
@ -206,12 +503,12 @@
<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">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="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="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">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="w"> </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="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">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>
@ -222,10 +519,10 @@
<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">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="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="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="w"> </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="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">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>
@ -235,7 +532,7 @@
<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="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="w"> </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="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="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>
@ -250,7 +547,7 @@
<span class="k">return</span> <span class="kc">False</span>
<span class="k">return</span> <span class="kc">True</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="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">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="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>
@ -287,7 +584,7 @@
<span class="k">return</span> <span class="n">aggr_outs</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="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">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="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>
@ -299,7 +596,7 @@
<span class="p">)</span>
<span class="k">return</span> <span class="n">scores</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">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">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="k">else</span><span class="p">:</span>
@ -307,7 +604,7 @@
<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="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="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>
@ -378,7 +675,7 @@
<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="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="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 X: sample contanining the instances</span>
@ -391,7 +688,7 @@
<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="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>
<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>
@ -401,7 +698,7 @@
<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="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>
<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>
@ -412,7 +709,7 @@
<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="k">def</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="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>
@ -424,7 +721,7 @@
<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="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="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="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>
@ -437,7 +734,7 @@
<span class="n">output</span> <span class="o">=</span> <span class="kc">None</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">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">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">else</span><span class="p">:</span>
@ -465,7 +762,7 @@
<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="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>
<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"> but for quantification.</span>
@ -491,7 +788,7 @@
<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="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>
<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"> Example:</span>
@ -513,7 +810,7 @@
<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="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>
<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"> hyper-parameters, and another for que quantifier-specific hyper-parameters</span>
@ -537,31 +834,75 @@
</pre></div>
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116
docs/build/html/_modules/quapy/plot.html vendored
View File

@ -29,8 +29,9 @@
<link href="../../_static/styles/theme.css?digest=a95f357e85573c9b56d5" rel="stylesheet" />
<link href="../../_static/styles/pydata-sphinx-theme.css?digest=a95f357e85573c9b56d5" rel="stylesheet" />
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@ -41,6 +42,7 @@
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<link rel="index" title="Index" href="../../genindex.html" />
@ -114,8 +116,14 @@
<img src="../../_static/quapy_logo.png" class="logo__image only-light" alt="QuaPy: A Python-based open-source framework for quantification 0.2.1 documentation - Home"/>
<img src="../../_static/quapy_logo_dark.png" class="logo__image only-dark pst-js-only" alt="QuaPy: A Python-based open-source framework for quantification 0.2.1 documentation - Home"/>
<p class="title logo__title">QuaPy</p>
</a></div>
@ -131,7 +139,7 @@
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@ -183,6 +191,21 @@
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@ -229,7 +252,7 @@
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@ -272,6 +295,21 @@
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@ -332,19 +370,19 @@
<article class="bd-article">
<h1>Source code for quapy.plot</h1><div class="highlight"><pre>
<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="w"> </span><span class="nn">math</span>
<span></span><span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
<span class="kn">import</span> <span class="nn">math</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">import</span><span class="w"> </span><span class="nn">matplotlib.colors</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">mcolors</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="w"> </span><span class="nn">matplotlib.colors</span><span class="w"> </span><span class="kn">import</span> <span class="n">LinearSegmentedColormap</span><span class="p">,</span> <span class="n">ListedColormap</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">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="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">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</span> <span class="kn">import</span> <span class="n">cm</span>
<span class="kn">import</span> <span class="nn">matplotlib.colors</span> <span class="k">as</span> <span class="nn">mcolors</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">from</span> <span class="nn">matplotlib.colors</span> <span class="kn">import</span> <span class="n">LinearSegmentedColormap</span><span class="p">,</span> <span class="n">ListedColormap</span>
<span class="kn">from</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">import</span> <span class="n">get_cmap</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">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</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">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</span> <span class="k">as</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.dpi&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">200</span>
@ -353,7 +391,7 @@
<div class="viewcode-block" id="binary_diagonal">
<a class="viewcode-back" href="../../quapy.html#quapy.plot.binary_diagonal">[docs]</a>
<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="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="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="sd"> The diagonal plot displays the predicted prevalence values (along the y-axis) as a function of the true prevalence</span>
@ -433,7 +471,7 @@
<div class="viewcode-block" id="binary_bias_global">
<a class="viewcode-back" href="../../quapy.html#quapy.plot.binary_bias_global">[docs]</a>
<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="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="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"> for each quantification method with respect to a given positive class.</span>
