refactoring and cleaning up
This commit is contained in:
Alejandro Moreo
parent
e44056d860
commit
a1dff57db0
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@ -8,9 +8,10 @@ Change Log 0.2.1
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- Extended KDEy with Aitchison/ILR kernels, shrinkage, and improved numerical stability.
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- Added TemperatureScalingFromLogits for calibrating pretrained logits.
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- Added DirichletProtocol for prevalence sampling from Dirichlet priors.
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- Added ReadMe method by Daniel Hopkins and Gary King
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- Added ReadMe method by Daniel Hopkins and Gary King.
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- Internal index in LabelledCollection is now "lazy", and is only constructed if required.
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- Improved unit testing and separated integration tests
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- Improved unit testing and separated integration tests.
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- Added RLLS (Regularized Learning for Domain Adaptation under Label Shifts) method.
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Change Log 0.2.0
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-----------------
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@ -82,43 +339,88 @@
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@ -71,12 +74,13 @@
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<div itemprop="articleBody">
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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></span><span class="kn">from</span><span class="w"> </span><span class="nn">copy</span><span class="w"> </span><span class="kn">import</span> <span class="n">deepcopy</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">abstention.calibration</span> <span class="kn">import</span> <span class="n">NoBiasVectorScaling</span><span class="p">,</span> <span class="n">TempScaling</span><span class="p">,</span> <span class="n">VectorScaling</span>
|
||||
<span class="kn">from</span> <span class="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">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.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 <http://proceedings.mlr.press/v119/alexandari20a.html></span>
|
||||
|
|
@ -84,7 +88,20 @@
|
|||
<span class="c1"># see https://github.com/kundajelab/abstention</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="RecalibratedProbabilisticClassifier"><a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifier">[docs]</a><span class="k">class</span> <span class="nc">RecalibratedProbabilisticClassifier</span><span class="p">:</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_require_abstention_calibration</span><span class="p">():</span>
|
||||
<span class="k">try</span><span class="p">:</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">abstention.calibration</span><span class="w"> </span><span class="kn">import</span> <span class="n">NoBiasVectorScaling</span><span class="p">,</span> <span class="n">TempScaling</span><span class="p">,</span> <span class="n">VectorScaling</span>
|
||||
<span class="k">except</span> <span class="ne">ImportError</span> <span class="k">as</span> <span class="n">exc</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span>
|
||||
<span class="s2">"Calibration methods in quapy.classification.calibration require the optional "</span>
|
||||
<span class="s2">"'abstention' package."</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">"""</span>
|
||||
<span class="sd"> Abstract class for (re)calibration method from `abstention.calibration`, as defined in</span>
|
||||
<span class="sd"> `Alexandari, A., Kundaje, A., & Shrikumar, A. (2020, November). Maximum likelihood with bias-corrected calibration</span>
|
||||
|
|
@ -94,7 +111,10 @@
|
|||
<span class="k">pass</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase"><a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase">[docs]</a><span class="k">class</span> <span class="nc">RecalibratedProbabilisticClassifierBase</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">RecalibratedProbabilisticClassifier</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">RecalibratedProbabilisticClassifierBase</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">RecalibratedProbabilisticClassifier</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Applies a (re)calibration method from `abstention.calibration`, as defined in</span>
|
||||
<span class="sd"> `Alexandari et al. paper <http://proceedings.mlr.press/v119/alexandari20a.html>`_.</span>
|
||||
|
|
@ -110,14 +130,16 @@
|
|||
<span class="sd"> :param verbose: whether or not to display information in the standard output</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">calibrator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">calibrator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">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="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.fit">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Fits the calibration for the probabilistic classifier.</span>
|
||||
|
||||
|
|
@ -135,7 +157,10 @@
|
|||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'wrong value for val_split: the proportion of validation documents must be in (0,1)'</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="nf">fit_cv</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.fit_cv">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_cv">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">fit_cv</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -153,7 +178,10 @@
|
|||
<span class="bp">self</span><span class="o">.</span><span class="n">calibration_function</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span><span class="p">(</span><span class="n">posteriors</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">nclasses</span><span class="p">)[</span><span class="n">y</span><span class="p">],</span> <span class="n">posterior_supplied</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.fit_tr_val"><a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_tr_val">[docs]</a> <span class="k">def</span> <span class="nf">fit_tr_val</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.fit_tr_val">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_tr_val">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">fit_tr_val</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -171,7 +199,10 @@
|
|||
<span class="bp">self</span><span class="o">.</span><span class="n">calibration_function</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span><span class="p">(</span><span class="n">posteriors</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">nclasses</span><span class="p">)[</span><span class="n">yva</span><span class="p">],</span> <span class="n">posterior_supplied</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.predict"><a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.predict">[docs]</a> <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.predict">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.predict">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Predicts class labels for the data instances in `X`</span>
|
||||
|
||||
|
|
@ -180,7 +211,10 @@
|
|||
<span class="sd"> """</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="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.predict_proba">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.predict_proba">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Generates posterior probabilities for the data instances in `X`</span>
|
||||
|
||||
|
|
@ -190,8 +224,9 @@
|
|||
<span class="n">posteriors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="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="nf">classes_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">classes_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns the classes on which the classifier has been trained on</span>
|
||||
|
||||
|
|
@ -200,7 +235,10 @@
|
|||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">classes_</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="NBVSCalibration"><a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.NBVSCalibration">[docs]</a><span class="k">class</span> <span class="nc">NBVSCalibration</span><span class="p">(</span><span class="n">RecalibratedProbabilisticClassifierBase</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="NBVSCalibration">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.NBVSCalibration">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">NBVSCalibration</span><span class="p">(</span><span class="n">RecalibratedProbabilisticClassifierBase</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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 <http://proceedings.mlr.press/v119/alexandari20a.html>`_:</span>
|
||||
|
|
@ -214,7 +252,8 @@
|
|||
<span class="sd"> :param verbose: whether or not to display information in the standard output</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="n">NoBiasVectorScaling</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_require_abstention_calibration</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">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>
|
||||
|
|
@ -222,7 +261,10 @@
|
|||
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="BCTSCalibration"><a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.BCTSCalibration">[docs]</a><span class="k">class</span> <span class="nc">BCTSCalibration</span><span class="p">(</span><span class="n">RecalibratedProbabilisticClassifierBase</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="BCTSCalibration">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.BCTSCalibration">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">BCTSCalibration</span><span class="p">(</span><span class="n">RecalibratedProbabilisticClassifierBase</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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 <http://proceedings.mlr.press/v119/alexandari20a.html>`_:</span>
|
||||
|
|
@ -236,7 +278,8 @@
|
|||
<span class="sd"> :param verbose: whether or not to display information in the standard output</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="n">_</span><span class="p">,</span> <span class="n">TempScaling</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_require_abstention_calibration</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">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">'all'</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>
|
||||
|
|
@ -244,7 +287,10 @@
|
|||
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="TSCalibration"><a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.TSCalibration">[docs]</a><span class="k">class</span> <span class="nc">TSCalibration</span><span class="p">(</span><span class="n">RecalibratedProbabilisticClassifierBase</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="TSCalibration">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.TSCalibration">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">TSCalibration</span><span class="p">(</span><span class="n">RecalibratedProbabilisticClassifierBase</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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 <http://proceedings.mlr.press/v119/alexandari20a.html>`_:</span>
|
||||
|
|
@ -258,7 +304,8 @@
|
|||
<span class="sd"> :param verbose: whether or not to display information in the standard output</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="n">_</span><span class="p">,</span> <span class="n">TempScaling</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_require_abstention_calibration</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">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>
|
||||
|
|
@ -266,7 +313,10 @@
|
|||
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="VSCalibration"><a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.VSCalibration">[docs]</a><span class="k">class</span> <span class="nc">VSCalibration</span><span class="p">(</span><span class="n">RecalibratedProbabilisticClassifierBase</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="VSCalibration">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.VSCalibration">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">VSCalibration</span><span class="p">(</span><span class="n">RecalibratedProbabilisticClassifierBase</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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 <http://proceedings.mlr.press/v119/alexandari20a.html>`_:</span>
|
||||
|
|
@ -280,13 +330,101 @@
|
|||
<span class="sd"> :param verbose: whether or not to display information in the standard output</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">VectorScaling</span> <span class="o">=</span> <span class="n">_require_abstention_calibration</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">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">"""</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"> """</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">"""</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"> """</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">'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 '</span>
|
||||
<span class="sa">f</span><span class="s1">'dimensionality of the logits (</span><span class="si">{</span><span class="n">logits_dim</span><span class="si">}</span><span class="s1">)'</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">'all'</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">"""</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"> """</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">"""</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"> """</span>
|
||||
<span class="n">posteriors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_encoder_</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">posteriors</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span></div>
|
||||
</div>
|
||||
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
|
|
|
|||
|
|
@ -1,22 +1,20 @@
|
|||
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en" data-content_root="../../../">
|
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<head>
|
||||
<meta charset="utf-8" />
|
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
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<title>quapy.classification.methods — QuaPy: A Python-based open-source framework for quantification 0.1.8 documentation</title>
|
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<link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=92fd9be5" />
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|
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<title>quapy.classification.methods — QuaPy: A Python-based open-source framework for quantification 0.2.1 documentation</title>
|
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<link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=b86133f3" />
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|
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@ -42,7 +40,13 @@
|
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<li class="toctree-l1"><a class="reference internal" href="../../../modules.html">quapy</a></li>
|
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<li class="toctree-l1"><a class="reference internal" href="../../../index.html">Quickstart</a></li>
|
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|
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<ul>
|
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<li class="toctree-l1"><a class="reference internal" href="../../../manuals.html">Manuals</a></li>
|
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|
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<ul>
|
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<li class="toctree-l1"><a class="reference internal" href="../../../quapy.html">API</a></li>
|
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</ul>
|
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</div>
|
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|
|
@ -70,14 +74,15 @@
|
|||
<div itemprop="articleBody">
|
||||
|
||||
<h1>Source code for quapy.classification.methods</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">BaseEstimator</span>
|
||||
<span 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>
|
||||
<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>
|
||||
|
||||
|
||||
<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="nc">LowRankLogisticRegression</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">):</span>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">LowRankLogisticRegression</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -91,13 +96,13 @@
|
|||
<span class="sd"> `Logistic Regression <https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html>`__ classifier</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_components</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_components</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_components</span> <span class="o">=</span> <span class="n">n_components</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">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="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Get hyper-parameters for this estimator.</span>
|
||||
|
||||
|
|
@ -110,7 +115,7 @@
|
|||
|
||||
<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="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">params</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">params</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Set the parameters of this estimator.</span>
|
||||
|
||||
|
|
@ -127,7 +132,7 @@
|
|||
|
||||
<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="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -148,7 +153,7 @@
|
|||
|
||||
<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="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Predicts labels for the instances `X` embedded into the low-rank space.</span>
|
||||
|
||||
|
|
@ -162,7 +167,7 @@
|
|||
|
||||
<div class="viewcode-block" id="LowRankLogisticRegression.predict_proba">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.predict_proba">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Predicts posterior probabilities for the instances `X` embedded into the low-rank space.</span>
|
||||
|
||||
|
|
@ -175,7 +180,7 @@
|
|||
|
||||
<div class="viewcode-block" id="LowRankLogisticRegression.transform">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.transform">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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` >= `X.shape[1]`.</span>
|
||||
|
|
@ -188,6 +193,38 @@
|
|||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MockClassifierFromPosteriors">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.MockClassifierFromPosteriors">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">MockClassifierFromPosteriors</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Mock classifier that bypasses classifier training when the input instances</span>
|
||||
<span class="sd"> are already posterior probabilities produced by a pretrained probabilistic</span>
|
||||
<span class="sd"> classifier.</span>
|
||||
|
||||
<span class="sd"> :param X: arrays of shape `(n_samples, n_classes)` are interpreted as posterior probabilities</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<div class="viewcode-block" id="MockClassifierFromPosteriors.fit">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.MockClassifierFromPosteriors.fit">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MockClassifierFromPosteriors.predict">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.MockClassifierFromPosteriors.predict">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MockClassifierFromPosteriors.predict_proba">
|
||||
<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.MockClassifierFromPosteriors.predict_proba">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="n">X</span></div>
|
||||
</div>
|
||||
|
||||
</pre></div>
|
||||
|
||||
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|
||||
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@ -70,21 +74,21 @@
|
|||
<div itemprop="articleBody">
|
||||
|
||||
<h1>Source code for quapy.classification.svmperf</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">import</span> <span class="nn">random</span>
|
||||
<span 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>
|
||||
<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>
|
||||
|
||||
|
||||
<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="nc">SVMperf</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">ClassifierMixin</span><span class="p">):</span>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">SVMperf</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">ClassifierMixin</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""A wrapper for the `SVM-perf package <https://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html>`__ 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 <https://hlt-isti.github.io/QuaPy/build/html/Installation.html#svm-perf-with-quantification-oriented-losses>`__</span>
|
||||
|
|
@ -106,31 +110,20 @@
|
|||
<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">'01'</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span> <span class="s1">'f1'</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span> <span class="s1">'kld'</span><span class="p">:</span><span class="mi">12</span><span class="p">,</span> <span class="s1">'nkld'</span><span class="p">:</span><span class="mi">13</span><span class="p">,</span> <span class="s1">'q'</span><span class="p">:</span><span class="mi">22</span><span class="p">,</span> <span class="s1">'qacc'</span><span class="p">:</span><span class="mi">23</span><span class="p">,</span> <span class="s1">'qf1'</span><span class="p">:</span><span class="mi">24</span><span class="p">,</span> <span class="s1">'qgm'</span><span class="p">:</span><span class="mi">25</span><span class="p">,</span> <span class="s1">'mae'</span><span class="p">:</span><span class="mi">26</span><span class="p">,</span> <span class="s1">'mrae'</span><span class="p">:</span><span class="mi">27</span><span class="p">}</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">svmperf_base</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">'01'</span><span class="p">,</span> <span class="n">host_folder</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="k">assert</span> <span class="n">exists</span><span class="p">(</span><span class="n">svmperf_base</span><span class="p">),</span> <span class="sa">f</span><span class="s1">'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'</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">'01'</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">'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;'</span>
|
||||
<span class="sa">f</span><span class="s1">'did you install svm-perf? '</span>
|
||||
<span class="sa">f</span><span class="s1">'see instructions in https://hlt-isti.github.io/QuaPy/manuals/explicit-loss-minimization.html'</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">svmperf_base</span> <span class="o">=</span> <span class="n">svmperf_base</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">C</span> <span class="o">=</span> <span class="n">C</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">loss</span> <span class="o">=</span> <span class="n">loss</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">host_folder</span> <span class="o">=</span> <span class="n">host_folder</span>
|
||||
|
||||
<span class="c1"># def set_params(self, **parameters):</span>
|
||||
<span class="c1"># """</span>
|
||||
<span class="c1"># Set the hyper-parameters for svm-perf. Currently, only the `C` and `loss` parameters are supported</span>
|
||||
<span class="c1">#</span>
|
||||
<span class="c1"># :param parameters: a `**kwargs` dictionary `{'C': <float>}`</span>
|
||||
<span class="c1"># """</span>
|
||||
<span class="c1"># assert sorted(list(parameters.keys())) == ['C', 'loss'], \</span>
|
||||
<span class="c1"># 'currently, only the C and loss parameters are supported'</span>
|
||||
<span class="c1"># self.C = parameters.get('C', self.C)</span>
|
||||
<span class="c1"># self.loss = parameters.get('loss', self.loss)</span>
|
||||
<span class="c1">#</span>
|
||||
<span class="c1"># def get_params(self, deep=True):</span>
|
||||
<span class="c1"># return {'C': self.C, 'loss': self.loss}</span>
|
||||
|
||||
<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="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Trains the SVM for the multivariate performance loss</span>
|
||||
|
||||
|
|
@ -180,7 +173,7 @@
|
|||
|
||||
<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="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Predicts labels for the instances `X`</span>
|
||||
|
||||
|
|
@ -195,7 +188,7 @@
|
|||
|
||||
<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="nf">decision_function</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">decision_function</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Evaluate the decision function for the samples in `X`.</span>
|
||||
|
||||
|
|
@ -231,7 +224,7 @@
|
|||
<span class="k">return</span> <span class="n">scores</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="fm">__del__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__del__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">'tmpdir'</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>
|
||||
|
||||
|
|
|
|||
|
|
@ -1,22 +1,20 @@
|
|||
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en" data-content_root="../../../">
|
||||
<head>
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>quapy.data.base — QuaPy: A Python-based open-source framework for quantification 0.1.8 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=92fd9be5" />
|
||||
