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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>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">clone</span>
<span class="kn">from</span><span class="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>
<span class="c1"># Wrappers of calibration defined by Alexandari et al. in paper &lt;http://proceedings.mlr.press/v119/alexandari20a.html&gt;</span>
<span class="c1"># requires &quot;pip install abstension&quot;</span>
<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">try</span><span class="p">:</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">abstention.calibration</span><span class="w"> </span><span class="kn">import</span> <span class="n">NoBiasVectorScaling</span><span class="p">,</span> <span class="n">TempScaling</span><span class="p">,</span> <span class="n">VectorScaling</span>
<span class="k">except</span> <span class="ne">ImportError</span> <span class="k">as</span> <span class="n">exc</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span>
<span class="s2">&quot;Calibration methods in quapy.classification.calibration require the optional &quot;</span>
<span class="s2">&quot;&#39;abstention&#39; package.&quot;</span>
<span class="p">)</span> <span class="kn">from</span><span class="w"> </span><span class="nn">exc</span>
<span class="k">return</span> <span class="n">NoBiasVectorScaling</span><span class="p">,</span> <span class="n">TempScaling</span><span class="p">,</span> <span class="n">VectorScaling</span>
<div class="viewcode-block" id="RecalibratedProbabilisticClassifier">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifier">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">RecalibratedProbabilisticClassifier</span><span class="p">:</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Abstract class for (re)calibration method from `abstention.calibration`, as defined in</span>
<span class="sd"> `Alexandari, A., Kundaje, A., &amp; Shrikumar, A. (2020, November). Maximum likelihood with bias-corrected calibration</span>
<span class="sd"> is hard-to-beat at label shift adaptation. In International Conference on Machine Learning (pp. 222-232). PMLR.</span>
<span class="sd"> &lt;http://proceedings.mlr.press/v119/alexandari20a.html&gt;`_:</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">pass</span></div>
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">RecalibratedProbabilisticClassifierBase</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">RecalibratedProbabilisticClassifier</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Applies a (re)calibration method from `abstention.calibration`, as defined in</span>
<span class="sd"> `Alexandari et al. paper &lt;http://proceedings.mlr.press/v119/alexandari20a.html&gt;`_.</span>
<span class="sd"> :param classifier: a scikit-learn probabilistic classifier</span>
<span class="sd"> :param calibrator: the calibration object (an instance of abstention.calibration.CalibratorFactory)</span>
<span class="sd"> :param val_split: indicate an integer k for performing kFCV to obtain the posterior probabilities, or a float p</span>
<span class="sd"> in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the</span>
<span class="sd"> training instances (the rest is used for training). In any case, the classifier is retrained in the whole</span>
<span class="sd"> training set afterwards. Default value is 5.</span>
<span class="sd"> :param n_jobs: indicate the number of parallel workers (only when val_split is an integer); default=None</span>
<span class="sd"> :param verbose: whether or not to display information in the standard output</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">calibrator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
<span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span> <span class="o">=</span> <span class="n">calibrator</span>
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span>
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.fit">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Fits the calibration for the probabilistic classifier.</span>
<span class="sd"> :param X: array-like of shape `(n_samples, n_features)` with the data instances</span>
<span class="sd"> :param y: array-like of shape `(n_samples,)` with the class labels</span>
<span class="sd"> :return: self</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">k</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">val_split</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
<span class="k">if</span> <span class="n">k</span> <span class="o">&lt;</span> <span class="mi">2</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;wrong value for val_split: the number of folds must be &gt; 2&#39;</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_cv</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">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="nb">float</span><span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="mi">0</span> <span class="o">&lt;</span> <span class="n">k</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;wrong value for val_split: the proportion of validation documents must be in (0,1)&#39;</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_tr_val</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span></div>
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.fit_cv">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_cv">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">fit_cv</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Fits the calibration in a cross-validation manner, i.e., it generates posterior probabilities for all</span>
<span class="sd"> training instances via cross-validation, and then retrains the classifier on all training instances.</span>