@ -479,7 +517,7 @@
<div class="viewcode-block" id="binary_bias_bins">
<a class="viewcode-back" href="../../quapy.html#quapy.plot.binary_bias_bins">[docs]</a>
<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="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="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="sd"> Box-plots displaying the local bias (i.e., signed error computed as the estimated value minus the true value)</span>
@ -505,7 +543,7 @@
<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="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="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="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>
@ -581,7 +619,7 @@
<div class="viewcode-block" id="error_by_drift">
<a class="viewcode-back" href="../../quapy.html#quapy.plot.error_by_drift">[docs]</a>
<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="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="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_legend</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
@ -717,7 +755,7 @@
<div class="viewcode-block" id="brokenbar_supremacy_by_drift">
<a class="viewcode-back" href="../../quapy.html#quapy.plot.brokenbar_supremacy_by_drift">[docs]</a>
<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="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="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">method_order</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
@ -831,7 +869,6 @@
<span class="n">best_bucket_methods</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">method_order</span><span class="p">[</span><span class="n">method_index</span><span class="p">])</span>
<span class="n">best_methods</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">best_bucket_methods</span><span class="p">)</span>
<span class="n">salient_methods</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">best_bucket_methods</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">best_bucket_methods</span><span class="p">)</span>
<span class="k">if</span> <span class="n">binning</span><span class="o">==</span><span class="s1">&#39;isomerous&#39;</span><span class="p">:</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">axes</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="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">gridspec_kw</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;height_ratios&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]},</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">salient_methods</span><span class="p">)))</span>
@ -914,7 +951,7 @@
<div class="viewcode-block" id="plot_simplex">
<a class="viewcode-back" href="../../quapy.html#quapy.plot.plot_simplex">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">plot_simplex</span><span class="p">(</span>
<span class="k">def</span> <span class="nf">plot_simplex</span><span class="p">(</span>
<span class="n">point_layers</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">region_layers</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">density_function</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
@ -1032,7 +1069,7 @@
<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="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="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">method_order</span><span class="o">=</span><span class="p">[]</span>
@ -1046,14 +1083,14 @@
<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">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="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="n">NUM_COLORS</span> <span class="o">=</span> <span class="n">n_methods</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">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="w"> </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="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="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>
@ -1063,7 +1100,7 @@
<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="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="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="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>
@ -1084,7 +1121,7 @@
<span class="k">return</span> <span class="n">data</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_simplex_to_cartesian</span><span class="p">(</span><span class="n">prevalences</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">_simplex_to_cartesian</span><span class="p">(</span><span class="n">prevalences</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">asarray</span><span class="p">(</span><span class="n">prevalences</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">prevalences</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">atleast_2d</span><span class="p">(</span><span class="n">prevalences</span><span class="p">)</span>
<span class="k">if</span> <span class="n">prevalences</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="mi">3</span><span class="p">:</span>
@ -1094,14 +1131,14 @@
<span class="k">return</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_barycentric_from_xy</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="nf">_barycentric_from_xy</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">p3</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">y</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="n">p2</span> <span class="o">=</span> <span class="n">x</span> <span class="o">-</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">p3</span>
<span class="n">p1</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">p2</span> <span class="o">-</span> <span class="n">p3</span>
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">stack</span><span class="p">([</span><span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">,</span> <span class="n">p3</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">def</span><span class="w"> </span><span class="nf">_simplex_mesh</span><span class="p">(</span><span class="n">resolution</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">_simplex_mesh</span><span class="p">(</span><span class="n">resolution</span><span class="p">):</span>
<span class="n">simplex_ymax</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span>
<span class="n">xs</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">resolution</span><span class="p">)</span>
<span class="n">ys</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="n">simplex_ymax</span><span class="p">,</span> <span class="n">resolution</span><span class="p">)</span>
@ -1111,7 +1148,7 @@
<span class="k">return</span> <span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">pts_bary</span><span class="p">,</span> <span class="n">mask</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_evaluate_simplex_function</span><span class="p">(</span><span class="n">function</span><span class="p">,</span> <span class="n">points</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">_evaluate_simplex_function</span><span class="p">(</span><span class="n">function</span><span class="p">,</span> <span class="n">points</span><span class="p">):</span>
<span class="n">points</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">points</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">try</span><span class="p">:</span>
<span class="n">values</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">function</span><span class="p">(</span><span class="n">points</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
@ -1124,14 +1161,14 @@
<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">function</span><span class="p">(</span><span class="n">point</span><span class="p">)</span> <span class="k">for</span> <span class="n">point</span> <span class="ow">in</span> <span class="n">points</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">def</span><span class="w"> </span><span class="nf">_region_colormap</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.35</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">_region_colormap</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.35</span><span class="p">):</span>
<span class="k">return</span> <span class="n">ListedColormap</span><span class="p">([</span>