<link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css?v=19f00094" />
|
||||
<title>quapy.data.base — QuaPy: A Python-based open-source framework for quantification 0.2.1 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=b86133f3" />
|
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<link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css?v=9edc463e" />
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<!--[if lt IE 9]>
|
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<script src="../../../_static/js/html5shiv.min.js"></script>
|
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<![endif]-->
|
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<script src="../../../_static/jquery.js?v=5d32c60e"></script>
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<script src="../../../_static/doctools.js?v=9a2dae69"></script>
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<script src="../../../_static/sphinx_highlight.js?v=dc90522c"></script>
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<script src="../../../_static/jquery.js?v=5d32c60e"></script>
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<script src="../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
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<script src="../../../_static/documentation_options.js?v=37f418d5"></script>
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<script src="../../../_static/doctools.js?v=fd6eb6e6"></script>
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<script src="../../../_static/sphinx_highlight.js?v=6ffebe34"></script>
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||||
<script src="../../../_static/js/theme.js"></script>
|
||||
<link rel="index" title="Index" href="../../../genindex.html" />
|
||||
<link rel="search" title="Search" href="../../../search.html" />
|
||||
|
|
@ -42,7 +40,13 @@
|
|||
</div>
|
||||
</div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../modules.html">quapy</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../index.html">Quickstart</a></li>
|
||||
</ul>
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../manuals.html">Manuals</a></li>
|
||||
</ul>
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../quapy.html">API</a></li>
|
||||
</ul>
|
||||
|
||||
</div>
|
||||
|
|
@ -70,22 +74,23 @@
|
|||
<div itemprop="articleBody">
|
||||
|
||||
<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></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>
|
||||
|
||||
<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="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>
|
||||
|
||||
|
||||
<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="nc">LabelledCollection</span><span class="p">:</span>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">LabelledCollection</span><span class="p">:</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -97,7 +102,7 @@
|
|||
<span class="sd"> (i.e., a prevalence of 0)</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">issparse</span><span class="p">(</span><span class="n">instances</span><span class="p">):</span>
|
||||
<span class="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">></span> <span class="mi">0</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">instances</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">str</span><span class="p">):</span>
|
||||
|
|
@ -106,21 +111,25 @@
|
|||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">instances</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span>
|
||||
<span class="n">n_docs</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">classes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">classes_from_labels</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">classes</span><span class="p">))</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">)</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">classes</span><span class="p">)))</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'labels (</span><span class="si">{</span><span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">)</span><span class="si">}</span><span class="s1">) contain values not included in classes_ (</span><span class="si">{</span><span class="nb">set</span><span class="p">(</span><span class="n">classes</span><span class="p">)</span><span class="si">}</span><span class="s1">)'</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="p">{</span><span class="n">class_</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">n_docs</span><span class="p">)[</span><span class="bp">self</span><span class="o">.</span><span class="n">labels</span> <span class="o">==</span> <span class="n">class_</span><span class="p">]</span> <span class="k">for</span> <span class="n">class_</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">}</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_index</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">index</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">'_index'</span><span class="p">)</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_index</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_index</span> <span class="o">=</span> <span class="p">{</span><span class="n">class_</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">))[</span><span class="bp">self</span><span class="o">.</span><span class="n">labels</span> <span class="o">==</span> <span class="n">class_</span><span class="p">]</span> <span class="k">for</span> <span class="n">class_</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">}</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_index</span>
|
||||
|
||||
<div class="viewcode-block" id="LabelledCollection.load">
|
||||
<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="nf">load</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">loader_func</span><span class="p">:</span> <span class="n">callable</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">loader_kwargs</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">load</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">loader_func</span><span class="p">:</span> <span class="nb">callable</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">loader_kwargs</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -137,7 +146,7 @@
|
|||
<span class="k">return</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="o">*</span><span class="n">loader_func</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="o">**</span><span class="n">loader_kwargs</span><span class="p">),</span> <span class="n">classes</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns the length of this collection (number of labelled instances)</span>
|
||||
|
||||
|
|
@ -147,7 +156,7 @@
|
|||
|
||||
<div class="viewcode-block" id="LabelledCollection.prevalence">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.prevalence">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">prevalence</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">prevalence</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns the prevalence, or relative frequency, of the classes in the codeframe.</span>
|
||||
|
||||
|
|
@ -159,7 +168,7 @@
|
|||
|
||||
<div class="viewcode-block" id="LabelledCollection.counts">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.counts">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">counts</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">counts</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns the number of instances for each of the classes in the codeframe.</span>
|
||||
|
||||
|
|
@ -170,7 +179,7 @@
|
|||
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">n_classes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">n_classes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> The number of classes</span>
|
||||
|
||||
|
|
@ -179,7 +188,16 @@
|
|||
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">binary</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">n_instances</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> The number of instances</span>
|
||||
|
||||
<span class="sd"> :return: integer</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">)</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">binary</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns True if the number of classes is 2</span>
|
||||
|
||||
|
|
@ -189,12 +207,11 @@
|
|||
|
||||
<div class="viewcode-block" id="LabelledCollection.sampling_index">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.sampling_index">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">sampling_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="o">*</span><span class="n">prevs</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">sampling_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="o">*</span><span class="n">prevs</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns an index to be used to extract a random sample of desired size and desired prevalence values. If the</span>
|
||||
<span class="sd"> prevalence values are not specified, then returns the index of a uniform sampling.</span>
|
||||
<span class="sd"> For each class, the sampling is drawn with replacement if the requested prevalence is larger than</span>
|
||||
<span class="sd"> the actual prevalence of the class, or without replacement otherwise.</span>
|
||||
<span class="sd"> For each class, the sampling is drawn with replacement.</span>
|
||||
|
||||
<span class="sd"> :param size: integer, the requested size</span>
|
||||
<span class="sd"> :param prevs: the prevalence for each class; the prevalence value for the last class can be lead empty since</span>
|
||||
|
|
@ -209,7 +226,7 @@
|
|||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">prevs</span><span class="p">)</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">prevs</span> <span class="o">=</span> <span class="n">prevs</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="nb">sum</span><span class="p">(</span><span class="n">prevs</span><span class="p">),)</span>
|
||||
<span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">prevs</span><span class="p">)</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span><span class="p">,</span> <span class="s1">'unexpected number of prevalences'</span>
|
||||
<span class="k">assert</span> <span class="nb">sum</span><span class="p">(</span><span class="n">prevs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'prevalences (</span><span class="si">{</span><span class="n">prevs</span><span class="si">}</span><span class="s1">) wrong range (sum=</span><span class="si">{</span><span class="nb">sum</span><span class="p">(</span><span class="n">prevs</span><span class="p">)</span><span class="si">}</span><span class="s1">)'</span>
|
||||
<span class="k">assert</span> <span class="n">np</span><span class="o">.</span><span class="n">isclose</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">prevs</span><span class="p">),</span> <span class="mi">1</span><span class="p">),</span> <span class="sa">f</span><span class="s1">'prevalences (</span><span class="si">{</span><span class="n">prevs</span><span class="si">}</span><span class="s1">) wrong range (sum=</span><span class="si">{</span><span class="nb">sum</span><span class="p">(</span><span class="n">prevs</span><span class="p">)</span><span class="si">}</span><span class="s1">)'</span>
|
||||
|
||||
<span class="c1"># Decide how many instances should be taken for each class in order to satisfy the requested prevalence</span>
|
||||
<span class="c1"># accurately, and the number of instances in the sample (exactly). If int(size * prevs[i]) (which is</span>
|
||||
|
|
@ -238,7 +255,7 @@
|
|||
<span class="k">for</span> <span class="n">class_</span><span class="p">,</span> <span class="n">n_requested</span> <span class="ow">in</span> <span class="n">n_requests</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
|
||||
<span class="n">n_candidates</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">class_</span><span class="p">])</span>
|
||||
<span class="n">index_sample</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">class_</span><span class="p">][</span>
|
||||
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">n_candidates</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">n_requested</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="p">(</span><span class="n">n_requested</span> <span class="o">></span> <span class="n">n_candidates</span><span class="p">))</span>
|
||||
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">n_candidates</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">n_requested</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="p">]</span> <span class="k">if</span> <span class="n">n_requested</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">indexes_sample</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">index_sample</span><span class="p">)</span>
|
||||
|
|
@ -253,11 +270,10 @@
|
|||
|
||||
<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="nf">uniform_sampling_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">uniform_sampling_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns an index to be used to extract a uniform sample of desired size. The sampling is drawn</span>
|
||||
<span class="sd"> with replacement if the requested size is greater than the number of instances, or without replacement</span>
|
||||
<span class="sd"> otherwise.</span>
|
||||
<span class="sd"> with replacement.</span>
|
||||
|
||||
<span class="sd"> :param size: integer, the size of the uniform sample</span>
|
||||
<span class="sd"> :param random_state: if specified, guarantees reproducibility of the split.</span>
|
||||
|
|
@ -267,16 +283,15 @@
|
|||
<span class="n">ng</span> <span class="o">=</span> <span class="n">RandomState</span><span class="p">(</span><span class="n">seed</span><span class="o">=</span><span class="n">random_state</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span>
|
||||
<span class="k">return</span> <span class="n">ng</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">),</span> <span class="n">size</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="n">size</span> <span class="o">></span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">))</span></div>
|
||||
<span class="k">return</span> <span class="n">ng</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="p">),</span> <span class="n">size</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="LabelledCollection.sampling">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.sampling">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">sampling</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="o">*</span><span class="n">prevs</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">sampling</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="o">*</span><span class="n">prevs</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Return a random sample (an instance of :class:`LabelledCollection`) of desired size and desired prevalence</span>
|
||||
<span class="sd"> values. For each class, the sampling is drawn without replacement if the requested prevalence is larger than</span>
|
||||
<span class="sd"> the actual prevalence of the class, or with replacement otherwise.</span>
|
||||
<span class="sd"> values. For each class, the sampling is drawn with replacement.</span>
|
||||
|
||||
<span class="sd"> :param size: integer, the requested size</span>
|
||||
<span class="sd"> :param prevs: the prevalence for each class; the prevalence value for the last class can be lead empty since</span>
|
||||
|
|
@ -293,11 +308,10 @@
|
|||
|
||||
<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="nf">uniform_sampling</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">uniform_sampling</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns a uniform sample (an instance of :class:`LabelledCollection`) of desired size. The sampling is drawn</span>
|
||||
<span class="sd"> with replacement if the requested size is greater than the number of instances, or without replacement</span>
|
||||
<span class="sd"> otherwise.</span>
|
||||
<span class="sd"> with replacement.</span>
|
||||
|
||||
<span class="sd"> :param size: integer, the requested size</span>
|
||||
<span class="sd"> :param random_state: if specified, guarantees reproducibility of the split.</span>
|
||||
|
|
@ -309,7 +323,7 @@
|
|||
|
||||
<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="nf">sampling_from_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">sampling_from_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -324,7 +338,7 @@
|
|||
|
||||
<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="nf">split_stratified</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">train_prop</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">split_stratified</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">train_prop</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns two instances of :class:`LabelledCollection` split with stratification from this collection, at desired</span>
|
||||
<span class="sd"> proportion.</span>
|
||||
|
|
@ -336,17 +350,17 @@
|
|||
<span class="sd"> :return: two instances of :class:`LabelledCollection`, the first one with `train_prop` elements, and the</span>
|
||||
<span class="sd"> second one with `1-train_prop` elements</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">tr_docs</span><span class="p">,</span> <span class="n">te_docs</span><span class="p">,</span> <span class="n">tr_labels</span><span class="p">,</span> <span class="n">te_labels</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
|
||||
<span class="n">tr_X</span><span class="p">,</span> <span class="n">te_X</span><span class="p">,</span> <span class="n">tr_y</span><span class="p">,</span> <span class="n">te_y</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">train_size</span><span class="o">=</span><span class="n">train_prop</span><span class="p">,</span> <span class="n">stratify</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span>
|
||||
<span class="p">)</span>
|
||||
<span class="n">training</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">tr_docs</span><span class="p">,</span> <span class="n">tr_labels</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
|
||||
<span class="n">test</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">te_docs</span><span class="p">,</span> <span class="n">te_labels</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
|
||||
<span class="n">training</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">tr_X</span><span class="p">,</span> <span class="n">tr_y</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
|
||||
<span class="n">test</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">te_X</span><span class="p">,</span> <span class="n">te_y</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">training</span><span class="p">,</span> <span class="n">test</span></div>
|
||||
|
||||
|
||||
<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="nf">split_random</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">train_prop</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">split_random</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">train_prop</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns two instances of :class:`LabelledCollection` split randomly from this collection, at desired</span>
|
||||
<span class="sd"> proportion.</span>
|
||||
|
|
@ -373,7 +387,7 @@
|
|||
<span class="k">return</span> <span class="n">training</span><span class="p">,</span> <span class="n">test</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="fm">__add__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__add__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">other</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -389,7 +403,7 @@
|
|||
<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="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">'LabelledCollection'</span><span class="p">]):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">join</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="s1">'LabelledCollection'</span><span class="p">]):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns a new :class:`LabelledCollection` as the union of the collections given in input.</span>
|
||||
|
||||
|
|
@ -430,7 +444,16 @@
|
|||
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">Xy</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">classes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Gets an array-like with the classes used in this collection</span>
|
||||
|
||||
<span class="sd"> :return: array-like</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">Xy</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Gets the instances and labels. This is useful when working with `sklearn` estimators, e.g.:</span>
|
||||
|
||||
|
|
@ -441,7 +464,7 @@
|
|||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">labels</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">Xp</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">Xp</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -451,7 +474,7 @@
|
|||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">X</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">X</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> An alias to self.instances</span>
|
||||
|
||||
|
|
@ -460,7 +483,7 @@
|
|||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">instances</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">y</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">y</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> An alias to self.labels</span>
|
||||
|
||||
|
|
@ -469,7 +492,7 @@
|
|||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">labels</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">p</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">p</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> An alias to self.prevalence()</span>
|
||||
|
||||
|
|
@ -480,7 +503,7 @@
|
|||
|
||||
<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="nf">stats</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">show</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">stats</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">show</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns (and eventually prints) a dictionary with some stats of this collection. E.g.,:</span>
|
||||
|
||||
|
|
@ -515,7 +538,7 @@
|
|||
|
||||
<div class="viewcode-block" id="LabelledCollection.kFCV">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.base.LabelledCollection.kFCV">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">kFCV</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nfolds</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">nrepeats</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">kFCV</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nfolds</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">nrepeats</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Generator of stratified folds to be used in k-fold cross validation.</span>
|
||||
|
||||
|
|
@ -529,13 +552,18 @@
|
|||
<span class="n">train</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">train_index</span><span class="p">)</span>
|
||||
<span class="n">test</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">test_index</span><span class="p">)</span>
|
||||
<span class="k">yield</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="nb">repr</span><span class="o">=</span><span class="sa">f</span><span class="s1">'<</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">), '</span>
|
||||
<span class="nb">repr</span><span class="o">+=</span><span class="sa">f</span><span class="s1">'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">>'</span>
|
||||
<span class="k">return</span> <span class="nb">repr</span></div>
|
||||
|
||||
|
||||
|
||||
<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="nc">Dataset</span><span class="p">:</span>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">Dataset</span><span class="p">:</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Abstraction of training and test :class:`LabelledCollection` objects.</span>
|
||||
|
||||
|
|
@ -545,7 +573,7 @@
|
|||
<span class="sd"> :param name: a string representing the name of the dataset</span>
|
||||
<span class="sd"> """</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">''</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">training</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">test</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">vocabulary</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s1">''</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">'incompatible labels in training and test collections'</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>
|
||||
|
|
@ -555,7 +583,7 @@
|
|||
<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="nf">SplitStratified</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">collection</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">train_size</span><span class="o">=</span><span class="mf">0.6</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">SplitStratified</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">collection</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">train_size</span><span class="o">=</span><span class="mf">0.6</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -568,7 +596,7 @@
|
|||
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">classes_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">classes_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> The classes according to which the training collection is labelled</span>
|
||||
|
||||
|
|
@ -577,7 +605,7 @@
|
|||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">classes_</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">n_classes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">n_classes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> The number of classes according to which the training collection is labelled</span>
|
||||
|
||||
|
|
@ -586,7 +614,7 @@
|