<span class="sd"> The posterior probabilities thus generated are used for calibrating the outputs of the classifier.</span>
<span class="sd"> :param X: array-like of shape `(n_samples, n_features)` with the data instances</span>
<span class="sd"> :param y: array-like of shape `(n_samples,)` with the class labels</span>
<span class="sd"> :return: self</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">posteriors</span> <span class="o">=</span> <span class="n">cross_val_predict</span><span class="p">(</span>
<span class="bp">self</span><span class="o">.</span><span class="n">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">cv</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">val_split</span><span class="p">,</span> <span class="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">verbose</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;predict_proba&#39;</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">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="n">nclasses</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">calibration_function</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span><span class="p">(</span><span class="n">posteriors</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">nclasses</span><span class="p">)[</span><span class="n">y</span><span class="p">],</span> <span class="n">posterior_supplied</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.fit_tr_val">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_tr_val">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">fit_tr_val</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Fits the calibration in a train/val-split manner, i.e.t, it partitions the training instances into a</span>
<span class="sd"> training and a validation set, and then uses the training samples to learn classifier which is then used</span>
<span class="sd"> to generate posterior probabilities for the held-out validation data. These posteriors are used to calibrate</span>
<span class="sd"> the classifier. The classifier is not retrained on the whole dataset.</span>
<span class="sd"> :param X: array-like of shape `(n_samples, n_features)` with the data instances</span>
<span class="sd"> :param y: array-like of shape `(n_samples,)` with the class labels</span>
<span class="sd"> :return: self</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">Xtr</span><span class="p">,</span> <span class="n">Xva</span><span class="p">,</span> <span class="n">ytr</span><span class="p">,</span> <span class="n">yva</span> <span class="o">=</span> <span class="n">train_test_split</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">test_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">val_split</span><span class="p">,</span> <span class="n">stratify</span><span class="o">=</span><span class="n">y</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">fit</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">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">Xva</span><span class="p">)</span>
<span class="n">nclasses</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">yva</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">calibration_function</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span><span class="p">(</span><span class="n">posteriors</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">nclasses</span><span class="p">)[</span><span class="n">yva</span><span class="p">],</span> <span class="n">posterior_supplied</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.predict">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.predict">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Predicts class labels for the data instances in `X`</span>
<span class="sd"> :param X: array-like of shape `(n_samples, n_features)` with the data instances</span>
<span class="sd"> :return: array-like of shape `(n_samples,)` with the class label predictions</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.predict_proba">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.predict_proba">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Generates posterior probabilities for the data instances in `X`</span>
<span class="sd"> :param X: array-like of shape `(n_samples, n_features)` with the data instances</span>
<span class="sd"> :return: array-like of shape `(n_samples, n_classes)` with posterior probabilities</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">posteriors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">calibration_function</span><span class="p">(</span><span class="n">posteriors</span><span class="p">)</span></div>
<span class="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="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Returns the classes on which the classifier has been trained on</span>
<span class="sd"> :return: array-like of shape `(n_classes)`</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">classes_</span></div>
<div class="viewcode-block" id="NBVSCalibration">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.NBVSCalibration">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">NBVSCalibration</span><span class="p">(</span><span class="n">RecalibratedProbabilisticClassifierBase</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Applies the No-Bias Vector Scaling (NBVS) calibration method from `abstention.calibration`, as defined in</span>
<span class="sd"> `Alexandari et al. paper &lt;http://proceedings.mlr.press/v119/alexandari20a.html&gt;`_:</span>
<span class="sd"> :param classifier: a scikit-learn probabilistic classifier</span>
<span class="sd"> :param val_split: indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p</span>
<span class="sd"> in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the</span>