<span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">),</span>
<span class="p">(</span><span class="o">*</span><span class="n">mcolors</span><span class="o">.</span><span class="n">to_rgb</span><span class="p">(</span><span class="n">color</span><span class="p">),</span> <span class="n">alpha</span><span class="p">),</span>
<span class="p">])</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_plot_simplex_points</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">point_layers</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">_plot_simplex_points</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">point_layers</span><span class="p">):</span>
<span class="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="n">point_layers</span><span class="p">:</span>
<span class="n">points</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">layer</span><span class="p">[</span><span class="s1">&#39;points&#39;</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">style</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;s&#39;</span><span class="p">:</span> <span class="mi">25</span><span class="p">,</span> <span class="s1">&#39;alpha&#39;</span><span class="p">:</span> <span class="mf">0.8</span><span class="p">}</span>
@ -1139,7 +1176,7 @@
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="o">*</span><span class="n">_simplex_to_cartesian</span><span class="p">(</span><span class="n">points</span><span class="p">),</span> <span class="n">label</span><span class="o">=</span><span class="n">layer</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;label&#39;</span><span class="p">),</span> <span class="o">**</span><span class="n">style</span><span class="p">)</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_plot_simplex_regions</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">region_layers</span><span class="p">,</span> <span class="n">resolution</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">_plot_simplex_regions</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">region_layers</span><span class="p">,</span> <span class="n">resolution</span><span class="p">):</span>
<span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">pts_bary</span><span class="p">,</span> <span class="n">simplex_mask</span> <span class="o">=</span> <span class="n">_simplex_mesh</span><span class="p">(</span><span class="n">resolution</span><span class="p">)</span>
<span class="n">valid_points</span> <span class="o">=</span> <span class="n">pts_bary</span><span class="p">[</span><span class="n">simplex_mask</span><span class="p">]</span>
@ -1158,7 +1195,7 @@
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">([],</span> <span class="p">[],</span> <span class="n">color</span><span class="o">=</span><span class="n">layer</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;color&#39;</span><span class="p">,</span> <span class="s1">&#39;blue&#39;</span><span class="p">),</span> <span class="n">alpha</span><span class="o">=</span><span class="n">layer</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;alpha&#39;</span><span class="p">,</span> <span class="mf">0.35</span><span class="p">),</span> <span class="n">label</span><span class="o">=</span><span class="n">layer</span><span class="p">[</span><span class="s1">&#39;label&#39;</span><span class="p">])</span>
<span class="k">def</span><span class="w"> </span><span class="nf">_plot_simplex_density</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">density_function</span><span class="p">,</span> <span class="n">resolution</span><span class="p">,</span> <span class="n">color</span><span class="p">,</span> <span class="n">alpha</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">_plot_simplex_density</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">density_function</span><span class="p">,</span> <span class="n">resolution</span><span class="p">,</span> <span class="n">color</span><span class="p">,</span> <span class="n">alpha</span><span class="p">):</span>
<span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">pts_bary</span><span class="p">,</span> <span class="n">simplex_mask</span> <span class="o">=</span> <span class="n">_simplex_mesh</span><span class="p">(</span><span class="n">resolution</span><span class="p">)</span>
<span class="n">valid_points</span> <span class="o">=</span> <span class="n">pts_bary</span><span class="p">[</span><span class="n">simplex_mask</span><span class="p">]</span>
<span class="n">density</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">simplex_mask</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
@ -1174,14 +1211,13 @@
<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="k">def</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>
@ -1202,8 +1238,8 @@
<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="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">sklearn.linear_model</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>

640
docs/build/html/_modules/quapy/protocol.html vendored
View File

@ -1,104 +1,400 @@
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<h1>Source code for quapy.protocol</h1><div class="highlight"><pre>
<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="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Iterable</span>
</nav></div>
</div>
<div class="navbar-header-items__end">
<div class="navbar-item navbar-persistent--container">
<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="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="w"> </span><span class="nn">itertools</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">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="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="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>
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<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 class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Iterable</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="nn">itertools</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="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">from</span> <span class="nn">quapy.data</span> <span class="kn">import</span> <span class="n">LabelledCollection</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="nn">os.path</span> <span class="kn">import</span> <span class="n">exists</span>
<span class="kn">from</span> <span class="nn">glob</span> <span class="kn">import</span> <span class="n">glob</span>
<span class="kn">from</span> <span class="nn">collections.abc</span> <span class="kn">import</span> <span class="n">Iterable</span>
<span class="kn">from</span> <span class="nn">numbers</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="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="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>
<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"> &quot;&quot;&quot;</span>
<span class="nd">@abstractmethod</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="k">def</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"> Implements the protocol. Yields one sample at a time along with its prevalence</span>
@ -109,7 +405,7 @@
<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="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="sd"> Indicates the total number of samples that the protocol generates.</span>
@ -122,16 +418,16 @@
<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="k">class</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="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"> &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">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="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="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="k">def</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 from the initial list at a time</span>
@ -143,7 +439,7 @@
<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="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="sd"> Returns the number of samples in this protocol</span>
@ -156,18 +452,18 @@
<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="k">class</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="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">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="k">def</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>