|||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">n_classes</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">binary</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">binary</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns True if the training collection is labelled according to two classes</span>
|
||||
|
||||
|
|
@ -597,7 +625,7 @@
|
|||
<div class="viewcode-block" id="Dataset.load">
|
||||
<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="nf">load</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">train_path</span><span class="p">,</span> <span class="n">test_path</span><span class="p">,</span> <span class="n">loader_func</span><span class="p">:</span> <span class="n">callable</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">loader_kwargs</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">load</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">train_path</span><span class="p">,</span> <span class="n">test_path</span><span class="p">,</span> <span class="n">loader_func</span><span class="p">:</span> <span class="nb">callable</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">loader_kwargs</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -619,7 +647,7 @@
|
|||
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">vocabulary_size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">vocabulary_size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> If the dataset is textual, and the vocabulary was indicated, returns the size of the vocabulary</span>
|
||||
|
||||
|
|
@ -628,7 +656,7 @@
|
|||
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">vocabulary</span><span class="p">)</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">train_test</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">train_test</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Alias to `self.training` and `self.test`</span>
|
||||
|
||||
|
|
@ -639,7 +667,7 @@
|
|||
|
||||
<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="nf">stats</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">show</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">stats</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">show</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns (and eventually prints) a dictionary with some stats of this dataset. E.g.,:</span>
|
||||
|
||||
|
|
@ -666,7 +694,7 @@
|
|||
<div class="viewcode-block" id="Dataset.kFCV">
|
||||
<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="nf">kFCV</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">nfolds</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">nrepeats</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">kFCV</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">nfolds</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">nrepeats</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -683,7 +711,7 @@
|
|||
|
||||
<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="nf">reduce</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_train</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">n_test</span><span class="o">=</span><span class="mi">100</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">reduce</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_train</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">n_test</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Reduce the number of instances in place for quick experiments. Preserves the prevalence of each set.</span>
|
||||
|
||||
|
|
@ -691,10 +719,21 @@
|
|||
<span class="sd"> :param n_test: number of test documents to keep (default 100)</span>
|
||||
<span class="sd"> :return: self</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">training</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="n">n_train</span><span class="p">,</span> <span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">prevalence</span><span class="p">())</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">test</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="n">n_test</span><span class="p">,</span> <span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">prevalence</span><span class="p">())</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">training</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span>
|
||||
<span class="n">n_train</span><span class="p">,</span>
|
||||
<span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">prevalence</span><span class="p">(),</span>
|
||||
<span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
|
||||
<span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">test</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span>
|
||||
<span class="n">n_test</span><span class="p">,</span>
|
||||
<span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">prevalence</span><span class="p">(),</span>
|
||||
<span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
|
||||
<span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="sa">f</span><span class="s1">'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">'</span></div>
|
||||
|
||||
</pre></div>
|
||||
|
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<h1>Source code for quapy.data.preprocessing</h1><div class="highlight"><pre>
|
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<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">scipy.sparse</span> <span class="kn">import</span> <span class="n">spmatrix</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.feature_extraction.text</span> <span class="kn">import</span> <span class="n">TfidfVectorizer</span><span class="p">,</span> <span class="n">CountVectorizer</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
|
||||
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
|
||||
<span></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">spmatrix</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.feature_extraction.text</span><span class="w"> </span><span class="kn">import</span> <span class="n">TfidfVectorizer</span><span class="p">,</span> <span class="n">CountVectorizer</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <span class="n">StandardScaler</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">tqdm</span><span class="w"> </span><span class="kn">import</span> <span class="n">tqdm</span>
|
||||
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">Dataset</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.util</span><span class="w"> </span><span class="kn">import</span> <span class="n">map_parallel</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">LabelledCollection</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="instance_transformation">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.instance_transformation">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">instance_transformation</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span><span class="n">Dataset</span><span class="p">,</span> <span class="n">transformer</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Transforms a :class:`quapy.data.base.Dataset` applying the `fit_transform` and `transform` functions</span>
|
||||
<span class="sd"> of a (sklearn's) transformer.</span>
|
||||
|
||||
<span class="sd"> :param dataset: a :class:`quapy.data.base.Dataset` where the instances of training and test collections are</span>
|
||||
<span class="sd"> lists of str</span>
|
||||
<span class="sd"> :param transformer: TransformerMixin implementing `fit_transform` and `transform` functions</span>
|
||||
<span class="sd"> :param inplace: whether or not to apply the transformation inplace (True), or to a new copy (False, default)</span>
|
||||
<span class="sd"> :return: a new :class:`quapy.data.base.Dataset` with transformed instances (if inplace=False) or a reference to the</span>
|
||||
<span class="sd"> current Dataset (if inplace=True) where the instances have been transformed</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">training_transformed</span> <span class="o">=</span> <span class="n">transformer</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="o">*</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">Xy</span><span class="p">)</span>
|
||||
<span class="n">test_transformed</span> <span class="o">=</span> <span class="n">transformer</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="n">orig_name</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">name</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">inplace</span><span class="p">:</span>
|
||||
<span class="n">dataset</span><span class="o">.</span><span class="n">training</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">training_transformed</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
|
||||
<span class="n">dataset</span><span class="o">.</span><span class="n">test</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">test_transformed</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">transformer</span><span class="p">,</span> <span class="s1">'vocabulary_'</span><span class="p">):</span>
|
||||
<span class="n">dataset</span><span class="o">.</span><span class="n">vocabulary</span> <span class="o">=</span> <span class="n">transformer</span><span class="o">.</span><span class="n">vocabulary_</span>
|
||||
<span class="k">return</span> <span class="n">dataset</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">training</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">training_transformed</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
|
||||
<span class="n">test</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">test_transformed</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
|
||||
<span class="n">vocab</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">transformer</span><span class="p">,</span> <span class="s1">'vocabulary_'</span><span class="p">):</span>
|
||||
<span class="n">vocab</span> <span class="o">=</span> <span class="n">transformer</span><span class="o">.</span><span class="n">vocabulary_</span>
|
||||
<span class="k">return</span> <span class="n">Dataset</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="n">test</span><span class="p">,</span> <span class="n">vocabulary</span><span class="o">=</span><span class="n">vocab</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">orig_name</span><span class="p">)</span></div>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.data.base</span> <span class="kn">import</span> <span class="n">Dataset</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.util</span> <span class="kn">import</span> <span class="n">map_parallel</span>
|
||||
<span class="kn">from</span> <span class="nn">.base</span> <span class="kn">import</span> <span class="n">LabelledCollection</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="text2tfidf">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.text2tfidf">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">text2tfidf</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span><span class="n">Dataset</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">sublinear_tf</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">text2tfidf</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span><span class="n">Dataset</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">sublinear_tf</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Transforms a :class:`quapy.data.base.Dataset` of textual instances into a :class:`quapy.data.base.Dataset` of</span>
|
||||
<span class="sd"> tfidf weighted sparse vectors</span>
|
||||
|
|
@ -103,24 +141,13 @@
|
|||
<span class="n">__check_type</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
|
||||
|
||||
<span class="n">vectorizer</span> <span class="o">=</span> <span class="n">TfidfVectorizer</span><span class="p">(</span><span class="n">min_df</span><span class="o">=</span><span class="n">min_df</span><span class="p">,</span> <span class="n">sublinear_tf</span><span class="o">=</span><span class="n">sublinear_tf</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="n">training_documents</span> <span class="o">=</span> <span class="n">vectorizer</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||||
<span class="n">test_documents</span> <span class="o">=</span> <span class="n">vectorizer</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">inplace</span><span class="p">:</span>
|
||||
<span class="n">dataset</span><span class="o">.</span><span class="n">training</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">training_documents</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
|
||||
<span class="n">dataset</span><span class="o">.</span><span class="n">test</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">test_documents</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
|
||||
<span class="n">dataset</span><span class="o">.</span><span class="n">vocabulary</span> <span class="o">=</span> <span class="n">vectorizer</span><span class="o">.</span><span class="n">vocabulary_</span>
|
||||
<span class="k">return</span> <span class="n">dataset</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">training</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">training_documents</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
|
||||
<span class="n">test</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">test_documents</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">Dataset</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="n">test</span><span class="p">,</span> <span class="n">vectorizer</span><span class="o">.</span><span class="n">vocabulary_</span><span class="p">)</span></div>
|
||||
<span class="k">return</span> <span class="n">instance_transformation</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">vectorizer</span><span class="p">,</span> <span class="n">inplace</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="reduce_columns">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.reduce_columns">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">reduce_columns</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">reduce_columns</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Reduces the dimensionality of the instances, represented as a `csr_matrix` (or any subtype of</span>
|
||||
<span class="sd"> `scipy.sparse.spmatrix`), of training and test documents by removing the columns of words which are not present</span>
|
||||
|
|
@ -138,7 +165,7 @@
|
|||
<span class="n">__check_type</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="n">spmatrix</span><span class="p">)</span>
|
||||
<span class="k">assert</span> <span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">instances</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s1">'unaligned vector spaces'</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">filter_by_occurrences</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">W</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">filter_by_occurrences</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">W</span><span class="p">):</span>
|
||||
<span class="n">column_prevalence</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">((</span><span class="n">X</span> <span class="o">></span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
|
||||
<span class="n">take_columns</span> <span class="o">=</span> <span class="n">column_prevalence</span> <span class="o">>=</span> <span class="n">min_df</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="n">take_columns</span><span class="p">]</span>
|
||||
|
|
@ -159,7 +186,7 @@
|
|||
|
||||
<div class="viewcode-block" id="standardize">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.standardize">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">standardize</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">standardize</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Standardizes the real-valued columns of a :class:`quapy.data.base.Dataset`.</span>
|
||||
<span class="sd"> Standardization, aka z-scoring, of a variable `X` comes down to subtracting the average and normalizing by the</span>
|
||||
|
|
@ -170,19 +197,24 @@
|
|||
<span class="sd"> :class:`quapy.data.base.Dataset` is to be returned</span>
|
||||
<span class="sd"> :return: an instance of :class:`quapy.data.base.Dataset`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">s</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">(</span><span class="n">copy</span><span class="o">=</span><span class="ow">not</span> <span class="n">inplace</span><span class="p">)</span>
|
||||
<span class="n">training</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||||
<span class="n">test</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
|
||||
<span class="n">s</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
|
||||
<span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">train_test</span>
|
||||
<span class="n">std_train_X</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">train</span><span class="o">.</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="n">std_test_X</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">inplace</span><span class="p">:</span>
|
||||
<span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">instances</span> <span class="o">=</span> <span class="n">std_train_X</span>
|
||||
<span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span> <span class="o">=</span> <span class="n">std_test_X</span>
|
||||
<span class="k">return</span> <span class="n">dataset</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">training</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">std_train_X</span><span class="p">,</span> <span class="n">train</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="n">train</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
|
||||
<span class="n">test</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">std_test_X</span><span class="p">,</span> <span class="n">test</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="n">test</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">Dataset</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="n">test</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">vocabulary</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">name</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="index">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.index">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">index</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">index</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Indexes the tokens of a textual :class:`quapy.data.base.Dataset` of string documents.</span>
|
||||
<span class="sd"> To index a document means to replace each different token by a unique numerical index.</span>
|
||||
|
|
@ -219,7 +251,7 @@
|
|||
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">__check_type</span><span class="p">(</span><span class="n">container</span><span class="p">,</span> <span class="n">container_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">element_type</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">__check_type</span><span class="p">(</span><span class="n">container</span><span class="p">,</span> <span class="n">container_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">element_type</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">container_type</span><span class="p">:</span>
|
||||
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">container</span><span class="p">,</span> <span class="n">container_type</span><span class="p">),</span> \
|
||||
<span class="sa">f</span><span class="s1">'unexpected type of container (expected </span><span class="si">{</span><span class="n">container_type</span><span class="si">}</span><span class="s1">, found </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">container</span><span class="p">)</span><span class="si">}</span><span class="s1">)'</span>
|
||||
|
|
@ -230,7 +262,7 @@
|
|||
|
||||
<div class="viewcode-block" id="IndexTransformer">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.IndexTransformer">[docs]</a>
|
||||
<span class="k">class</span> <span class="nc">IndexTransformer</span><span class="p">:</span>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">IndexTransformer</span><span class="p">:</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> This class implements a sklearn's-style transformer that indexes text as numerical ids for the tokens it</span>
|
||||
<span class="sd"> contains, and that would be generated by sklearn's</span>
|
||||
|
|
@ -240,14 +272,14 @@
|
|||
<span class="sd"> `CountVectorizer <https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html>`_</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">vect</span> <span class="o">=</span> <span class="n">CountVectorizer</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">unk</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span> <span class="c1"># a valid index is assigned after fit</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">pad</span> <span class="o">=</span> <span class="o">-</span><span class="mi">2</span> <span class="c1"># a valid index is assigned after fit</span>
|
||||
|
||||
<div class="viewcode-block" id="IndexTransformer.fit">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.IndexTransformer.fit">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Fits the transformer, i.e., decides on the vocabulary, given a list of strings.</span>
|
||||
|
||||
|
|
@ -264,7 +296,7 @@
|
|||
|
||||
<div class="viewcode-block" id="IndexTransformer.transform">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.IndexTransformer.transform">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Transforms the strings in `X` as lists of numerical ids</span>
|
||||
|
||||
|
|
@ -279,13 +311,13 @@
|
|||
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">documents</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">documents</span><span class="p">):</span>
|
||||
<span class="n">vocab</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
|
||||
<span class="k">return</span> <span class="p">[[</span><span class="n">vocab</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">word</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">unk</span><span class="p">)</span> <span class="k">for</span> <span class="n">word</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">analyzer</span><span class="p">(</span><span class="n">doc</span><span class="p">)]</span> <span class="k">for</span> <span class="n">doc</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">documents</span><span class="p">,</span> <span class="s1">'indexing'</span><span class="p">)]</span>
|
||||
|
||||
<div class="viewcode-block" id="IndexTransformer.fit_transform">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.IndexTransformer.fit_transform">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">fit_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">fit_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Fits the transform on `X` and transforms it.</span>
|
||||
|
||||
|
|
@ -298,7 +330,7 @@
|
|||
|
||||
<div class="viewcode-block" id="IndexTransformer.vocabulary_size">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.IndexTransformer.vocabulary_size">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">vocabulary_size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">vocabulary_size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Gets the length of the vocabulary according to which the document tokens have been indexed</span>
|
||||
|
||||
|
|
@ -309,7 +341,7 @@
|
|||
|
||||
<div class="viewcode-block" id="IndexTransformer.add_word">
|
||||
<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.IndexTransformer.add_word">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">add_word</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">word</span><span class="p">,</span> <span class="nb">id</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nogaps</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">add_word</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">word</span><span class="p">,</span> <span class="nb">id</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nogaps</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Adds a new token (regardless of whether it has been found in the text or not), with dedicated id.</span>
|
||||
<span class="sd"> Useful to define special tokens for codifying unknown words, or padding tokens.</span>
|
||||
|
|
|
|||
|
|
@ -1,22 +1,20 @@
|
|||
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en" data-content_root="../../../">
|
||||
<head>
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>quapy.data.reader — QuaPy: A Python-based open-source framework for quantification 0.1.8 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=92fd9be5" />
|
||||
<link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css?v=19f00094" />
|
||||
<title>quapy.data.reader — QuaPy: A Python-based open-source framework for quantification 0.2.1 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=b86133f3" />
|
||||
<link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css?v=9edc463e" />
|
||||
|
||||
|
||||
<!--[if lt IE 9]>
|
||||
<script src="../../../_static/js/html5shiv.min.js"></script>
|
||||
<![endif]-->
|
||||
|
||||
<script src="../../../_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="../../../_static/documentation_options.js?v=22607128"></script>
|
||||
<script src="../../../_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="../../../_static/sphinx_highlight.js?v=dc90522c"></script>
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<script src="../../../_static/jquery.js?v=5d32c60e"></script>
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<script src="../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="../../../_static/documentation_options.js?v=37f418d5"></script>
|
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<script src="../../../_static/doctools.js?v=fd6eb6e6"></script>
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<script src="../../../_static/sphinx_highlight.js?v=6ffebe34"></script>
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<script src="../../../_static/js/theme.js"></script>
|
||||
<link rel="index" title="Index" href="../../../genindex.html" />
|
||||
<link rel="search" title="Search" href="../../../search.html" />
|
||||
|
|
@ -42,7 +40,13 @@