<span class="sd"> training instances (the rest is used for training). In any case, the classifier is retrained in the whole</span>
<span class="sd"> training set afterwards. Default value is 5.</span>
<span class="sd"> :param n_jobs: indicate the number of parallel workers (only when val_split is an integer)</span>
<span class="sd"> :param verbose: whether or not to display information in the standard output</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="n">NoBiasVectorScaling</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_require_abstention_calibration</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
<span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span> <span class="o">=</span> <span class="n">NoBiasVectorScaling</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span></div>
<div class="viewcode-block" id="BCTSCalibration">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.BCTSCalibration">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">BCTSCalibration</span><span class="p">(</span><span class="n">RecalibratedProbabilisticClassifierBase</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Applies the Bias-Corrected Temperature Scaling (BCTS) calibration method from `abstention.calibration`, as defined in</span>
<span class="sd"> `Alexandari et al. paper &lt;http://proceedings.mlr.press/v119/alexandari20a.html&gt;`_:</span>
<span class="sd"> :param classifier: a scikit-learn probabilistic classifier</span>
<span class="sd"> :param val_split: indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p</span>
<span class="sd"> in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the</span>
<span class="sd"> training instances (the rest is used for training). In any case, the classifier is retrained in the whole</span>
<span class="sd"> training set afterwards. Default value is 5.</span>
<span class="sd"> :param n_jobs: indicate the number of parallel workers (only when val_split is an integer)</span>
<span class="sd"> :param verbose: whether or not to display information in the standard output</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="n">_</span><span class="p">,</span> <span class="n">TempScaling</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_require_abstention_calibration</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
<span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span> <span class="o">=</span> <span class="n">TempScaling</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">,</span> <span class="n">bias_positions</span><span class="o">=</span><span class="s1">&#39;all&#39;</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span></div>
<div class="viewcode-block" id="TSCalibration">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.TSCalibration">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">TSCalibration</span><span class="p">(</span><span class="n">RecalibratedProbabilisticClassifierBase</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Applies the Temperature Scaling (TS) calibration method from `abstention.calibration`, as defined in</span>
<span class="sd"> `Alexandari et al. paper &lt;http://proceedings.mlr.press/v119/alexandari20a.html&gt;`_:</span>
<span class="sd"> :param classifier: a scikit-learn probabilistic classifier</span>
<span class="sd"> :param val_split: indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p</span>
<span class="sd"> in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the</span>
<span class="sd"> training instances (the rest is used for training). In any case, the classifier is retrained in the whole</span>
<span class="sd"> training set afterwards. Default value is 5.</span>
<span class="sd"> :param n_jobs: indicate the number of parallel workers (only when val_split is an integer)</span>
<span class="sd"> :param verbose: whether or not to display information in the standard output</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="n">_</span><span class="p">,</span> <span class="n">TempScaling</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_require_abstention_calibration</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
<span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span> <span class="o">=</span> <span class="n">TempScaling</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span></div>
<div class="viewcode-block" id="VSCalibration">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.VSCalibration">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">VSCalibration</span><span class="p">(</span><span class="n">RecalibratedProbabilisticClassifierBase</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Applies the Vector Scaling (VS) calibration method from `abstention.calibration`, as defined in</span>
<span class="sd"> `Alexandari et al. paper &lt;http://proceedings.mlr.press/v119/alexandari20a.html&gt;`_:</span>
<span class="sd"> :param classifier: a scikit-learn probabilistic classifier</span>
<span class="sd"> :param val_split: indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p</span>
<span class="sd"> in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the</span>
<span class="sd"> training instances (the rest is used for training). In any case, the classifier is retrained in the whole</span>
<span class="sd"> training set afterwards. Default value is 5.</span>
<span class="sd"> :param n_jobs: indicate the number of parallel workers (only when val_split is an integer)</span>
<span class="sd"> :param verbose: whether or not to display information in the standard output</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">VectorScaling</span> <span class="o">=</span> <span class="n">_require_abstention_calibration</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