@ -179,7 +475,7 @@
<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="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="sd"> Returns the number of samples in this protocol</span>
@ -192,7 +488,7 @@
<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="k">class</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="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>
@ -209,21 +505,21 @@
<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="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="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="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="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">def</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="nd">@random_state</span><span class="o">.</span><span class="n">setter</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="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="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>
<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="k">def</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"> This function has to return all the necessary parameters to replicate the samples</span>
@ -235,7 +531,7 @@
<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="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>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Extract one sample determined by the given parameters</span>
@ -245,7 +541,7 @@
<span class="o">...</span></div>
<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="k">def</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. 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>
@ -264,7 +560,7 @@
<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="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="n">params</span><span class="p">):</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"> This collator simply returns the sample as it is. Classes inheriting from this abstract class can</span>
@ -281,7 +577,7 @@
<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="k">class</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="sd"> Protocols that generate samples from a :class:`qp.data.LabelledCollection` object.</span>
<span class="sd"> &quot;&quot;&quot;</span>
@ -290,7 +586,7 @@
<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="k">def</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="sd"> Returns the labelled collection on which this protocol acts.</span>
@ -301,7 +597,7 @@
<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="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>
<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"> :class:`qp.data.LabelledCollection` in which the original instances have been replaced</span>
@ -328,7 +624,7 @@
<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="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>
<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>
@ -350,7 +646,7 @@
<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="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>
@ -364,7 +660,7 @@
<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="k">class</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="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>
@ -388,7 +684,7 @@
<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">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="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="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>
@ -410,7 +706,7 @@
<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="k">def</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="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>
@ -438,7 +734,7 @@
<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="k">def</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 as according to the APP protocol.</span>
@ -453,7 +749,7 @@
<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="w"> </span><span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<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="sd"> Returns the number of samples that will be generated</span>
@ -466,7 +762,7 @@
<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="k">class</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="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>
@ -481,7 +777,7 @@
<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">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="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="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>
@ -492,7 +788,7 @@
<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="k">def</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 as according to the NPP protocol.</span>
@ -507,7 +803,7 @@
<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="w"> </span><span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<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="sd"> Returns the number of samples that will be generated (equals to &quot;repeats&quot;)</span>
@ -520,7 +816,7 @@
<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="k">class</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="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>
@ -539,7 +835,7 @@
<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">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="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="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>
@ -550,7 +846,7 @@
<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="k">def</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 as according to the UPP protocol.</span>
@ -565,7 +861,7 @@
<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="w"> </span><span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<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="sd"> Returns the number of samples that will be generated (equals to &quot;repeats&quot;)</span>
@ -578,7 +874,7 @@
<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="k">class</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>
@ -596,13 +892,8 @@
<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="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">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>
@ -617,7 +908,6 @@
<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>
@ -625,7 +915,7 @@
<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="k">def</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>
@ -638,7 +928,7 @@
<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="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="sd"> Returns the number of samples that will be generated (equals to &quot;repeats&quot;)</span>
@ -651,7 +941,7 @@
<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="k">class</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="sd"> Generates mixtures of two domains (A and B) at controlled rates, but preserving the original class prevalence.</span>
@ -670,7 +960,7 @@
<span class="sd"> will be the same every time the protocol is called)</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="k">def</span> <span class="fm">__init__</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">domainB</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span>
@ -704,7 +994,7 @@
<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="k">def</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 as according to the this protocol.</span>
@ -724,7 +1014,7 @@
<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="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>
<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>
@ -739,7 +1029,7 @@
<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="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="sd"> Returns the number of samples that will be generated (equals to &quot;repeats * mixture_points&quot;)</span>
@ -757,31 +1047,75 @@
<span class="n">UniformPrevalenceProtocol</span> <span class="o">=</span> <span class="n">UPP</span>
</pre></div>
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@ -370,26 +370,26 @@
<article class="bd-article">