|
|||
</div>
|
||||
</div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../modules.html">quapy</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../index.html">Quickstart</a></li>
|
||||
</ul>
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../manuals.html">Manuals</a></li>
|
||||
</ul>
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../quapy.html">API</a></li>
|
||||
</ul>
|
||||
|
||||
</div>
|
||||
|
|
@ -70,14 +74,14 @@
|
|||
<div itemprop="articleBody">
|
||||
|
||||
<h1>Source code for quapy.data.reader</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span 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>
|
||||
<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>
|
||||
|
||||
|
||||
<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="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">'utf-8'</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">class2int</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">from_text</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">'utf-8'</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">"""</span>
|
||||
<span class="sd"> Reads a labelled colletion of documents.</span>
|
||||
<span class="sd"> File fomart <0 or 1>\t<document>\n</span>
|
||||
|
|
@ -111,7 +115,7 @@
|
|||
|
||||
<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="nf">from_sparse</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">from_sparse</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Reads a labelled collection of real-valued instances expressed in sparse format</span>
|
||||
<span class="sd"> File format <-1 or 0 or 1>[\s col(int):val(float)]\n</span>
|
||||
|
|
@ -120,7 +124,7 @@
|
|||
<span class="sd"> :return: a `csr_matrix` containing the instances (rows), and a ndarray containing the labels</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">split_col_val</span><span class="p">(</span><span class="n">col_val</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">split_col_val</span><span class="p">(</span><span class="n">col_val</span><span class="p">):</span>
|
||||
<span class="n">col</span><span class="p">,</span> <span class="n">val</span> <span class="o">=</span> <span class="n">col_val</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">':'</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>
|
||||
|
|
@ -148,7 +152,7 @@
|
|||
|
||||
<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="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">'utf-8'</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">from_csv</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">'utf-8'</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Reads a csv file in which columns are separated by ','.</span>
|
||||
<span class="sd"> File format <label>,<feat1>,<feat2>,...,<featn>\n</span>
|
||||
|
|
@ -171,7 +175,7 @@
|
|||
|
||||
<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="nf">reindex_labels</span><span class="p">(</span><span class="n">y</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">reindex_labels</span><span class="p">(</span><span class="n">y</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -194,7 +198,7 @@
|
|||
|
||||
<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="nf">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Binarizes a categorical array-like collection of labels towards the positive class `pos_class`. E.g.,:</span>
|
||||
|
||||
|
|
|
|||
|
|
@ -1,23 +1,20 @@
|
|||
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en">
|
||||
<html class="writer-html5" lang="en" data-content_root="../../">
|
||||
<head>
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>quapy.error — QuaPy: A Python-based open-source framework for quantification 0.1.8 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="../../_static/pygments.css" />
|
||||
<link rel="stylesheet" type="text/css" href="../../_static/css/theme.css" />
|
||||
<title>quapy.error — QuaPy: A Python-based open-source framework for quantification 0.2.1 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=b86133f3" />
|
||||
<link rel="stylesheet" type="text/css" href="../../_static/css/theme.css?v=9edc463e" />
|
||||
|
||||
|
||||
<!--[if lt IE 9]>
|
||||
<script src="../../_static/js/html5shiv.min.js"></script>
|
||||
<![endif]-->
|
||||
|
||||
<script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js"></script>
|
||||
<script src="../../_static/jquery.js"></script>
|
||||
<script src="../../_static/underscore.js"></script>
|
||||
<script src="../../_static/_sphinx_javascript_frameworks_compat.js"></script>
|
||||
<script src="../../_static/doctools.js"></script>
|
||||
<script src="../../_static/sphinx_highlight.js"></script>
|
||||
<script src="../../_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<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/js/theme.js"></script>
|
||||
<link rel="index" title="Index" href="../../genindex.html" />
|
||||
<link rel="search" title="Search" href="../../search.html" />
|
||||
|
|
@ -43,7 +40,13 @@
|
|||
</div>
|
||||
</div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../modules.html">quapy</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../index.html">Quickstart</a></li>
|
||||
</ul>
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../manuals.html">Manuals</a></li>
|
||||
</ul>
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../quapy.html">API</a></li>
|
||||
</ul>
|
||||
|
||||
</div>
|
||||
|
|
@ -73,12 +76,14 @@
|
|||
<h1>Source code for quapy.error</h1><div class="highlight"><pre>
|
||||
<span></span><span class="sd">"""Implementation of error measures used for quantification"""</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>
|
||||
<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>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="from_name"><a class="viewcode-back" href="../../quapy.html#quapy.error.from_name">[docs]</a><span class="k">def</span> <span class="nf">from_name</span><span class="p">(</span><span class="n">err_name</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="from_name">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.from_name">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">from_name</span><span class="p">(</span><span class="n">err_name</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Gets an error function from its name. E.g., `from_name("mae")`</span>
|
||||
<span class="sd"> will return function :meth:`quapy.error.mae`</span>
|
||||
|
||||
|
|
@ -90,7 +95,10 @@
|
|||
<span class="k">return</span> <span class="n">callable_error</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="f1e"><a class="viewcode-back" href="../../quapy.html#quapy.error.f1e">[docs]</a><span class="k">def</span> <span class="nf">f1e</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="f1e">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.f1e">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">f1e</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""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>
|
||||
|
|
@ -105,7 +113,10 @@
|
|||
<span class="k">return</span> <span class="mf">1.</span> <span class="o">-</span> <span class="n">f1_score</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">average</span><span class="o">=</span><span class="s1">'macro'</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="acce"><a class="viewcode-back" href="../../quapy.html#quapy.error.acce">[docs]</a><span class="k">def</span> <span class="nf">acce</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="acce">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.acce">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">acce</span><span class="p">(</span><span class="n">y_true</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""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>
|
||||
|
|
@ -118,89 +129,207 @@
|
|||
<span class="k">return</span> <span class="mf">1.</span> <span class="o">-</span> <span class="p">(</span><span class="n">y_true</span> <span class="o">==</span> <span class="n">y_pred</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="mae"><a class="viewcode-back" href="../../quapy.html#quapy.error.mae">[docs]</a><span class="k">def</span> <span class="nf">mae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="mae">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.mae">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">mae</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Computes the mean absolute error (see :meth:`quapy.error.ae`) across the sample pairs.</span>
|
||||
|
||||
<span class="sd"> :param prevs: array-like of shape `(n_samples, n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_true: array-like of shape `(n_samples, n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_samples, n_classes,)` with the predicted</span>
|
||||
<span class="sd"> prevalence values</span>
|
||||
<span class="sd"> :return: mean absolute error</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="n">ae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>
|
||||
<span class="k">return</span> <span class="n">ae</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ae"><a class="viewcode-back" href="../../quapy.html#quapy.error.ae">[docs]</a><span class="k">def</span> <span class="nf">ae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="ae">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.ae">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">ae</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""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>
|
||||
<span class="sd"> where :math:`\\mathcal{Y}` are the classes of interest.</span>
|
||||
|
||||
<span class="sd"> :param prevs: array-like of shape `(n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_true: array-like of shape `(n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` with the predicted prevalence values</span>
|
||||
<span class="sd"> :return: absolute error</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">assert</span> <span class="n">prevs</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">prevs_hat</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'wrong shape </span><span class="si">{</span><span class="n">prevs</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s1"> vs. </span><span class="si">{</span><span class="n">prevs_hat</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s1">'</span>
|
||||
<span class="k">return</span> <span class="nb">abs</span><span class="p">(</span><span class="n">prevs_hat</span> <span class="o">-</span> <span class="n">prevs</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></div>
|
||||
<span class="n">prevs_true</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">)</span>
|
||||
<span class="n">prevs_hat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevs_hat</span><span class="p">)</span>
|
||||
<span class="k">assert</span> <span class="n">prevs_true</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">prevs_hat</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'wrong shape </span><span class="si">{</span><span class="n">prevs_true</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s1"> vs. </span><span class="si">{</span><span class="n">prevs_hat</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s1">'</span>
|
||||
<span class="k">return</span> <span class="nb">abs</span><span class="p">(</span><span class="n">prevs_hat</span> <span class="o">-</span> <span class="n">prevs_true</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="nae"><a class="viewcode-back" href="../../quapy.html#quapy.error.nae">[docs]</a><span class="k">def</span> <span class="nf">nae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="nae">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.nae">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">nae</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""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>
|
||||
<span class="sd"> where :math:`z_{AE}=\\frac{2(1-\\min_{y\\in \\mathcal{Y}} p(y))}{|\\mathcal{Y}|}`, and :math:`\\mathcal{Y}`</span>
|
||||
<span class="sd"> are the classes of interest.</span>
|
||||
|
||||
<span class="sd"> :param prevs: array-like of shape `(n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_true: array-like of shape `(n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` with the predicted prevalence values</span>
|
||||
<span class="sd"> :return: normalized absolute error</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">assert</span> <span class="n">prevs</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">prevs_hat</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'wrong shape </span><span class="si">{</span><span class="n">prevs</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s1"> vs. </span><span class="si">{</span><span class="n">prevs_hat</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s1">'</span>
|
||||
<span class="k">return</span> <span class="nb">abs</span><span class="p">(</span><span class="n">prevs_hat</span> <span class="o">-</span> <span class="n">prevs</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">prevs</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)))</span></div>
|
||||
<span class="n">prevs_true</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">)</span>
|
||||
<span class="n">prevs_hat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevs_hat</span><span class="p">)</span>
|
||||
<span class="k">assert</span> <span class="n">prevs_true</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">prevs_hat</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'wrong shape </span><span class="si">{</span><span class="n">prevs_true</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s1"> vs. </span><span class="si">{</span><span class="n">prevs_hat</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s1">'</span>
|
||||
<span class="k">return</span> <span class="nb">abs</span><span class="p">(</span><span class="n">prevs_hat</span> <span class="o">-</span> <span class="n">prevs_true</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">prevs_true</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)))</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="mnae"><a class="viewcode-back" href="../../quapy.html#quapy.error.mnae">[docs]</a><span class="k">def</span> <span class="nf">mnae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="mnae">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.mnae">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">mnae</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Computes the mean normalized absolute error (see :meth:`quapy.error.nae`) across the sample pairs.</span>
|
||||
|
||||
<span class="sd"> :param prevs: array-like of shape `(n_samples, n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_true: array-like of shape `(n_samples, n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_samples, n_classes,)` with the predicted</span>
|
||||
<span class="sd"> prevalence values</span>
|
||||
<span class="sd"> :return: mean normalized absolute error</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="n">nae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>
|
||||
<span class="k">return</span> <span class="n">nae</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="mse"><a class="viewcode-back" href="../../quapy.html#quapy.error.mse">[docs]</a><span class="k">def</span> <span class="nf">mse</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="mse">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.mse">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">mse</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Computes the mean squared error (see :meth:`quapy.error.se`) across the sample pairs.</span>
|
||||
|
||||
<span class="sd"> :param prevs: array-like of shape `(n_samples, n_classes,)` with the</span>
|
||||
<span class="sd"> :param prevs_true: array-like of shape `(n_samples, n_classes,)` with the</span>
|
||||
<span class="sd"> true prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_samples, n_classes,)` with the</span>
|
||||
<span class="sd"> predicted prevalence values</span>
|
||||
<span class="sd"> :return: mean squared error</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="n">se</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>
|
||||
<span class="k">return</span> <span class="n">se</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="se"><a class="viewcode-back" href="../../quapy.html#quapy.error.se">[docs]</a><span class="k">def</span> <span class="nf">se</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="se">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.se">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">se</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""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>
|
||||
<span class="sd"> where</span>
|
||||
<span class="sd"> :math:`\\mathcal{Y}` are the classes of interest.</span>
|
||||
|
||||
<span class="sd"> :param prevs: array-like of shape `(n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_true: array-like of shape `(n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` with the predicted prevalence values</span>
|
||||
<span class="sd"> :return: absolute error</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="p">((</span><span class="n">prevs_hat</span> <span class="o">-</span> <span class="n">prevs</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></div>
|
||||
<span class="n">prevs_true</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">)</span>
|
||||
<span class="n">prevs_hat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevs_hat</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="p">((</span><span class="n">prevs_hat</span> <span class="o">-</span> <span class="n">prevs_true</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="mkld"><a class="viewcode-back" href="../../quapy.html#quapy.error.mkld">[docs]</a><span class="k">def</span> <span class="nf">mkld</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="sre">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.sre">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">sre</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">prevs_train</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">0.</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Computes the squared ratio error between two prevalence vectors.</span>
|
||||
<span class="sd"> The squared ratio error between prevalence vectors :math:`p` and</span>
|
||||
<span class="sd"> :math:`\\hat{p}` with training prevalence :math:`p^{tr}` is:</span>
|
||||
<span class="sd"> :math:`SRE(p,\\hat{p},p^{tr})=\\frac{1}{|\\mathcal{Y}|}\\sum_{i \\in \\mathcal{Y}}(w_i-\\hat{w}_i)^2`,</span>
|
||||
<span class="sd"> where :math:`w_i=\\frac{p_i}{p^{tr}_i}`.</span>
|
||||
|
||||
<span class="sd"> :param prevs_true: array-like with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like with the predicted prevalence values</span>
|
||||
<span class="sd"> :param prevs_train: array-like with the training prevalence values, or a single</span>
|
||||
<span class="sd"> prevalence vector when all comparisons refer to the same training set</span>
|
||||
<span class="sd"> :param eps: smoothing factor for the prevalence values (default 0, i.e., no smoothing)</span>
|
||||
<span class="sd"> :return: squared ratio error</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">prevs_true</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">)</span>
|
||||
<span class="n">prevs_hat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevs_hat</span><span class="p">)</span>
|
||||
<span class="n">prevs_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevs_train</span><span class="p">)</span>
|
||||
<span class="k">assert</span> <span class="n">prevs_true</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">prevs_hat</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'wrong shape </span><span class="si">{</span><span class="n">prevs_true</span><span class="o">.</span><span class="n">shape</span><span class="si">=}</span><span class="s1"> vs </span><span class="si">{</span><span class="n">prevs_hat</span><span class="o">.</span><span class="n">shape</span><span class="si">=}</span><span class="s1">'</span>
|
||||
<span class="k">assert</span> <span class="n">prevs_true</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">prevs_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="s1">'wrong shape for training prevalence'</span>
|
||||
<span class="k">if</span> <span class="n">prevs_true</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">2</span> <span class="ow">and</span> <span class="n">prevs_train</span><span class="o">.</span><span class="n">ndim</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">prevs_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">prevs_train</span><span class="p">,</span> <span class="n">reps</span><span class="o">=</span><span class="p">(</span><span class="n">prevs_true</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">))</span>
|
||||
<span class="k">if</span> <span class="n">eps</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">prevs_true</span> <span class="o">=</span> <span class="n">smooth</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span>
|
||||
<span class="n">prevs_hat</span> <span class="o">=</span> <span class="n">smooth</span><span class="p">(</span><span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span>
|
||||
<span class="n">prevs_train</span> <span class="o">=</span> <span class="n">smooth</span><span class="p">(</span><span class="n">prevs_train</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span>
|
||||
|
||||
<span class="n">n_classes</span> <span class="o">=</span> <span class="n">prevs_true</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="n">w</span> <span class="o">=</span> <span class="n">prevs_true</span> <span class="o">/</span> <span class="n">prevs_train</span>
|
||||
<span class="n">w_hat</span> <span class="o">=</span> <span class="n">prevs_hat</span> <span class="o">/</span> <span class="n">prevs_train</span>
|
||||
<span class="k">return</span> <span class="p">(</span><span class="mf">1.</span> <span class="o">/</span> <span class="n">n_classes</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">w</span> <span class="o">-</span> <span class="n">w_hat</span><span class="p">)</span> <span class="o">**</span> <span class="mf">2.</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="msre">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.msre">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">msre</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">prevs_train</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="mf">0.</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Computes the mean squared ratio error (see :meth:`quapy.error.sre`) across the sample pairs.</span>
|
||||
|
||||
<span class="sd"> :param prevs_true: array-like of shape `(n_samples, n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape equal to prevs_true with the predicted prevalence values</span>
|
||||
<span class="sd"> :param prevs_train: array-like with the training prevalence values</span>
|
||||
<span class="sd"> :param eps: smoothing factor (default 0, i.e., no smoothing)</span>
|
||||
<span class="sd"> :return: mean squared ratio error</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">sre</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">prevs_train</span><span class="p">,</span> <span class="n">eps</span><span class="p">))</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="aitchisondist">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.aitchisondist">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">aitchisondist</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Computes the Aitchison distance between two prevalence vectors.</span>
|
||||
<span class="sd"> The Aitchison distance between prevalence vectors :math:`p` and</span>
|
||||
<span class="sd"> :math:`\\hat{p}` is computed as</span>
|
||||
<span class="sd"> :math:`d_A(p,\\hat{p})=\\|\\mathrm{clr}(p)-\\mathrm{clr}(\\hat{p})\\|_2`,</span>
|
||||
<span class="sd"> where :math:`\\mathrm{clr}(p)_i=\\log p_i-\\frac{1}{|\\mathcal{Y}|}</span>
|
||||
<span class="sd"> \\sum_{j \\in \\mathcal{Y}} \\log p_j`.</span>
|
||||
|
||||
<span class="sd"> :param prevs_true: array-like with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like with the predicted prevalence values</span>
|
||||
<span class="sd"> :return: Aitchison distance</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.functional</span><span class="w"> </span><span class="kn">import</span> <span class="n">CLRtransformation</span>
|
||||
|
||||
<span class="n">clr</span> <span class="o">=</span> <span class="n">CLRtransformation</span><span class="p">()</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">norm</span><span class="p">(</span><span class="n">clr</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">)</span> <span class="o">-</span> <span class="n">clr</span><span class="p">(</span><span class="n">prevs_hat</span><span class="p">),</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="maitchisondist">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.maitchisondist">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">maitchisondist</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Computes the mean Aitchison distance (see :meth:`quapy.error.aitchisondist`)</span>
|
||||
<span class="sd"> across the sample pairs, i.e.,</span>
|
||||
<span class="sd"> :math:`\\mathrm{mAitchisonDist}=\\frac{1}{n}\\sum_{i=1}^n</span>
|
||||
<span class="sd"> d_A(p_i,\\hat{p}_i)`.</span>
|
||||
|
||||
<span class="sd"> :param prevs_true: array-like with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like with the predicted prevalence values</span>
|
||||
<span class="sd"> :return: mean Aitchison distance</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">aitchisondist</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">))</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="mkld">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.mkld">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">mkld</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""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>