<span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span> <span class="o">=</span> <span class="n">VectorScaling</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span></div>
<div class="viewcode-block" id="TemperatureScalingFromLogits">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.TemperatureScalingFromLogits">[docs]</a>
<span class="k">class</span><span class="w"> </span><span class="nc">TemperatureScalingFromLogits</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Calibrates a matrix of logits by learning a temperature-scaling mapping</span>
<span class="sd"> with the calibration methods from `abstention.calibration`.</span>
<span class="sd"> This estimator is useful when the inputs are already logits produced by a</span>
<span class="sd"> pretrained classifier, and the goal is to transform them directly into</span>
<span class="sd"> calibrated posterior probabilities without retraining the underlying model.</span>
<span class="sd"> :param bias_corrected: if True, uses Bias-Corrected Temperature Scaling</span>
<span class="sd"> (BCTS); otherwise, uses standard Temperature Scaling (TS)</span>
<span class="sd"> :param verbose: whether the underlying calibrator should display progress</span>
<span class="sd"> information</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">bias_corrected</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">bias_corrected</span> <span class="o">=</span> <span class="n">bias_corrected</span>
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span>
<div class="viewcode-block" id="TemperatureScalingFromLogits.fit">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.TemperatureScalingFromLogits.fit">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Fits the logits calibrator.</span>
<span class="sd"> :param X: array-like of shape `(n_samples, n_classes)` containing</span>
<span class="sd"> logits</span>
<span class="sd"> :param y: array-like of shape `(n_samples,)` containing class labels</span>
<span class="sd"> :return: self</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">check_X_y</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">label_encoder_</span> <span class="o">=</span> <span class="n">LabelEncoder</span><span class="p">()</span>
<span class="n">y_enc</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_encoder_</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_encoder_</span><span class="o">.</span><span class="n">classes_</span>
<span class="n">n_classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
<span class="n">logits_dim</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="k">if</span> <span class="n">n_classes</span> <span class="o">!=</span> <span class="n">logits_dim</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
<span class="sa">f</span><span class="s1">&#39;mismatch between the number of classes (</span><span class="si">{</span><span class="n">n_classes</span><span class="si">}</span><span class="s1">) and the &#39;</span>
<span class="sa">f</span><span class="s1">&#39;dimensionality of the logits (</span><span class="si">{</span><span class="n">logits_dim</span><span class="si">}</span><span class="s1">)&#39;</span>
<span class="p">)</span>
<span class="n">_</span><span class="p">,</span> <span class="n">TempScaling</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_require_abstention_calibration</span><span class="p">()</span>
<span class="n">calibrator</span> <span class="o">=</span> <span class="n">TempScaling</span><span class="p">(</span>
<span class="n">verbose</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">,</span>
<span class="n">bias_positions</span><span class="o">=</span><span class="s1">&#39;all&#39;</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias_corrected</span> <span class="k">else</span> <span class="p">[],</span>
<span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">calibrator_</span> <span class="o">=</span> <span class="n">calibrator</span>
<span class="bp">self</span><span class="o">.</span><span class="n">calibration_function_</span> <span class="o">=</span> <span class="n">calibrator</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_classes</span><span class="p">)[</span><span class="n">y_enc</span><span class="p">])</span>
<span class="k">return</span> <span class="bp">self</span></div>
<div class="viewcode-block" id="TemperatureScalingFromLogits.predict_proba">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.TemperatureScalingFromLogits.predict_proba">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Converts logits into calibrated posterior probabilities.</span>
<span class="sd"> :param X: array-like of shape `(n_samples, n_classes)` containing</span>
<span class="sd"> logits</span>
<span class="sd"> :return: array-like of shape `(n_samples, n_classes)` with calibrated</span>
<span class="sd"> posterior probabilities</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">calibration_function_</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
<div class="viewcode-block" id="TemperatureScalingFromLogits.predict">
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.TemperatureScalingFromLogits.predict">[docs]</a>
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Predicts class labels after calibration.</span>
<span class="sd"> :param X: array-like of shape `(n_samples, n_classes)` containing</span>
<span class="sd"> logits</span>
<span class="sd"> :return: array-like of shape `(n_samples,)` with class label</span>
<span class="sd"> predictions</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">posteriors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_encoder_</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">posteriors</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span></div>
</div>
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