<h1>Source code for quapy.util</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">contextlib</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">itertools</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">multiprocessing</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">os</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">pickle</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">urllib</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="w"> </span><span class="nn">contextlib</span><span class="w"> </span><span class="kn">import</span> <span class="n">ExitStack</span>
<span></span><span class="kn">import</span> <span class="nn">contextlib</span>
<span class="kn">import</span> <span class="nn">itertools</span>
<span class="kn">import</span> <span class="nn">multiprocessing</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">pickle</span>
<span class="kn">import</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="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">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</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">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</span> <span class="k">as</span> <span class="nn">qp</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="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="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="nn">signal</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">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">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">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>
@ -399,7 +399,7 @@
<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="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>
<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"> func is applied in two parallel processes to args[0:50] and to args[50:99]. func is a function</span>
@ -420,7 +420,7 @@
<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="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>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> A wrapper of multiprocessing:</span>
@ -438,7 +438,7 @@
<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="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="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="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>
@ -458,7 +458,7 @@
<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="k">def</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>
@ -476,7 +476,7 @@
<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="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="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>
@ -496,7 +496,7 @@
<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="k">def</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="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>
@ -520,14 +520,14 @@
<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="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>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Downloads a file from a url</span>
<span class="sd"> :param url: the url</span>
<span class="sd"> :param archive_filename: destination filename</span>
<span class="sd"> &quot;&quot;&quot;</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="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="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="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>
@ -539,7 +539,7 @@
<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="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>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Downloads a file (using :meth:`download_file`) if the file does not exist.</span>
@ -555,7 +555,7 @@
<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="k">def</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="sd"> An alias to `os.makedirs(path, exist_ok=True)` that also returns the path. This is useful in cases like, e.g.:</span>
@ -571,7 +571,7 @@
<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="k">def</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="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>
@ -586,7 +586,7 @@
<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="k">def</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="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>
@ -601,7 +601,7 @@
<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="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>
<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>
@ -616,7 +616,7 @@
<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="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="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="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>
@ -646,7 +646,7 @@
<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">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">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="s1">&#39;error: sample_size set to None, and cannot be resolved from the environment&#39;</span>
@ -658,8 +658,8 @@
<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="k">def</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="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>
@ -683,7 +683,7 @@
<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="k">class</span> <span class="nc">EarlyStop</span><span class="p">:</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>
@ -708,7 +708,7 @@
<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="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="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="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>
@ -718,7 +718,7 @@
<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">IMPROVED</span> <span class="o">=</span> <span class="kc">False</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="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="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"> the patiente counter gets reset. If otherwise, the patience counter is decreased, and in case it reachs 0,</span>
@ -742,7 +742,7 @@
<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="k">def</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="sd"> Opens a context that will launch an exception if not closed after a given number of seconds</span>
@ -761,7 +761,7 @@
<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="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="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">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">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>

291
docs/source/manuals/methods.md
View File

@ -227,6 +227,38 @@ and is suitable for problems in which the `q = Mp` matrix is nearly non-invertib
Note that this quantification method requires `val_split` to be a `float` and installation of additional dependencies (`$ pip install quapy[bayes]`) needed to run Markov chain Monte Carlo sampling. Markov Chain Monte Carlo is is slower than matrix inversion methods, but is guaranteed to sample proper probability vectors, so no clipping strategies are required.
An example presenting how to run the method and use posterior samples is available in `examples/bayesian_quantification.py`.
### Regularized Learning under Label Shift (RLLS)
`RLLS` is available at `qp.method.aggregative.RLLS` and ports the regularized
importance-weight estimation procedure of
[Azizzadenesheli, K., Liu, A., Yang, F., and Anandkumar, A. (2019). Regularized
Learning for Domain Adaptation under Label Shifts.
ICLR 2019](https://arxiv.org/abs/1903.09734) to QuaPy's aggregative interface.
The method estimates the label-shift importance weights `w = q(y)/p(y)` from
the classifier's validation posteriors (or, in `mode='hard'`, its argmax
predictions) and the corresponding source labels, regularizing the estimation
by an amount controlled by `alpha` (scaled by a finite-sample confidence term
governed by `delta`). The resulting weights are then used to rescale the
training prevalence into the target prevalence estimate.
Like ACC and PACC, RLLS requires validation predictions and therefore expects
`val_split` to be set (as an integer for k-fold cross-validation, a float for
a held-out split, or an explicit `(X, y)` tuple) whenever `fit_classifier=True`.
This method relies on the optional `cvxpy` dependency, which must be
installed separately (`$ pip install cvxpy`).