|
||||
|
||||
<span class="sd"> :param prevs: array-like of shape `(n_samples, n_classes,)` with the true</span>
|
||||
<span class="sd"> :param prevs_true: array-like of shape `(n_samples, n_classes,)` with the true</span>
|
||||
<span class="sd"> prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_samples, n_classes,)` with the predicted</span>
|
||||
<span class="sd"> prevalence values</span>
|
||||
|
|
@ -210,10 +339,13 @@
|
|||
<span class="sd"> (which has thus to be set beforehand).</span>
|
||||
<span class="sd"> :return: mean Kullback-Leibler distribution</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="n">kld</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>
|
||||
<span class="k">return</span> <span class="n">kld</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="kld"><a class="viewcode-back" href="../../quapy.html#quapy.error.kld">[docs]</a><span class="k">def</span> <span class="nf">kld</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="kld">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.kld">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">kld</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""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>
|
||||
|
|
@ -222,7 +354,7 @@
|
|||
<span class="sd"> where :math:`\\mathcal{Y}` are the classes of interest.</span>
|
||||
<span class="sd"> The distributions are smoothed using the `eps` factor (see :meth:`quapy.error.smooth`).</span>
|
||||
|
||||
<span class="sd"> :param prevs: array-like of shape `(n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_true: array-like of shape `(n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` with the predicted prevalence values</span>
|
||||
<span class="sd"> :param eps: smoothing factor. KLD is not defined in cases in which the distributions contain</span>
|
||||
<span class="sd"> zeros; `eps` is typically set to be :math:`\\frac{1}{2T}`, with :math:`T` the sample size.</span>
|
||||
|
|
@ -231,17 +363,20 @@
|
|||
<span class="sd"> :return: Kullback-Leibler divergence between the two distributions</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">eps</span> <span class="o">=</span> <span class="n">__check_eps</span><span class="p">(</span><span class="n">eps</span><span class="p">)</span>
|
||||
<span class="n">smooth_prevs</span> <span class="o">=</span> <span class="n">prevs</span> <span class="o">+</span> <span class="n">eps</span>
|
||||
<span class="n">smooth_prevs_hat</span> <span class="o">=</span> <span class="n">prevs_hat</span> <span class="o">+</span> <span class="n">eps</span>
|
||||
<span class="n">smooth_prevs</span> <span class="o">=</span> <span class="n">smooth</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span>
|
||||
<span class="n">smooth_prevs_hat</span> <span class="o">=</span> <span class="n">smooth</span><span class="p">(</span><span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="p">(</span><span class="n">smooth_prevs</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">smooth_prevs</span><span class="o">/</span><span class="n">smooth_prevs_hat</span><span class="p">))</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="mnkld"><a class="viewcode-back" href="../../quapy.html#quapy.error.mnkld">[docs]</a><span class="k">def</span> <span class="nf">mnkld</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="mnkld">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.mnkld">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">mnkld</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""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>
|
||||
|
||||
<span class="sd"> :param prevs: array-like of shape `(n_samples, n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_true: array-like of shape `(n_samples, n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_samples, n_classes,)` with the predicted</span>
|
||||
<span class="sd"> prevalence values</span>
|
||||
<span class="sd"> :param eps: smoothing factor. NKLD is not defined in cases in which the distributions contain</span>
|
||||
|
|
@ -250,10 +385,13 @@
|
|||
<span class="sd"> (which has thus to be set beforehand).</span>
|
||||
<span class="sd"> :return: mean Normalized Kullback-Leibler distribution</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="n">nkld</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>
|
||||
<span class="k">return</span> <span class="n">nkld</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="nkld"><a class="viewcode-back" href="../../quapy.html#quapy.error.nkld">[docs]</a><span class="k">def</span> <span class="nf">nkld</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="nkld">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.nkld">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">nkld</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""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>
|
||||
|
|
@ -262,7 +400,7 @@
|
|||
<span class="sd"> :math:`\\mathcal{Y}` are the classes of interest.</span>
|
||||
<span class="sd"> The distributions are smoothed using the `eps` factor (see :meth:`quapy.error.smooth`).</span>
|
||||
|
||||
<span class="sd"> :param prevs: array-like of shape `(n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_true: array-like of shape `(n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` with the predicted prevalence values</span>
|
||||
<span class="sd"> :param eps: smoothing factor. NKLD is not defined in cases in which the distributions</span>
|
||||
<span class="sd"> contain zeros; `eps` is typically set to be :math:`\\frac{1}{2T}`, with :math:`T` the sample</span>
|
||||
|
|
@ -270,16 +408,19 @@
|
|||
<span class="sd"> `SAMPLE_SIZE` (which has thus to be set beforehand).</span>
|
||||
<span class="sd"> :return: Normalized Kullback-Leibler divergence between the two distributions</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">ekld</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">kld</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">))</span>
|
||||
<span class="n">ekld</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">kld</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">))</span>
|
||||
<span class="k">return</span> <span class="mf">2.</span> <span class="o">*</span> <span class="n">ekld</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">ekld</span><span class="p">)</span> <span class="o">-</span> <span class="mf">1.</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="mrae"><a class="viewcode-back" href="../../quapy.html#quapy.error.mrae">[docs]</a><span class="k">def</span> <span class="nf">mrae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="mrae">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.mrae">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">mrae</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""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>
|
||||
|
||||
<span class="sd"> :param prevs: array-like of shape `(n_samples, n_classes,)` with the true</span>
|
||||
<span class="sd"> :param prevs_true: array-like of shape `(n_samples, n_classes,)` with the true</span>
|
||||
<span class="sd"> prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_samples, n_classes,)` with the predicted</span>
|
||||
<span class="sd"> prevalence values</span>
|
||||
|
|
@ -289,10 +430,13 @@
|
|||
<span class="sd"> the environment variable `SAMPLE_SIZE` (which has thus to be set beforehand).</span>
|
||||
<span class="sd"> :return: mean relative absolute error</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="n">rae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>
|
||||
<span class="k">return</span> <span class="n">rae</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="rae"><a class="viewcode-back" href="../../quapy.html#quapy.error.rae">[docs]</a><span class="k">def</span> <span class="nf">rae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="rae">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.rae">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">rae</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""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>
|
||||
|
|
@ -301,7 +445,7 @@
|
|||
<span class="sd"> where :math:`\\mathcal{Y}` are the classes of interest.</span>
|
||||
<span class="sd"> The distributions are smoothed using the `eps` factor (see :meth:`quapy.error.smooth`).</span>
|
||||
|
||||
<span class="sd"> :param prevs: array-like of shape `(n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_true: array-like of shape `(n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` with the predicted prevalence values</span>
|
||||
<span class="sd"> :param eps: smoothing factor. `rae` is not defined in cases in which the true distribution</span>
|
||||
<span class="sd"> contains zeros; `eps` is typically set to be :math:`\\frac{1}{2T}`, with :math:`T` the</span>
|
||||
|
|
@ -310,12 +454,15 @@
|
|||
<span class="sd"> :return: relative absolute error</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">eps</span> <span class="o">=</span> <span class="n">__check_eps</span><span class="p">(</span><span class="n">eps</span><span class="p">)</span>
|
||||
<span class="n">prevs</span> <span class="o">=</span> <span class="n">smooth</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span>
|
||||
<span class="n">prevs_true</span> <span class="o">=</span> <span class="n">smooth</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span>
|
||||
<span class="n">prevs_hat</span> <span class="o">=</span> <span class="n">smooth</span><span class="p">(</span><span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">prevs</span> <span class="o">-</span> <span class="n">prevs_hat</span><span class="p">)</span> <span class="o">/</span> <span class="n">prevs</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></div>
|
||||
<span class="k">return</span> <span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">prevs_true</span> <span class="o">-</span> <span class="n">prevs_hat</span><span class="p">)</span> <span class="o">/</span> <span class="n">prevs_true</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="nrae"><a class="viewcode-back" href="../../quapy.html#quapy.error.nrae">[docs]</a><span class="k">def</span> <span class="nf">nrae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="nrae">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.nrae">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">nrae</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""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>
|
||||
|
|
@ -325,7 +472,7 @@
|
|||
<span class="sd"> and :math:`\\mathcal{Y}` are the classes of interest.</span>
|
||||
<span class="sd"> The distributions are smoothed using the `eps` factor (see :meth:`quapy.error.smooth`).</span>
|
||||
|
||||
<span class="sd"> :param prevs: array-like of shape `(n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_true: array-like of shape `(n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` with the predicted prevalence values</span>
|
||||
<span class="sd"> :param eps: smoothing factor. `nrae` is not defined in cases in which the true distribution</span>
|
||||
<span class="sd"> contains zeros; `eps` is typically set to be :math:`\\frac{1}{2T}`, with :math:`T` the</span>
|
||||
|
|
@ -334,18 +481,21 @@
|
|||
<span class="sd"> :return: normalized relative absolute error</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">eps</span> <span class="o">=</span> <span class="n">__check_eps</span><span class="p">(</span><span class="n">eps</span><span class="p">)</span>
|
||||
<span class="n">prevs</span> <span class="o">=</span> <span class="n">smooth</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span>
|
||||
<span class="n">prevs_true</span> <span class="o">=</span> <span class="n">smooth</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span>
|
||||
<span class="n">prevs_hat</span> <span class="o">=</span> <span class="n">smooth</span><span class="p">(</span><span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span>
|
||||
<span class="n">min_p</span> <span class="o">=</span> <span class="n">prevs</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">prevs</span> <span class="o">-</span> <span class="n">prevs_hat</span><span class="p">)</span> <span class="o">/</span> <span class="n">prevs</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">prevs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="mi">1</span><span class="o">+</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">min_p</span><span class="p">)</span><span class="o">/</span><span class="n">min_p</span><span class="p">)</span></div>
|
||||
<span class="n">min_p</span> <span class="o">=</span> <span class="n">prevs_true</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">prevs_true</span> <span class="o">-</span> <span class="n">prevs_hat</span><span class="p">)</span> <span class="o">/</span> <span class="n">prevs_true</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">prevs_true</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">min_p</span><span class="p">)</span> <span class="o">/</span> <span class="n">min_p</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="mnrae"><a class="viewcode-back" href="../../quapy.html#quapy.error.mnrae">[docs]</a><span class="k">def</span> <span class="nf">mnrae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="mnrae">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.mnrae">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">mnrae</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""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>
|
||||
|
||||
<span class="sd"> :param prevs: array-like of shape `(n_samples, n_classes,)` with the true</span>
|
||||
<span class="sd"> :param prevs_true: array-like of shape `(n_samples, n_classes,)` with the true</span>
|
||||
<span class="sd"> prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_samples, n_classes,)` with the predicted</span>
|
||||
<span class="sd"> prevalence values</span>
|
||||
|
|
@ -355,10 +505,84 @@
|
|||
<span class="sd"> the environment variable `SAMPLE_SIZE` (which has thus to be set beforehand).</span>
|
||||
<span class="sd"> :return: mean normalized relative absolute error</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="n">nrae</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>
|
||||
<span class="k">return</span> <span class="n">nrae</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">eps</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="smooth"><a class="viewcode-back" href="../../quapy.html#quapy.error.smooth">[docs]</a><span class="k">def</span> <span class="nf">smooth</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">eps</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="nmd">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.nmd">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">nmd</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Computes the Normalized Match Distance; which is the Normalized Distance multiplied by the factor</span>
|
||||
<span class="sd"> `1/(n-1)` to guarantee the measure ranges between 0 (best prediction) and 1 (worst prediction).</span>
|
||||
|
||||
<span class="sd"> :param prevs_true: array-like of shape `(n_classes,)` or `(n_instances, n_classes)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` or `(n_instances, n_classes)` with the predicted prevalence values</span>
|
||||
<span class="sd"> :return: float in [0,1]</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">prevs_true</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">)</span>
|
||||
<span class="n">prevs_hat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevs_hat</span><span class="p">)</span>
|
||||
<span class="n">n</span> <span class="o">=</span> <span class="n">prevs_true</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="k">return</span> <span class="p">(</span><span class="mf">1.</span><span class="o">/</span><span class="p">(</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">match_distance</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">))</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="bias_binary">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.bias_binary">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">bias_binary</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Computes the (positive) bias in a binary problem. The bias is simply the difference between the</span>
|
||||
<span class="sd"> predicted positive value and the true positive value, so that a positive such value indicates the</span>
|
||||
<span class="sd"> prediction has positive bias (i.e., it tends to overestimate) the true value, and negative otherwise.</span>
|
||||
<span class="sd"> :math:`bias(p,\\hat{p})=\\hat{p}_1-p_1`,</span>
|
||||
<span class="sd"> :param prevs_true: array-like of shape `(n_samples, n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_samples, n_classes,)` with the predicted</span>
|
||||
<span class="sd"> prevalence values</span>
|
||||
<span class="sd"> :return: binary bias</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">prevs_true</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">)</span>
|
||||
<span class="n">prevs_hat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevs_hat</span><span class="p">)</span>
|
||||
<span class="k">assert</span> <span class="n">prevs_true</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span> <span class="ow">and</span> <span class="n">prevs_true</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'bias_binary can only be applied to binary problems'</span>
|
||||
<span class="k">return</span> <span class="n">prevs_hat</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">prevs_true</span><span class="p">[</span><span class="o">...</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="mean_bias_binary">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.mean_bias_binary">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">mean_bias_binary</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Computes the mean of the (positive) bias in a binary problem.</span>
|
||||
<span class="sd"> :param prevs_true: array-like of shape `(n_classes,)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` with the predicted prevalence values</span>
|
||||
<span class="sd"> :return: mean binary bias</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">bias_binary</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">))</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="md">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.md">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">md</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">prevs_hat</span><span class="p">,</span> <span class="n">ERROR_TOL</span><span class="o">=</span><span class="mf">1E-3</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Computes the Match Distance, under the assumption that the cost in mistaking class i with class i+1 is 1 in</span>
|
||||
<span class="sd"> all cases.</span>
|
||||
|
||||
<span class="sd"> :param prevs_true: array-like of shape `(n_classes,)` or `(n_instances, n_classes)` with the true prevalence values</span>
|
||||
<span class="sd"> :param prevs_hat: array-like of shape `(n_classes,)` or `(n_instances, n_classes)` with the predicted prevalence values</span>
|
||||
<span class="sd"> :return: float</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">P</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">prevs_true</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">P_hat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">prevs_hat</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="k">assert</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">isclose</span><span class="p">(</span><span class="n">P_hat</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="mf">1.0</span><span class="p">,</span> <span class="n">rtol</span><span class="o">=</span><span class="n">ERROR_TOL</span><span class="p">)),</span> \
|
||||
<span class="s1">'arg error in match_distance: the array does not represent a valid distribution'</span>
|
||||
<span class="n">distances</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">P</span><span class="o">-</span><span class="n">P_hat</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">distances</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="smooth">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.error.smooth">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">smooth</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">eps</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">""" 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>
|
||||
|
|
@ -367,11 +591,13 @@
|
|||
<span class="sd"> :param eps: smoothing factor</span>
|
||||
<span class="sd"> :return: array-like of shape `(n_classes,)` with the smoothed distribution</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">prevs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevs</span><span class="p">)</span>
|
||||
<span class="n">n_classes</span> <span class="o">=</span> <span class="n">prevs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="k">return</span> <span class="p">(</span><span class="n">prevs</span> <span class="o">+</span> <span class="n">eps</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">eps</span> <span class="o">*</span> <span class="n">n_classes</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">__check_eps</span><span class="p">(</span><span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">__check_eps</span><span class="p">(</span><span class="n">eps</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">eps</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="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">'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>
|
||||
|
|
@ -381,8 +607,8 @@
|
|||
|
||||
|
||||
<span class="n">CLASSIFICATION_ERROR</span> <span class="o">=</span> <span class="p">{</span><span class="n">f1e</span><span class="p">,</span> <span class="n">acce</span><span class="p">}</span>
|
||||
<span class="n">QUANTIFICATION_ERROR</span> <span class="o">=</span> <span class="p">{</span><span class="n">mae</span><span class="p">,</span> <span class="n">mnae</span><span class="p">,</span> <span class="n">mrae</span><span class="p">,</span> <span class="n">mnrae</span><span class="p">,</span> <span class="n">mse</span><span class="p">,</span> <span class="n">mkld</span><span class="p">,</span> <span class="n">mnkld</span><span class="p">}</span>
|
||||
<span class="n">QUANTIFICATION_ERROR_SINGLE</span> <span class="o">=</span> <span class="p">{</span><span class="n">ae</span><span class="p">,</span> <span class="n">nae</span><span class="p">,</span> <span class="n">rae</span><span class="p">,</span> <span class="n">nrae</span><span class="p">,</span> <span class="n">se</span><span class="p">,</span> <span class="n">kld</span><span class="p">,</span> <span class="n">nkld</span><span class="p">}</span>
|
||||
<span class="n">QUANTIFICATION_ERROR</span> <span class="o">=</span> <span class="p">{</span><span class="n">mae</span><span class="p">,</span> <span class="n">mnae</span><span class="p">,</span> <span class="n">mrae</span><span class="p">,</span> <span class="n">mnrae</span><span class="p">,</span> <span class="n">mse</span><span class="p">,</span> <span class="n">mkld</span><span class="p">,</span> <span class="n">mnkld</span><span class="p">,</span> <span class="n">msre</span><span class="p">,</span> <span class="n">maitchisondist</span><span class="p">}</span>
|
||||
<span class="n">QUANTIFICATION_ERROR_SINGLE</span> <span class="o">=</span> <span class="p">{</span><span class="n">ae</span><span class="p">,</span> <span class="n">nae</span><span class="p">,</span> <span class="n">rae</span><span class="p">,</span> <span class="n">nrae</span><span class="p">,</span> <span class="n">se</span><span class="p">,</span> <span class="n">kld</span><span class="p">,</span> <span class="n">nkld</span><span class="p">,</span> <span class="n">sre</span><span class="p">,</span> <span class="n">aitchisondist</span><span class="p">}</span>
|
||||
<span class="n">QUANTIFICATION_ERROR_SMOOTH</span> <span class="o">=</span> <span class="p">{</span><span class="n">kld</span><span class="p">,</span> <span class="n">nkld</span><span class="p">,</span> <span class="n">rae</span><span class="p">,</span> <span class="n">nrae</span><span class="p">,</span> <span class="n">mkld</span><span class="p">,</span> <span class="n">mnkld</span><span class="p">,</span> <span class="n">mrae</span><span class="p">}</span>
|
||||
<span class="n">CLASSIFICATION_ERROR_NAMES</span> <span class="o">=</span> <span class="p">{</span><span class="n">func</span><span class="o">.</span><span class="vm">__name__</span> <span class="k">for</span> <span class="n">func</span> <span class="ow">in</span> <span class="n">CLASSIFICATION_ERROR</span><span class="p">}</span>
|
||||
<span class="n">QUANTIFICATION_ERROR_NAMES</span> <span class="o">=</span> <span class="p">{</span><span class="n">func</span><span class="o">.</span><span class="vm">__name__</span> <span class="k">for</span> <span class="n">func</span> <span class="ow">in</span> <span class="n">QUANTIFICATION_ERROR</span><span class="p">}</span>
|
||||
|
|
@ -394,6 +620,9 @@
|
|||
<span class="n">f1_error</span> <span class="o">=</span> <span class="n">f1e</span>
|
||||
<span class="n">acc_error</span> <span class="o">=</span> <span class="n">acce</span>
|
||||
<span class="n">mean_absolute_error</span> <span class="o">=</span> <span class="n">mae</span>
|
||||
<span class="n">squared_ratio_error</span> <span class="o">=</span> <span class="n">sre</span>
|
||||
<span class="n">dist_aitchison</span> <span class="o">=</span> <span class="n">aitchisondist</span>
|
||||
<span class="n">mean_dist_aitchison</span> <span class="o">=</span> <span class="n">maitchisondist</span>
|
||||
<span class="n">absolute_error</span> <span class="o">=</span> <span class="n">ae</span>
|
||||
<span class="n">mean_relative_absolute_error</span> <span class="o">=</span> <span class="n">mrae</span>
|
||||
<span class="n">relative_absolute_error</span> <span class="o">=</span> <span class="n">rae</span>