```python
import quapy as qp
from quapy.method.aggregative import RLLS
from sklearn.linear_model import LogisticRegression
train, test = qp.datasets.fetch_UCIBinaryDataset('haberman').train_test
model = RLLS(LogisticRegression(max_iter=2000), val_split=5)
model.fit(*train.Xy)
estim_prevalence = model.predict(test.X)
```
### Expectation Maximization (EMQ)
The Expectation Maximization Quantifier (EMQ), also known as
@ -274,6 +306,40 @@ or Temperature Scaling (`ts`); default is `None` (no calibration).
You can use the class method `EMQ_BCTS` to effortlessly instantiate EMQ with the best performing
heuristics found by [Alexandari et al. (2020)](http://proceedings.mlr.press/v119/alexandari20a.html). See the API documentation for further details.
#### BayesianMAPLS
`BayesianMAPLS` is a Bayesian variant of EMQ/MLLS proposed by
Ye, C. et al. (2024). Label shift estimation for class-imbalance problem: A
Bayesian approach. Proceedings of the IEEE/CVF Winter Conference on
Applications of Computer Vision (WACV 2024). QuaPy's implementation is
adapted from the [authors' reference code](https://github.com/ChangkunYe/MAPLS/blob/main/label_shift/mapls.py).
Rather than returning a single point estimate for the class prevalence, it
places a Dirichlet prior over the sought prevalence vector (in an
unconstrained, Isometric-Log-Ratio-transformed space) and samples from the
resulting posterior via Markov Chain Monte Carlo (using `numpyro`/`jax`),
conditioned on a preliminary MAP estimate obtained via the underlying `mapls`
routine. Like `BayesianCC`, its `aggregate` method returns the posterior mean,
while `predict_conf` additionally returns a confidence region (`intervals`,
`ellipse`, `ellipse-clr`, or `ellipse-ilr`) built from the posterior samples.
This method requires installation of additional dependencies
(`$ pip install quapy[bayes]`) needed to run MCMC sampling; parameters
`num_warmup` and `num_samples` control the length of the chain, and `prior`
allows choosing between a uniform Dirichlet prior (default) or one of the
data-dependent priors ("map"/"map2") proposed in the original paper.
```python
import quapy as qp
from quapy.method._bayesian import BayesianMAPLS
from sklearn.linear_model import LogisticRegression
train, test = qp.datasets.fetch_UCIBinaryDataset('haberman').train_test
model = BayesianMAPLS(LogisticRegression())
model.fit(*train.Xy)
estim_prevalence, conf_region = model.predict_conf(test.X)
```
### Hellinger Distance y (HDy)
@ -324,6 +390,38 @@ framework proposed by [Maletzke et al (2020)](https://ojs.aaai.org/index.php/AAA
and the "SMM" method proposed by [Hassan et al (2019)](https://ieeexplore.ieee.org/document/9260028)
(thanks to _Pablo González_ for the contributions!)
#### PQ
`PQ` (Precise Quantifier), available at `qp.method.confidence.PQ`, is a
Bayesian distribution-matching variant of `HDy` proposed in
[Igiraneza, A.B., Fraser, C., and Hinch, R. (2025). Estimating prevalence
with precision and accuracy.](https://arxiv.org/abs/2507.06061)
Rather than matching a single test histogram against a mixture of two
class-conditional histograms via a divergence measure (as `HDy` does), `PQ`
places the histogram-matching problem in a Bayesian setting and samples the
full posterior distribution over the (binary) prevalence value via Markov
Chain Monte Carlo (using `stan`). Its `aggregate` method returns the
posterior mean, while `predict_conf` additionally returns a confidence
region built from the posterior samples (`intervals`, `ellipse`, or
`ellipse-clr`).
`PQ` accepts `nbins` (the number of histogram bins, quantile-based by default,
or uniform if `fixed_bins=True`), and the usual MCMC controls `num_warmup`,
`num_samples`, and `stan_seed`. This method relies on the optional `stan`
dependency, installed via `$ pip install quapy[bayes]`.
```python
import quapy as qp
from quapy.method.confidence import PQ
from sklearn.linear_model import LogisticRegression
train, test = qp.datasets.fetch_UCIBinaryDataset('haberman').train_test
model = PQ(LogisticRegression())
model.fit(*train.Xy)
estim_prevalence, conf_region = model.predict_conf(test.X)
```
### Threshold Optimization methods
QuaPy implements Forman's threshold optimization methods;
@ -442,6 +540,199 @@ All KDE-based methods depend on the hyperparameter `bandwidth` of the kernel. Ty
that can be explored in model selection range in [0.01, 0.25]. Previous experiments reveal the methods' performance
varies smoothly at small variations of this hyperparameter.
#### BayesianKDEy
`BayesianKDEy`, available at `qp.method._bayesian.BayesianKDEy`, is a Bayesian
version of KDEy. Instead of solving for the single prevalence vector that
minimizes a divergence between the test distribution and a KDE-based mixture
model (as the KDEy variants above do), `BayesianKDEy` places a Dirichlet
prior over the prevalence vector and samples its posterior via Markov Chain
Monte Carlo (using `numpyro`/`jax`), conditioned on the same KDE mixture
components. Its `aggregate` method returns the posterior mean, while
`predict_conf` additionally returns a confidence region built from the
posterior samples.
In addition to the `kernel` and `bandwidth` hyperparameters (with the same
`gaussian`/`aitchison`/`ilr` kernel choice, and `shrinkage` regularization
for the latter two, available in `KDEyML`), `BayesianKDEy` exposes the usual
MCMC controls: `num_warmup`, `num_samples`, `mcmc_seed`, a `temperature` for
posterior calibration, and `prior` for choosing the Dirichlet prior
(`'uniform'` by default, or a custom scalar/array). This method relies on
the optional MCMC dependencies, installed via
`$ pip install quapy[bayes]`.
```python
import quapy as qp
from quapy.method._bayesian import BayesianKDEy
from sklearn.linear_model import LogisticRegression
train, test = qp.datasets.fetch_UCIBinaryDataset('haberman').train_test
model = BayesianKDEy(LogisticRegression(), bandwidth=0.1)
model.fit(*train.Xy)
estim_prevalence, conf_region = model.predict_conf(test.X)
```
## Non-Aggregative Methods
Non-aggregative methods are quantifiers that do not follow the two-step
(classify, then aggregate) pattern described above for aggregative methods.
These methods are implemented in the `qp.method.non_aggregative` module and
extend `BaseQuantifier` directly, implementing `fit` and `predict` on their own terms.
### Maximum Likelihood Prevalence Estimation (MLPE)
`MaximumLikelihoodPrevalenceEstimation` (MLPE) is a lazy baseline quantifier
that assumes the IID assumption holds, i.e., that there is no prior probability
shift between the training and the test distributions. Its `fit` method simply
computes and stores the training prevalence, and its `predict` method returns
that same training prevalence for any test sample, irrespective of the sample
itself. MLPE is considered a lower-bound quantifier: any quantification method
worth using should outperform it.
```python
import quapy as qp
from quapy.method.non_aggregative import MaximumLikelihoodPrevalenceEstimation
dataset = qp.datasets.fetch_UCIBinaryDataset('haberman')
train, test = dataset.train_test
model = MaximumLikelihoodPrevalenceEstimation()
model.fit(*train.Xy)
estim_prevalence = model.predict(test.X) # always equals train.prevalence()
```
### Distribution Matching x (DMx) and Hellinger Distance x (HDx)
`DMx` is the covariate-space counterpart of the `DMy` distribution-matching
quantifier described in {ref}`the Hellinger Distance y (HDy) section <manuals/methods:Hellinger Distance y (HDy)>`:
instead of matching distributions built from the classifier's predictions, `DMx` matches
distributions built directly from the (discretized) feature space, and thus
requires no classifier at all. For each class, `DMx` builds one histogram per
feature from the training instances of that class; at prediction time, it
searches for the mixture of these class-conditional histograms that best
matches the (also histogram-based) representation of the test sample, in
terms of a chosen divergence.
`DMx` accepts the following hyperparameters in its constructor:
* `nbins`: the number of bins used to discretize each feature (default 8)
* `divergence`: a string ("HD" for Hellinger Distance, or "topsoe") or a
callable taking two histograms and returning a divergence value (default "HD")
* `cdf`: whether to match cumulative distributions (CDFs) instead of the
histograms (PDFs) themselves (default False)
* `search`: the strategy used for finding the optimal prevalence; valid
options are `optim_minimize` (default, works for binary and multiclass
problems), `linear_search`, and `ternary_search` (these last two are
binary-only)
* `n_jobs`: number of parallel workers (default None)
`DMx` also offers the class method `DMx.HDx` (aliased as
`qp.method.non_aggregative.HDx`, and also as `HellingerDistanceX`) that
reproduces the original Hellinger Distance x (HDx) method proposed by
[González-Castro, Alaiz-Rodríguez, and Alegre (2013)](https://www.sciencedirect.com/science/article/pii/S0020025512004069),
the same paper that introduced HDy. HDx is a binary-only method that computes
the matching for `nbins` ranging over `[10, 20, ..., 110]` (via a
`MedianEstimator`, taking the median of the resulting estimates) and searches
for the best prevalence via a linear search stepping by 0.01, rather than via
the `optim_minimize` search used by `DMx` by default.