|
||||
|
|
@ -401,6 +630,8 @@
|
|||
<span class="n">normalized_relative_absolute_error</span> <span class="o">=</span> <span class="n">nrae</span>
|
||||
<span class="n">mean_normalized_absolute_error</span> <span class="o">=</span> <span class="n">mnae</span>
|
||||
<span class="n">mean_normalized_relative_absolute_error</span> <span class="o">=</span> <span class="n">mnrae</span>
|
||||
<span class="n">normalized_match_distance</span> <span class="o">=</span> <span class="n">nmd</span>
|
||||
<span class="n">match_distance</span> <span class="o">=</span> <span class="n">md</span>
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
|
|
|
|||
|
|
@ -1,23 +1,20 @@
|
|||
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en">
|
||||
<html class="writer-html5" lang="en" data-content_root="../../">
|
||||
<head>
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>quapy.evaluation — QuaPy: A Python-based open-source framework for quantification 0.1.8 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="../../_static/pygments.css" />
|
||||
<link rel="stylesheet" type="text/css" href="../../_static/css/theme.css" />
|
||||
<title>quapy.evaluation — QuaPy: A Python-based open-source framework for quantification 0.2.1 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=b86133f3" />
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<link rel="stylesheet" type="text/css" href="../../_static/css/theme.css?v=9edc463e" />
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<script src="../../_static/js/html5shiv.min.js"></script>
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<![endif]-->
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<script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js"></script>
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<script src="../../_static/jquery.js"></script>
|
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<script src="../../_static/underscore.js"></script>
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<script src="../../_static/_sphinx_javascript_frameworks_compat.js"></script>
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<script src="../../_static/doctools.js"></script>
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<script src="../../_static/sphinx_highlight.js"></script>
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<script src="../../_static/jquery.js?v=5d32c60e"></script>
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<script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
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<script src="../../_static/documentation_options.js?v=37f418d5"></script>
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<script src="../../_static/doctools.js?v=fd6eb6e6"></script>
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<script src="../../_static/sphinx_highlight.js?v=6ffebe34"></script>
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<link rel="index" title="Index" href="../../genindex.html" />
|
||||
<link rel="search" title="Search" href="../../search.html" />
|
||||
|
|
@ -43,7 +40,13 @@
|
|||
</div>
|
||||
</div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../modules.html">quapy</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../index.html">Quickstart</a></li>
|
||||
</ul>
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../manuals.html">Manuals</a></li>
|
||||
</ul>
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../quapy.html">API</a></li>
|
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</ul>
|
||||
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||||
</div>
|
||||
|
|
@ -71,16 +74,18 @@
|
|||
<div itemprop="articleBody">
|
||||
|
||||
<h1>Source code for quapy.evaluation</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Iterable</span>
|
||||
<span 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">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
|
||||
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.protocol</span> <span class="kn">import</span> <span class="n">AbstractProtocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span><span class="p">,</span> <span class="n">IterateProtocol</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.method.base</span> <span class="kn">import</span> <span class="n">BaseQuantifier</span>
|
||||
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
|
||||
<span></span><span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Iterable</span>
|
||||
<span class="kn">import</span><span class="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">tqdm</span><span class="w"> </span><span class="kn">import</span> <span class="n">tqdm</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.protocol</span><span class="w"> </span><span class="kn">import</span> <span class="n">AbstractProtocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span><span class="p">,</span> <span class="n">IterateProtocol</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseQuantifier</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="prediction"><a class="viewcode-back" href="../../quapy.html#quapy.evaluation.prediction">[docs]</a><span class="k">def</span> <span class="nf">prediction</span><span class="p">(</span>
|
||||
<div class="viewcode-block" id="prediction">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.evaluation.prediction">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">prediction</span><span class="p">(</span>
|
||||
<span class="n">model</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span>
|
||||
<span class="n">protocol</span><span class="p">:</span> <span class="n">AbstractProtocol</span><span class="p">,</span>
|
||||
<span class="n">aggr_speedup</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'auto'</span><span class="p">,</span>
|
||||
|
|
@ -118,7 +123,7 @@
|
|||
<span class="c1"># checks whether the prediction can be made more efficiently; this check consists in verifying if the model is</span>
|
||||
<span class="c1"># of type aggregative, if the protocol is based on LabelledCollection, and if the total number of documents to</span>
|
||||
<span class="c1"># classify using the protocol would exceed the number of test documents in the original collection</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="n">AggregativeQuantifier</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.aggregative</span><span class="w"> </span><span class="kn">import</span> <span class="n">AggregativeQuantifier</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">AggregativeQuantifier</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">protocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">aggr_speedup</span> <span class="o">==</span> <span class="s1">'force'</span><span class="p">:</span>
|
||||
<span class="n">apply_optimization</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
|
|
@ -136,10 +141,11 @@
|
|||
<span class="n">protocol_with_predictions</span> <span class="o">=</span> <span class="n">protocol</span><span class="o">.</span><span class="n">on_preclassified_instances</span><span class="p">(</span><span class="n">pre_classified</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">__prediction_helper</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">aggregate</span><span class="p">,</span> <span class="n">protocol_with_predictions</span><span class="p">,</span> <span class="n">verbose</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">__prediction_helper</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">,</span> <span class="n">protocol</span><span class="p">,</span> <span class="n">verbose</span><span class="p">)</span></div>
|
||||
<span class="k">return</span> <span class="n">__prediction_helper</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">,</span> <span class="n">protocol</span><span class="p">,</span> <span class="n">verbose</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">__prediction_helper</span><span class="p">(</span><span class="n">quantification_fn</span><span class="p">,</span> <span class="n">protocol</span><span class="p">:</span> <span class="n">AbstractProtocol</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">__prediction_helper</span><span class="p">(</span><span class="n">quantification_fn</span><span class="p">,</span> <span class="n">protocol</span><span class="p">:</span> <span class="n">AbstractProtocol</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">sample_instances</span><span class="p">,</span> <span class="n">sample_prev</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">protocol</span><span class="p">(),</span> <span class="n">total</span><span class="o">=</span><span class="n">protocol</span><span class="o">.</span><span class="n">total</span><span class="p">(),</span> <span class="n">desc</span><span class="o">=</span><span class="s1">'predicting'</span><span class="p">)</span> <span class="k">if</span> <span class="n">verbose</span> <span class="k">else</span> <span class="n">protocol</span><span class="p">():</span>
|
||||
<span class="n">estim_prevs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">quantification_fn</span><span class="p">(</span><span class="n">sample_instances</span><span class="p">))</span>
|
||||
|
|
@ -151,7 +157,9 @@
|
|||
<span class="k">return</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="evaluation_report"><a class="viewcode-back" href="../../quapy.html#quapy.evaluation.evaluation_report">[docs]</a><span class="k">def</span> <span class="nf">evaluation_report</span><span class="p">(</span><span class="n">model</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span>
|
||||
<div class="viewcode-block" id="evaluation_report">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.evaluation.evaluation_report">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">evaluation_report</span><span class="p">(</span><span class="n">model</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span>
|
||||
<span class="n">protocol</span><span class="p">:</span> <span class="n">AbstractProtocol</span><span class="p">,</span>
|
||||
<span class="n">error_metrics</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span><span class="n">Callable</span><span class="p">]]</span> <span class="o">=</span> <span class="s1">'mae'</span><span class="p">,</span>
|
||||
<span class="n">aggr_speedup</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'auto'</span><span class="p">,</span>
|
||||
|
|
@ -182,7 +190,8 @@
|
|||
<span class="k">return</span> <span class="n">_prevalence_report</span><span class="p">(</span><span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">error_metrics</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_prevalence_report</span><span class="p">(</span><span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">error_metrics</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">]]</span> <span class="o">=</span> <span class="s1">'mae'</span><span class="p">):</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_prevalence_report</span><span class="p">(</span><span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">error_metrics</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">]]</span> <span class="o">=</span> <span class="s1">'mae'</span><span class="p">):</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">error_metrics</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
|
||||
<span class="n">error_metrics</span> <span class="o">=</span> <span class="p">[</span><span class="n">error_metrics</span><span class="p">]</span>
|
||||
|
|
@ -203,7 +212,9 @@
|
|||
<span class="k">return</span> <span class="n">df</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="evaluate"><a class="viewcode-back" href="../../quapy.html#quapy.evaluation.evaluate">[docs]</a><span class="k">def</span> <span class="nf">evaluate</span><span class="p">(</span>
|
||||
<div class="viewcode-block" id="evaluate">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.evaluation.evaluate">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">evaluate</span><span class="p">(</span>
|
||||
<span class="n">model</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span>
|
||||
<span class="n">protocol</span><span class="p">:</span> <span class="n">AbstractProtocol</span><span class="p">,</span>
|
||||
<span class="n">error_metric</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">],</span>
|
||||
|
|
@ -235,7 +246,10 @@
|
|||
<span class="k">return</span> <span class="n">error_metric</span><span class="p">(</span><span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="evaluate_on_samples"><a class="viewcode-back" href="../../quapy.html#quapy.evaluation.evaluate_on_samples">[docs]</a><span class="k">def</span> <span class="nf">evaluate_on_samples</span><span class="p">(</span>
|
||||
|
||||
<div class="viewcode-block" id="evaluate_on_samples">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.evaluation.evaluate_on_samples">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">evaluate_on_samples</span><span class="p">(</span>
|
||||
<span class="n">model</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span>
|
||||
<span class="n">samples</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="p">],</span>
|
||||
<span class="n">error_metric</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">],</span>
|
||||
|
|
@ -259,6 +273,7 @@
|
|||
|
||||
|
||||
|
||||
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load Diff
|
|
@ -1,23 +1,20 @@
|
|||
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en">
|
||||
<html class="writer-html5" lang="en" data-content_root="../../../">
|
||||
<head>
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>quapy.method._kdey — QuaPy: A Python-based open-source framework for quantification 0.1.8 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
|
||||
<link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
|
||||
<title>quapy.method._kdey — QuaPy: A Python-based open-source framework for quantification 0.2.1 documentation</title>
|
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<link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=b86133f3" />
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|
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@ -43,7 +40,13 @@
|
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|
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|
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|
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|
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<li class="toctree-l1"><a class="reference internal" href="../../../manuals.html">Manuals</a></li>
|
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|
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<ul>
|
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<li class="toctree-l1"><a class="reference internal" href="../../../quapy.html">API</a></li>
|
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|
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</div>
|
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|
|
@ -71,61 +74,85 @@
|
|||
<div itemprop="articleBody">
|
||||
|
||||
<h1>Source code for quapy.method._kdey</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span>
|
||||
<span 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.neighbors</span> <span class="kn">import</span> <span class="n">KernelDensity</span>
|
||||
<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
|
||||
<span class="kn">from</span><span class="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 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.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">sklearn.metrics.pairwise</span> <span class="kn">import</span> <span class="n">rbf_kernel</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.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>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="KDEBase"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase">[docs]</a><span class="k">class</span> <span class="nc">KDEBase</span><span class="p">:</span>
|
||||
<div class="viewcode-block" id="KDEBase">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">KDEBase</span><span class="p">:</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Common ancestor for KDE-based methods. Implements some common routines.</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">BANDWIDTH_METHOD</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'scott'</span><span class="p">,</span> <span class="s1">'silverman'</span><span class="p">]</span>
|
||||
<span class="n">KERNELS</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'gaussian'</span><span class="p">,</span> <span class="s1">'aitchison'</span><span class="p">,</span> <span class="s1">'ilr'</span><span class="p">]</span>
|
||||
|
||||
<span class="nd">@classmethod</span>
|
||||
<span class="k">def</span> <span class="nf">_check_bandwidth</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">bandwidth</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_check_bandwidth</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">bandwidth</span><span class="p">,</span> <span class="n">kernel</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Checks that the bandwidth parameter is correct</span>
|
||||
|
||||
<span class="sd"> :param bandwidth: either a string (see BANDWIDTH_METHOD) or a float</span>
|
||||
<span class="sd"> :return: nothing, but raises an exception for invalid values</span>
|
||||
<span class="sd"> :return: the bandwidth if the check is passed, or raises an exception for invalid values</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">assert</span> <span class="n">bandwidth</span> <span class="ow">in</span> <span class="n">KDEBase</span><span class="o">.</span><span class="n">BANDWIDTH_METHOD</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">,</span> <span class="nb">float</span><span class="p">),</span> \
|
||||
<span class="k">assert</span> <span class="n">bandwidth</span> <span class="ow">in</span> <span class="n">KDEBase</span><span class="o">.</span><span class="n">BANDWIDTH_METHOD</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">,</span> <span class="n">Real</span><span class="p">),</span> \
|
||||
<span class="sa">f</span><span class="s1">'invalid bandwidth, valid ones are </span><span class="si">{</span><span class="n">KDEBase</span><span class="o">.</span><span class="n">BANDWIDTH_METHOD</span><span class="si">}</span><span class="s1"> or float values'</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">,</span> <span class="nb">float</span><span class="p">):</span>
|
||||
<span class="k">assert</span> <span class="mi">0</span> <span class="o"><</span> <span class="n">bandwidth</span> <span class="o"><</span> <span class="mi">1</span><span class="p">,</span> <span class="s2">"the bandwith for KDEy should be in (0,1), since this method models the unit simplex"</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">,</span> <span class="n">Real</span><span class="p">):</span>
|
||||
<span class="n">bandwidth</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">bandwidth</span>
|
||||
|
||||
<div class="viewcode-block" id="KDEBase.get_kde_function"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.get_kde_function">[docs]</a> <span class="k">def</span> <span class="nf">get_kde_function</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">bandwidth</span><span class="p">):</span>
|
||||
<span class="nd">@classmethod</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_check_kernel</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">kernel</span><span class="p">):</span>
|
||||
<span class="k">assert</span> <span class="n">kernel</span> <span class="ow">in</span> <span class="n">KDEBase</span><span class="o">.</span><span class="n">KERNELS</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'unknown </span><span class="si">{</span><span class="n">kernel</span><span class="si">=}</span><span class="s1">'</span>
|
||||
<span class="k">return</span> <span class="n">kernel</span>
|
||||
|
||||
<div class="viewcode-block" id="KDEBase.get_kde_function">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.get_kde_function">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">get_kde_function</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">bandwidth</span><span class="p">,</span> <span class="n">kernel</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Wraps the KDE function from scikit-learn.</span>
|
||||
|
||||
<span class="sd"> :param X: data for which the density function is to be estimated</span>
|
||||
<span class="sd"> :param bandwidth: the bandwidth of the kernel</span>
|
||||
<span class="sd"> :param kernel: the kernel family</span>
|
||||
<span class="sd"> :return: a scikit-learn's KernelDensity object</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform_posteriors</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">kernel</span><span class="p">)</span>
|
||||
<span class="n">bandwidth</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">effective_bandwidth</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">,</span> <span class="n">kernel</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">KernelDensity</span><span class="p">(</span><span class="n">bandwidth</span><span class="o">=</span><span class="n">bandwidth</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="KDEBase.pdf"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.pdf">[docs]</a> <span class="k">def</span> <span class="nf">pdf</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">kde</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="KDEBase.pdf">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.pdf">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">pdf</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">kde</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">kernel</span><span class="p">,</span> <span class="n">log_densities</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Wraps the density evalution of scikit-learn's KDE. Scikit-learn returns log-scores (s), so this</span>
|
||||
<span class="sd"> function returns :math:`e^{s}`</span>
|
||||
|
||||
<span class="sd"> :param kde: a previously fit KDE function</span>
|
||||
<span class="sd"> :param X: the data for which the density is to be estimated</span>
|
||||
<span class="sd"> :param kernel: the kernel family</span>
|
||||
<span class="sd"> :return: np.ndarray with the densities</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">kde</span><span class="o">.</span><span class="n">score_samples</span><span class="p">(</span><span class="n">X</span><span class="p">))</span></div>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform_posteriors</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">kernel</span><span class="p">)</span>
|
||||
<span class="n">log_density</span> <span class="o">=</span> <span class="n">kde</span><span class="o">.</span><span class="n">score_samples</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">log_densities</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">log_density</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">log_density</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="KDEBase.get_mixture_components"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.get_mixture_components">[docs]</a> <span class="k">def</span> <span class="nf">get_mixture_components</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">bandwidth</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="KDEBase.get_mixture_components">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.get_mixture_components">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">get_mixture_components</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">classes</span><span class="p">,</span> <span class="n">bandwidth</span><span class="p">,</span> <span class="n">kernel</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns an array containing the mixture components, i.e., the KDE functions for each class.</span>
|
||||
|
||||
|
|
@ -133,13 +160,72 @@
|
|||
<span class="sd"> :param y: the class labels</span>
|
||||
<span class="sd"> :param n_classes: integer, the number of classes</span>
|
||||
<span class="sd"> :param bandwidth: float, the bandwidth of the kernel</span>
|
||||
<span class="sd"> :param kernel: the kernel family</span>
|
||||
<span class="sd"> :return: a list of KernelDensity objects, each fitted with the corresponding class-specific covariates</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">get_kde_function</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">y</span> <span class="o">==</span> <span class="n">cat</span><span class="p">],</span> <span class="n">bandwidth</span><span class="p">)</span> <span class="k">for</span> <span class="n">cat</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_classes</span><span class="p">)]</span></div></div>
|
||||
<span class="n">class_cond_X</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">cat</span> <span class="ow">in</span> <span class="n">classes</span><span class="p">:</span>
|
||||
<span class="n">selX</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">y</span><span class="o">==</span><span class="n">cat</span><span class="p">]</span>
|
||||
<span class="k">if</span> <span class="n">selX</span><span class="o">.</span><span class="n">size</span><span class="o">==</span><span class="mi">0</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'empty class </span><span class="si">{</span><span class="n">cat</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="n">class_cond_X</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">selX</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">get_kde_function</span><span class="p">(</span><span class="n">X_cond_yi</span><span class="p">,</span> <span class="n">bandwidth</span><span class="p">,</span> <span class="n">kernel</span><span class="p">)</span> <span class="k">for</span> <span class="n">X_cond_yi</span> <span class="ow">in</span> <span class="n">class_cond_X</span><span class="p">]</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="KDEBase.transform_posteriors">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.transform_posteriors">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">transform_posteriors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">kernel</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">kernel</span> <span class="ow">in</span> <span class="p">{</span><span class="s1">'aitchison'</span><span class="p">,</span> <span class="s1">'ilr'</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">'aitchison'</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">clr_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">kernel</span> <span class="o">==</span> <span class="s1">'ilr'</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">ilr_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">X</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="KDEBase.shrink_posteriors">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.shrink_posteriors">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">shrink_posteriors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="n">shrinkage</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">'shrinkage'</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"><=</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">X</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="n">n_classes</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="n">uniform</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">n_classes</span><span class="p">,</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">X</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">shrinkage</span><span class="p">)</span> <span class="o">*</span> <span class="n">X</span> <span class="o">+</span> <span class="n">shrinkage</span> <span class="o">*</span> <span class="n">uniform</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="KDEBase.effective_bandwidth">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.effective_bandwidth">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">effective_bandwidth</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">bandwidth</span><span class="p">,</span> <span class="n">kernel</span><span class="p">):</span>
|
||||
<span class="n">shrinkage</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">'shrinkage'</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">></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">'aitchison'</span><span class="p">,</span> <span class="s1">'ilr'</span><span class="p">}</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">,</span> <span class="n">Real</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">shrinkage</span><span class="p">)</span> <span class="o">*</span> <span class="nb">float</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">bandwidth</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="KDEBase.clr_transform">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.clr_transform">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">clr_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">'clr'</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">clr</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">CLRtransformation</span><span class="p">()</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">clr</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="KDEBase.ilr_transform">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.ilr_transform">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">ilr_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">'ilr'</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">ilr</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">ILRtransformation</span><span class="p">()</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">ilr</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="KDEyML"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyML">[docs]</a><span class="k">class</span> <span class="nc">KDEyML</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">,</span> <span class="n">KDEBase</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="KDEyML">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyML">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">KDEyML</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">,</span> <span class="n">KDEBase</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -165,32 +251,49 @@
|
|||
|
||||
<span class="sd"> which corresponds to the maximum likelihood estimate.</span>
|
||||
|
||||
<span class="sd"> :param classifier: a sklearn's Estimator that generates a binary classifier.</span>
|
||||
<span class="sd"> :param classifier: a scikit-learn's BaseEstimator, or None, in which case the classifier is taken to be</span>
|
||||
<span class="sd"> the one indicated in `qp.environ['DEFAULT_CLS']`</span>
|
||||
<span class="sd"> :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
|
||||
<span class="sd"> learner has been trained outside the quantifier.</span>
|
||||
<span class="sd"> :param val_split: specifies the data used for generating classifier predictions. This specification</span>
|
||||
<span class="sd"> can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span>
|
||||
<span class="sd"> be extracted from the training set; or as an integer (default 5), indicating that the predictions</span>
|
||||
<span class="sd"> are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span>
|
||||
<span class="sd"> for `k`); or as a collection defining the specific set of data to use for validation.</span>
|
||||
<span class="sd"> Alternatively, this set can be specified at fit time by indicating the exact set of data</span>
|
||||
<span class="sd"> on which the predictions are to be generated.</span>
|
||||
<span class="sd"> for `k`); or as a tuple (X,y) defining the specific set of data to use for validation.</span>
|
||||
<span class="sd"> :param bandwidth: float, the bandwidth of the Kernel</span>
|
||||
<span class="sd"> :param n_jobs: number of parallel workers</span>
|
||||
<span class="sd"> :param kernel: kernel of KDE, valid ones are in KDEBase.KERNELS</span>
|
||||
<span class="sd"> :param shrinkage: amount of shrinkage towards the uniform distribution to apply before</span>
|
||||
<span class="sd"> Aitchison/ILR transformations. Must be in ``[0,1)``.</span>
|
||||
<span class="sd"> :param random_state: a seed to be set before fitting any base quantifier (default None)</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">bandwidth</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_check_bandwidth</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span> <span class="o">=</span> <span class="n">bandwidth</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">bandwidth</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
|
||||
<span class="n">kernel</span><span class="o">=</span><span class="s1">'gaussian'</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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_kernel</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
|
||||
<span class="k">assert</span> <span class="mi">0</span> <span class="o"><=</span> <span class="n">shrinkage</span> <span class="o"><</span> <span class="mi">1</span><span class="p">,</span> <span class="s1">'shrinkage must be in [0,1)'</span>
|
||||
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">!=</span> <span class="s1">'gaussian'</span> <span class="ow">or</span> <span class="n">shrinkage</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span> \
|
||||
<span class="s1">'shrinkage is only supported for Aitchison/ILR kernels'</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shrinkage</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">shrinkage</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span>
|
||||
|
||||
<div class="viewcode-block" id="KDEyML.aggregation_fit"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyML.aggregation_fit">[docs]</a> <span class="k">def</span> <span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_mixture_components</span><span class="p">(</span><span class="o">*</span><span class="n">classif_predictions</span><span class="o">.</span><span class="n">Xy</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">n_classes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span><span class="p">)</span>
|
||||
<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="bp">self</span><span class="o">.</span><span class="n">mix_densities</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_mixture_components</span><span class="p">(</span>
|
||||
<span class="n">classif_predictions</span><span class="p">,</span>
|
||||
<span class="n">labels</span><span class="p">,</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">,</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span><span class="p">,</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">kernel</span><span class="p">,</span>
|
||||
<span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
<div class="viewcode-block" id="KDEyML.aggregate"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyML.aggregate">[docs]</a> <span class="k">def</span> <span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="KDEyML.aggregate">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyML.aggregate">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -198,20 +301,35 @@
|
|||
<span class="sd"> :param posteriors: instances in the sample converted into posterior probabilities</span>
|
||||
<span class="sd"> :return: a vector of class prevalence estimates</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">)</span>
|
||||
<span class="n">epsilon</span> <span class="o">=</span> <span class="mf">1e-10</span>
|
||||
<span class="n">n_classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span><span class="p">)</span>
|
||||
<span class="n">test_densities</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">kde_i</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">)</span> <span class="k">for</span> <span class="n">kde_i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span><span class="p">]</span>
|
||||
<span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">):</span>
|
||||
<span class="n">epsilon</span> <span class="o">=</span> <span class="mf">1e-12</span>
|
||||
<span class="n">n_classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">!=</span> <span class="s1">'gaussian'</span> <span class="ow">and</span> <span class="n">n_classes</span> <span class="o">>=</span> <span class="mi">20</span><span class="p">)</span> <span class="ow">or</span> <span class="n">n_classes</span> <span class="o">>=</span> <span class="mi">30</span><span class="p">:</span>
|
||||
<span class="n">test_log_densities</span> <span class="o">=</span> <span class="p">[</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">kde_i</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span><span class="p">,</span> <span class="n">log_densities</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">kde_i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span>
|
||||
<span class="p">]</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">neg_loglikelihood</span><span class="p">(</span><span class="n">prev</span><span class="p">):</span>
|
||||
<span class="n">test_mixture_likelihood</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">prev_i</span> <span class="o">*</span> <span class="n">dens_i</span> <span class="k">for</span> <span class="n">prev_i</span><span class="p">,</span> <span class="n">dens_i</span> <span class="ow">in</span> <span class="nb">zip</span> <span class="p">(</span><span class="n">prev</span><span class="p">,</span> <span class="n">test_densities</span><span class="p">))</span>
|
||||
<span class="n">test_loglikelihood</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">test_mixture_likelihood</span> <span class="o">+</span> <span class="n">epsilon</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">test_loglikelihood</span><span class="p">)</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">neg_loglikelihood</span><span class="p">(</span><span class="n">prev</span><span class="p">):</span>
|
||||
<span class="n">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">return</span> <span class="n">F</span><span class="o">.</span><span class="n">optim_minimize</span><span class="p">(</span><span class="n">neg_loglikelihood</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span></div></div>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">neg_loglikelihood</span><span class="p">(</span><span class="n">prev</span><span class="p">):</span>
|
||||
<span class="n">test_mixture_likelihood</span> <span class="o">=</span> <span class="n">prev</span> <span class="o">@</span> <span class="n">test_densities</span>
|
||||
<span class="n">test_loglikelihood</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">test_mixture_likelihood</span> <span class="o">+</span> <span class="n">epsilon</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">test_loglikelihood</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">optim_minimize</span><span class="p">(</span><span class="n">neg_loglikelihood</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="KDEyHD"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyHD">[docs]</a><span class="k">class</span> <span class="nc">KDEyHD</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">,</span> <span class="n">KDEBase</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="KDEyHD">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyHD">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">KDEyHD</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">,</span> <span class="n">KDEBase</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -242,53 +360,59 @@
|
|||
<span class="sd"> where the datapoints (trials) :math:`x_1,\\ldots,x_t\\sim_{\\mathrm{iid}} r` with :math:`r` the</span>
|
||||
<span class="sd"> uniform distribution.</span>
|
||||
|
||||
<span class="sd"> :param classifier: a sklearn's Estimator that generates a binary classifier.</span>
|
||||
<span class="sd"> :param classifier: a scikit-learn's BaseEstimator, or None, in which case the classifier is taken to be</span>
|
||||
<span class="sd"> the one indicated in `qp.environ['DEFAULT_CLS']`</span>
|
||||
<span class="sd"> :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
|
||||
<span class="sd"> learner has been trained outside the quantifier.</span>
|
||||
<span class="sd"> :param val_split: specifies the data used for generating classifier predictions. This specification</span>
|
||||
<span class="sd"> can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span>
|
||||
<span class="sd"> be extracted from the training set; or as an integer (default 5), indicating that the predictions</span>
|
||||
<span class="sd"> are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span>
|
||||
<span class="sd"> for `k`); or as a collection defining the specific set of data to use for validation.</span>
|
||||
<span class="sd"> Alternatively, this set can be specified at fit time by indicating the exact set of data</span>
|
||||
<span class="sd"> on which the predictions are to be generated.</span>
|
||||
<span class="sd"> for `k`); or as a tuple (X,y) defining the specific set of data to use for validation.</span>
|
||||
<span class="sd"> :param bandwidth: float, the bandwidth of the Kernel</span>
|
||||
<span class="sd"> :param n_jobs: number of parallel workers</span>
|
||||
<span class="sd"> :param random_state: a seed to be set before fitting any base quantifier (default None)</span>
|
||||
<span class="sd"> :param montecarlo_trials: number of Monte Carlo trials (default 10000)</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">divergence</span><span class="p">:</span> <span class="nb">str</span><span class="o">=</span><span class="s1">'HD'</span><span class="p">,</span>
|
||||
<span class="n">bandwidth</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">montecarlo_trials</span><span class="o">=</span><span class="mi">10000</span><span class="p">):</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_check_bandwidth</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
|
||||
<span class="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">'HD'</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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">divergence</span> <span class="o">=</span> <span class="n">divergence</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span> <span class="o">=</span> <span class="n">bandwidth</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">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">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">'gaussian'</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">montecarlo_trials</span> <span class="o">=</span> <span class="n">montecarlo_trials</span>
|
||||
|
||||
<div class="viewcode-block" id="KDEyHD.aggregation_fit"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyHD.aggregation_fit">[docs]</a> <span class="k">def</span> <span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_mixture_components</span><span class="p">(</span><span class="o">*</span><span class="n">classif_predictions</span><span class="o">.</span><span class="n">Xy</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">n_classes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span><span class="p">)</span>
|
||||
<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="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">'gaussian'</span>
|
||||
<span class="p">)</span>
|
||||
|
||||
<span class="n">N</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">montecarlo_trials</span>
|
||||
<span class="n">rs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span>
|
||||
<span class="n">n</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">n_classes</span>
|
||||
<span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reference_samples</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span><span class="n">kde_i</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">N</span><span class="o">//</span><span class="n">n</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">rs</span><span class="p">)</span> <span class="k">for</span> <span class="n">kde_i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span><span class="p">])</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reference_classwise_densities</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">kde_j</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_samples</span><span class="p">)</span> <span class="k">for</span> <span class="n">kde_j</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span><span class="p">])</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reference_classwise_densities</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span>
|
||||
<span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">kde_j</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_samples</span><span class="p">,</span> <span class="s1">'gaussian'</span><span class="p">)</span> <span class="k">for</span> <span class="n">kde_j</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span><span class="p">]</span>
|
||||
<span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reference_density</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">reference_classwise_densities</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="c1"># equiv. to (uniform @ self.reference_classwise_densities)</span>
|
||||
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
<div class="viewcode-block" id="KDEyHD.aggregate"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyHD.aggregate">[docs]</a> <span class="k">def</span> <span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="KDEyHD.aggregate">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyHD.aggregate">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
|
||||
<span class="c1"># we retain all n*N examples (sampled from a mixture with uniform parameter), and then</span>
|
||||
<span class="c1"># apply importance sampling (IS). In this version we compute D(p_alpha||q) with IS</span>
|
||||
<span class="n">n_classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span><span class="p">)</span>
|
||||
|
||||
<span class="n">test_kde</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_kde_function</span><span class="p">(</span><span class="n">posteriors</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span><span class="p">)</span>
|
||||
<span class="n">test_densities</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">test_kde</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_samples</span><span class="p">)</span>
|
||||
<span class="n">test_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">'gaussian'</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">'gaussian'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">f_squared_hellinger</span><span class="p">(</span><span class="n">u</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">f_squared_hellinger</span><span class="p">(</span><span class="n">u</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">u</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span>
|
||||
|
||||
<span class="c1"># todo: this will fail when self.divergence is a callable, and is not the right place to do it anyway</span>
|
||||
|
|
@ -304,15 +428,19 @@
|
|||
<span class="n">p_class</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_classwise_densities</span> <span class="o">+</span> <span class="n">epsilon</span>
|
||||
<span class="n">fracs</span> <span class="o">=</span> <span class="n">p_class</span><span class="o">/</span><span class="n">qs</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">divergence</span><span class="p">(</span><span class="n">prev</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">divergence</span><span class="p">(</span><span class="n">prev</span><span class="p">):</span>
|
||||
<span class="c1"># ps / qs = (prev @ p_class) / qs = prev @ (p_class / qs) = prev @ fracs</span>
|
||||
<span class="n">ps_div_qs</span> <span class="o">=</span> <span class="n">prev</span> <span class="o">@</span> <span class="n">fracs</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span> <span class="n">f</span><span class="p">(</span><span class="n">ps_div_qs</span><span class="p">)</span> <span class="o">*</span> <span class="n">iw</span> <span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">optim_minimize</span><span class="p">(</span><span class="n">divergence</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span></div></div>
|
||||
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">optim_minimize</span><span class="p">(</span><span class="n">divergence</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="KDEyCS"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyCS">[docs]</a><span class="k">class</span> <span class="nc">KDEyCS</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="KDEyCS">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyCS">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">KDEyCS</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -337,26 +465,25 @@
|
|||
|
||||
<span class="sd"> The authors showed that this distribution matching admits a closed-form solution</span>
|
||||
|
||||
<span class="sd"> :param classifier: a sklearn's Estimator that generates a binary classifier.</span>
|
||||
<span class="sd"> :param classifier: a scikit-learn's BaseEstimator, or None, in which case the classifier is taken to be</span>
|
||||
<span class="sd"> the one indicated in `qp.environ['DEFAULT_CLS']`</span>
|
||||
<span class="sd"> :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
|
||||
<span class="sd"> learner has been trained outside the quantifier.</span>
|
||||
<span class="sd"> :param val_split: specifies the data used for generating classifier predictions. This specification</span>
|
||||
<span class="sd"> can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span>
|
||||
<span class="sd"> be extracted from the training set; or as an integer (default 5), indicating that the predictions</span>
|
||||
<span class="sd"> are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span>
|
||||
<span class="sd"> for `k`); or as a collection defining the specific set of data to use for validation.</span>
|
||||
<span class="sd"> Alternatively, this set can be specified at fit time by indicating the exact set of data</span>
|
||||
<span class="sd"> on which the predictions are to be generated.</span>
|
||||
<span class="sd"> for `k`); or as a tuple (X,y) defining the specific set of data to use for validation.</span>
|
||||
<span class="sd"> :param bandwidth: float, the bandwidth of the Kernel</span>
|
||||
<span class="sd"> :param n_jobs: number of parallel workers</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">bandwidth</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="n">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="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span> <span class="o">=</span> <span class="n">bandwidth</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
|
||||
<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="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">'gaussian'</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="nf">gram_matrix_mix_sum</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="KDEyCS.gram_matrix_mix_sum">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyCS.gram_matrix_mix_sum">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">gram_matrix_mix_sum</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="c1"># this adapts the output of the rbf_kernel function (pairwise evaluations of Gaussian kernels k(x,y))</span>
|
||||
<span class="c1"># to contain pairwise evaluations of N(x|mu,Sigma1+Sigma2) with mu=y and Sigma1 and Sigma2 are </span>
|
||||
<span class="c1"># two "scalar matrices" (h^2)*I each, so Sigma1+Sigma2 has scalar 2(h^2) (h is the bandwidth)</span>
|
||||
|
|
@ -368,17 +495,20 @@
|
|||
<span class="n">gram</span> <span class="o">=</span> <span class="n">norm_factor</span> <span class="o">*</span> <span class="n">rbf_kernel</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="n">gamma</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">gram</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span></div>