The following code, adapted from the example comparing HDy and HDx
(`examples/11.comparing_HDy_HDx.py`), shows the two methods side-by-side:
```python
from sklearn.linear_model import LogisticRegression
import quapy as qp
from quapy.method.aggregative import HDy
from quapy.method.non_aggregative import DMx
train, test = qp.datasets.fetch_UCIBinaryDataset('haberman').train_test
Xtr, ytr = train.Xy
hdy = HDy(LogisticRegression()).fit(Xtr, ytr)
estim_prevalence_hdy = hdy.predict(test.X)
hdx = DMx.HDx(n_jobs=-1).fit(Xtr, ytr)
estim_prevalence_hdx = hdx.predict(test.X)
```
Note that, unlike HDy, HDx requires no classifier whatsoever, since it
operates directly on the covariates.
### ReadMe
`ReadMe` is a non-aggregative quantification method proposed by
[Hopkins, D. and King, G. (2007). A method of automated nonparametric content
analysis for social science. American Journal of Political Science,
54(1):229-247.](https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1540-5907.2009.00428.x)
The method estimates `Q(Y=i)` directly from `Q(X) = sum_i Q(X|Y=i) Q(Y=i)` by
solving a (constrained) least-squares regression, thus avoiding the cost of
estimating posterior probabilities `Q(Y=i|X)` altogether.
Since `Q(X)` and `Q(X|Y=i)` can be of very high dimension for realistic
feature spaces, ReadMe renders the problem tractable by performing bagging in
the feature space: many small random subsets of features (of size
`bagging_range`) are drawn, the least-squares problem is solved on each
subset, and the resulting estimates are averaged. ReadMe additionally
combines this bagging procedure with bootstrap resampling of the training
instances in order to derive confidence regions around the point estimate;
accordingly, `ReadMe` implements the `WithConfidenceABC` interface (see the
{ref}`confidence regions section <manuals/methods:Confidence Regions for Class Prevalence Estimation>`),
and exposes a `predict_conf` method in addition to `predict`.
`ReadMe` accepts the following hyperparameters:
* `prob_model`: either `"full"` (default), the original Hopkins and King
formulation, in which `Q(X)` and `Q(X|Y)` are modelled empirically and thus
require the feature matrix `X` to be binary (e.g., term presence/absence);
or `"naive"`, a much faster approximation that models `Q(X)` and `Q(X|Y)` as
multinomial (bag-of-words) distributions, and that supports much larger
values of `bagging_range`
* `bootstrap_trials`: number of bootstrap resamplings of the training data
used for deriving the confidence region (default 300)
* `bagging_trials`: number of bagging trials, i.e., random feature subsets,
averaged for each point estimate (default 300)
* `bagging_range`: number of features kept in each bagging trial (default 15);
note that, when `prob_model="full"`, this value should typically be kept
small (the authors advise against values above 25) since the empirical
distribution requires enumerating `2^bagging_range` possible feature
configurations
* `confidence_level`: the confidence level for the confidence region
(default 0.95)
* `region`: the type of confidence region to construct, one of `"intervals"`
(default), `"ellipse"`, or `"ellipse-clr"` (see the
{ref}`confidence regions section <manuals/methods:Confidence Regions for Class Prevalence Estimation>`
for details)
* `random_state`: an int for replicability, or `None` (default)
* `verbose`: whether to display progress information (default False)
The following minimal example, adapted from
`examples/18.ReadMe_for_text_analysis.py`, shows ReadMe applied to a binary
bag-of-words text quantification problem:
```python
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.pipeline import Pipeline
import quapy as qp
from quapy.method.non_aggregative import ReadMe
reviews = qp.datasets.fetch_reviews('imdb').reduce(n_train=1000, random_state=0)
# ReadMe's "full" model requires a binary feature matrix
encode_0_1 = Pipeline([('0_1_terms', CountVectorizer(min_df=5, binary=True))])
train, test = qp.data.preprocessing.instance_transformation(reviews, encode_0_1, inplace=True).train_test
model = ReadMe(prob_model='full', bootstrap_trials=100, bagging_trials=100, bagging_range=20, random_state=0)
model.fit(*train.Xy) # lazy: only bootstrap resampling happens here
estim_prevalence, conf_region = model.predict_conf(test.X)
```
Note that `ReadMe` is computationally expensive: its cost scales with the
product of `bootstrap_trials` and `bagging_trials`, each of which requires
solving a least-squares problem.
## Composable Methods