|
||||
|
||||
<div class="viewcode-block" id="KDEyCS.aggregation_fit"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyCS.aggregation_fit">[docs]</a> <span class="k">def</span> <span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
|
||||
|
||||
<span class="n">P</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">classif_predictions</span><span class="o">.</span><span class="n">Xy</span>
|
||||
<span class="n">n</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">n_classes</span>
|
||||
<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="n">P</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span>
|
||||
<span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
|
||||
|
||||
<span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">))</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">n</span><span class="p">)),</span> \
|
||||
<span class="s1">'label name gaps not allowed in current implementation'</span>
|
||||
|
||||
<span class="c1"># counts_inv keeps track of the relative weight of each datapoint within its class</span>
|
||||
<span class="c1"># (i.e., the weight in its KDE model)</span>
|
||||
<span class="n">counts_inv</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">counts</span><span class="p">())</span>
|
||||
<span class="n">counts_inv</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">counts_from_labels</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">))</span>
|
||||
|
||||
<span class="c1"># tr_tr_sums corresponds to symbol \overline{B} in the paper</span>
|
||||
<span class="n">tr_tr_sums</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">n</span><span class="p">,</span><span class="n">n</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
|
||||
|
|
@ -399,7 +529,10 @@
|
|||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="KDEyCS.aggregate"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyCS.aggregate">[docs]</a> <span class="k">def</span> <span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="KDEyCS.aggregate">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyCS.aggregate">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
|
||||
<span class="n">Ptr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">Ptr</span>
|
||||
<span class="n">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>
|
||||
|
|
@ -419,16 +552,17 @@
|
|||
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
|
||||
<span class="n">tr_te_sums</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gram_matrix_mix_sum</span><span class="p">(</span><span class="n">Ptr</span><span class="p">[</span><span class="n">y</span><span class="o">==</span><span class="n">i</span><span class="p">],</span> <span class="n">Pte</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">divergence</span><span class="p">(</span><span class="n">alpha</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">divergence</span><span class="p">(</span><span class="n">alpha</span><span class="p">):</span>
|
||||
<span class="c1"># called \overline{r} in the paper</span>
|
||||
<span class="n">alpha_ratio</span> <span class="o">=</span> <span class="n">alpha</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">counts_inv</span>
|
||||
|
||||
<span class="c1"># recal that tr_te_sums already accounts for the constant terms (1/Li)*(1/M)</span>
|
||||
<span class="c1"># recall that tr_te_sums already accounts for the constant terms (1/Li)*(1/M)</span>
|
||||
<span class="n">partA</span> <span class="o">=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">((</span><span class="n">alpha_ratio</span> <span class="o">@</span> <span class="n">tr_te_sums</span><span class="p">)</span> <span class="o">*</span> <span class="n">Minv</span><span class="p">)</span>
|
||||
<span class="n">partB</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">alpha_ratio</span> <span class="o">@</span> <span class="n">tr_tr_sums</span> <span class="o">@</span> <span class="n">alpha_ratio</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">partA</span> <span class="o">+</span> <span class="n">partB</span> <span class="c1">#+ partC</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">optim_minimize</span><span class="p">(</span><span class="n">divergence</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span></div></div>
|
||||
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">optim_minimize</span><span class="p">(</span><span class="n">divergence</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
</pre></div>
|
||||
|
||||
|
|
|
|||
|
|
@ -1,23 +1,20 @@
|
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|
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@ -71,17 +74,19 @@
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<div itemprop="articleBody">
|
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|
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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></span><span class="kn">from</span><span class="w"> </span><span class="nn">abc</span><span class="w"> </span><span class="kn">import</span> <span class="n">abstractmethod</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span class="kn">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>
|
||||
<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>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ThresholdOptimization"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization">[docs]</a><span class="k">class</span> <span class="nc">ThresholdOptimization</span><span class="p">(</span><span class="n">BinaryAggregativeQuantifier</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="ThresholdOptimization">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">ThresholdOptimization</span><span class="p">(</span><span class="n">BinaryAggregativeQuantifier</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Abstract class of Threshold Optimization variants for :class:`ACC` as proposed by</span>
|
||||
<span class="sd"> `Forman 2006 <https://dl.acm.org/doi/abs/10.1145/1150402.1150423>`_ and</span>
|
||||
|
|
@ -91,22 +96,29 @@
|
|||
<span class="sd"> that would allow for more true positives and many more false positives, on the grounds this</span>
|
||||
<span class="sd"> would deliver larger denominators.</span>
|
||||
|
||||
<span class="sd"> :param classifier: a sklearn's Estimator that generates a classifier</span>
|
||||
<span class="sd"> :param val_split: indicates the proportion of data to be used as a stratified held-out validation set in which the</span>
|
||||
<span class="sd"> misclassification rates are to be estimated.</span>
|
||||
<span class="sd"> This parameter can be indicated as a real value (between 0 and 1), representing a proportion of</span>
|
||||
<span class="sd"> validation data, or as an integer, indicating that the misclassification rates should be estimated via</span>
|
||||
<span class="sd"> `k`-fold cross validation (this integer stands for the number of folds `k`, defaults 5), or as a</span>
|
||||
<span class="sd"> :class:`quapy.data.base.LabelledCollection` (the split itself).</span>
|
||||
<span class="sd"> :param classifier: a scikit-learn's BaseEstimator, or None, in which case the classifier is taken to be</span>
|
||||
<span class="sd"> the one indicated in `qp.environ['DEFAULT_CLS']`</span>
|
||||
|
||||
<span class="sd"> :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
|
||||
<span class="sd"> learner has been trained outside the quantifier.</span>
|
||||
|
||||
<span class="sd"> :param val_split: specifies the data used for generating classifier predictions. This specification</span>
|
||||
<span class="sd"> can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span>
|
||||
<span class="sd"> be extracted from the training set; or as an integer (default 5), indicating that the predictions</span>
|
||||
<span class="sd"> are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span>
|
||||
<span class="sd"> for `k`); or as a tuple (X,y) defining the specific set of data to use for validation.</span>
|
||||
|
||||
<span class="sd"> :param n_jobs: number of parallel workers</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
|
||||
<span class="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="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="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">-></span> <span class="nb">float</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">-></span> <span class="nb">float</span><span class="p">:</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -117,7 +129,10 @@
|
|||
<span class="sd"> """</span>
|
||||
<span class="o">...</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ThresholdOptimization.discard"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.discard">[docs]</a> <span class="k">def</span> <span class="nf">discard</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-></span> <span class="nb">bool</span><span class="p">:</span>
|
||||
|
||||
<div class="viewcode-block" id="ThresholdOptimization.discard">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.discard">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">discard</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-></span> <span class="nb">bool</span><span class="p">:</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Indicates whether a combination of tpr and fpr should be discarded</span>
|
||||
|
||||
|
|
@ -128,7 +143,8 @@
|
|||
<span class="k">return</span> <span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_eval_candidate_thresholds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_eval_candidate_thresholds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -163,7 +179,9 @@
|
|||
|
||||
<span class="k">return</span> <span class="n">candidates</span>
|
||||
|
||||
<div class="viewcode-block" id="ThresholdOptimization.aggregate_with_threshold"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.aggregate_with_threshold">[docs]</a> <span class="k">def</span> <span class="nf">aggregate_with_threshold</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">tprs</span><span class="p">,</span> <span class="n">fprs</span><span class="p">,</span> <span class="n">thresholds</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="ThresholdOptimization.aggregate_with_threshold">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.aggregate_with_threshold">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">aggregate_with_threshold</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">tprs</span><span class="p">,</span> <span class="n">fprs</span><span class="p">,</span> <span class="n">thresholds</span><span class="p">):</span>
|
||||
<span class="c1"># This function performs the adjusted count for given tpr, fpr, and threshold.</span>
|
||||
<span class="c1"># Note that, due to broadcasting, tprs, fprs, and thresholds could be arrays of length > 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">>=</span> <span class="n">thresholds</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
|
|
@ -171,35 +189,45 @@
|
|||
<span class="n">prevs_estims</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">as_binary_prevalence</span><span class="p">(</span><span class="n">prevs_estims</span><span class="p">,</span> <span class="n">clip_if_necessary</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">prevs_estims</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span></div>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_compute_table</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">y_</span><span class="p">):</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_compute_table</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">y_</span><span class="p">):</span>
|
||||
<span class="n">TP</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">y</span> <span class="o">==</span> <span class="n">y_</span><span class="p">,</span> <span class="n">y</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">pos_label</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
|
||||
<span class="n">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="nf">_compute_tpr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">TP</span><span class="p">,</span> <span class="n">FP</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_compute_tpr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">TP</span><span class="p">,</span> <span class="n">FP</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">TP</span> <span class="o">+</span> <span class="n">FP</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="k">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">def</span> <span class="nf">_compute_fpr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">FP</span><span class="p">,</span> <span class="n">TN</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_compute_fpr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">FP</span><span class="p">,</span> <span class="n">TN</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">FP</span> <span class="o">+</span> <span class="n">TN</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="k">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="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
|
||||
<span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">classif_predictions</span><span class="o">.</span><span class="n">Xy</span>
|
||||
<div class="viewcode-block" id="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="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>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ThresholdOptimization.aggregate"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.aggregate">[docs]</a> <span class="k">def</span> <span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="ThresholdOptimization.aggregate">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.aggregate">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
|
||||
<span class="c1"># the standard behavior is to compute the adjusted count using the best threshold found</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">aggregate_with_threshold</span><span class="p">(</span><span class="n">classif_predictions</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tpr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fpr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span><span class="p">)</span></div></div>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">aggregate_with_threshold</span><span class="p">(</span><span class="n">classif_predictions</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tpr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fpr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="T50"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.T50">[docs]</a><span class="k">class</span> <span class="nc">T50</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="T50">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.T50">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">T50</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Threshold Optimization variant for :class:`ACC` as proposed by</span>
|
||||
<span class="sd"> `Forman 2006 <https://dl.acm.org/doi/abs/10.1145/1150402.1150423>`_ and</span>
|
||||
|
|
@ -207,23 +235,34 @@
|
|||
<span class="sd"> for the threshold that makes `tpr` closest to 0.5.</span>
|
||||
<span class="sd"> The goal is to bring improved stability to the denominator of the adjustment.</span>
|
||||
|
||||
<span class="sd"> :param classifier: a sklearn's Estimator that generates a classifier</span>
|
||||
<span class="sd"> :param val_split: indicates the proportion of data to be used as a stratified held-out validation set in which the</span>
|
||||
<span class="sd"> misclassification rates are to be estimated.</span>
|
||||
<span class="sd"> This parameter can be indicated as a real value (between 0 and 1), representing a proportion of</span>
|
||||
<span class="sd"> validation data, or as an integer, indicating that the misclassification rates should be estimated via</span>
|
||||
<span class="sd"> `k`-fold cross validation (this integer stands for the number of folds `k`, defaults 5), or as a</span>
|
||||
<span class="sd"> :class:`quapy.data.base.LabelledCollection` (the split itself).</span>
|
||||
<span class="sd"> :param classifier: a scikit-learn's BaseEstimator, or None, in which case the classifier is taken to be</span>
|
||||
<span class="sd"> the one indicated in `qp.environ['DEFAULT_CLS']`</span>
|
||||
|
||||
<span class="sd"> :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
|
||||
<span class="sd"> learner has been trained outside the quantifier.</span>
|
||||
|
||||
<span class="sd"> :param val_split: specifies the data used for generating classifier predictions. This specification</span>
|
||||
<span class="sd"> can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span>
|
||||
<span class="sd"> be extracted from the training set; or as an integer (default 5), indicating that the predictions</span>
|
||||
<span class="sd"> are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span>
|
||||
<span class="sd"> for `k`); or as a tuple (X,y) defining the specific set of data to use for validation.</span>
|
||||
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="T50.condition"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.T50.condition">[docs]</a> <span class="k">def</span> <span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-></span> <span class="nb">float</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="mf">0.5</span><span class="p">)</span></div></div>
|
||||
<div class="viewcode-block" id="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">-></span> <span class="nb">float</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="mf">0.5</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MAX"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MAX">[docs]</a><span class="k">class</span> <span class="nc">MAX</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="MAX">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MAX">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">MAX</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Threshold Optimization variant for :class:`ACC` as proposed by</span>
|
||||
<span class="sd"> `Forman 2006 <https://dl.acm.org/doi/abs/10.1145/1150402.1150423>`_ and</span>
|
||||
|
|
@ -231,24 +270,33 @@
|
|||
<span class="sd"> for the threshold that maximizes `tpr-fpr`.</span>
|
||||
<span class="sd"> The goal is to bring improved stability to the denominator of the adjustment.</span>
|
||||
|
||||
<span class="sd"> :param classifier: a sklearn's Estimator that generates a classifier</span>
|
||||
<span class="sd"> :param val_split: indicates the proportion of data to be used as a stratified held-out validation set in which the</span>
|
||||
<span class="sd"> misclassification rates are to be estimated.</span>
|
||||
<span class="sd"> This parameter can be indicated as a real value (between 0 and 1), representing a proportion of</span>
|
||||
<span class="sd"> validation data, or as an integer, indicating that the misclassification rates should be estimated via</span>
|
||||
<span class="sd"> `k`-fold cross validation (this integer stands for the number of folds `k`, defaults 5), or as a</span>
|
||||
<span class="sd"> :class:`quapy.data.base.LabelledCollection` (the split itself).</span>
|
||||
<span class="sd"> :param classifier: a scikit-learn's BaseEstimator, or None, in which case the classifier is taken to be</span>
|
||||
<span class="sd"> the one indicated in `qp.environ['DEFAULT_CLS']`</span>
|
||||
<span class="sd"> :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
|
||||
<span class="sd"> learner has been trained outside the quantifier.</span>
|
||||
<span class="sd"> :param val_split: specifies the data used for generating classifier predictions. This specification</span>
|
||||
<span class="sd"> can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span>
|
||||
<span class="sd"> be extracted from the training set; or as an integer (default 5), indicating that the predictions</span>
|
||||
<span class="sd"> are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span>
|
||||
<span class="sd"> for `k`); or as a tuple (X,y) defining the specific set of data to use for validation.</span>
|
||||
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="MAX.condition"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MAX.condition">[docs]</a> <span class="k">def</span> <span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-></span> <span class="nb">float</span><span class="p">:</span>
|
||||
<div class="viewcode-block" id="MAX.condition">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MAX.condition">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-></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>
|
||||
<span class="k">return</span> <span class="p">(</span><span class="n">fpr</span> <span class="o">-</span> <span class="n">tpr</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="X"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.X">[docs]</a><span class="k">class</span> <span class="nc">X</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="X">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.X">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">X</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Threshold Optimization variant for :class:`ACC` as proposed by</span>
|
||||
<span class="sd"> `Forman 2006 <https://dl.acm.org/doi/abs/10.1145/1150402.1150423>`_ and</span>
|
||||
|
|
@ -256,23 +304,32 @@
|
|||
<span class="sd"> for the threshold that yields `tpr=1-fpr`.</span>
|
||||
<span class="sd"> The goal is to bring improved stability to the denominator of the adjustment.</span>
|
||||
|
||||
<span class="sd"> :param classifier: a sklearn's Estimator that generates a classifier</span>
|
||||
<span class="sd"> :param val_split: indicates the proportion of data to be used as a stratified held-out validation set in which the</span>
|
||||
<span class="sd"> misclassification rates are to be estimated.</span>
|
||||
<span class="sd"> This parameter can be indicated as a real value (between 0 and 1), representing a proportion of</span>
|
||||
<span class="sd"> validation data, or as an integer, indicating that the misclassification rates should be estimated via</span>
|
||||
<span class="sd"> `k`-fold cross validation (this integer stands for the number of folds `k`, defaults 5), or as a</span>
|
||||
<span class="sd"> :class:`quapy.data.base.LabelledCollection` (the split itself).</span>
|
||||
<span class="sd"> :param classifier: a scikit-learn's BaseEstimator, or None, in which case the classifier is taken to be</span>
|
||||
<span class="sd"> the one indicated in `qp.environ['DEFAULT_CLS']`</span>
|
||||
<span class="sd"> :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
|
||||
<span class="sd"> learner has been trained outside the quantifier.</span>
|
||||
<span class="sd"> :param val_split: specifies the data used for generating classifier predictions. This specification</span>
|
||||
<span class="sd"> can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span>
|
||||
<span class="sd"> be extracted from the training set; or as an integer (default 5), indicating that the predictions</span>
|
||||
<span class="sd"> are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span>
|
||||
<span class="sd"> for `k`); or as a tuple (X,y) defining the specific set of data to use for validation.</span>
|
||||
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="X.condition"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.X.condition">[docs]</a> <span class="k">def</span> <span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-></span> <span class="nb">float</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="nb">abs</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="p">(</span><span class="n">tpr</span> <span class="o">+</span> <span class="n">fpr</span><span class="p">))</span></div></div>
|
||||
<div class="viewcode-block" id="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">-></span> <span class="nb">float</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="nb">abs</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="p">(</span><span class="n">tpr</span> <span class="o">+</span> <span class="n">fpr</span><span class="p">))</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MS"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS">[docs]</a><span class="k">class</span> <span class="nc">MS</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="MS">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">MS</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Median Sweep. Threshold Optimization variant for :class:`ACC` as proposed by</span>
|
||||
<span class="sd"> `Forman 2006 <https://dl.acm.org/doi/abs/10.1145/1150402.1150423>`_ and</span>
|
||||
|
|
@ -280,22 +337,30 @@
|
|||
<span class="sd"> class prevalence estimates for all decision thresholds and returns the median of them all.</span>
|
||||
<span class="sd"> The goal is to bring improved stability to the denominator of the adjustment.</span>
|
||||
|
||||
<span class="sd"> :param classifier: a sklearn's Estimator that generates a classifier</span>
|
||||
<span class="sd"> :param val_split: indicates the proportion of data to be used as a stratified held-out validation set in which the</span>
|
||||
<span class="sd"> misclassification rates are to be estimated.</span>
|
||||
<span class="sd"> This parameter can be indicated as a real value (between 0 and 1), representing a proportion of</span>
|
||||
<span class="sd"> validation data, or as an integer, indicating that the misclassification rates should be estimated via</span>
|
||||
<span class="sd"> `k`-fold cross validation (this integer stands for the number of folds `k`, defaults 5), or as a</span>
|
||||
<span class="sd"> :class:`quapy.data.base.LabelledCollection` (the split itself).</span>
|
||||
<span class="sd"> :param classifier: a scikit-learn's BaseEstimator, or None, in which case the classifier is taken to be</span>
|
||||
<span class="sd"> the one indicated in `qp.environ['DEFAULT_CLS']`</span>
|
||||
<span class="sd"> :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
|
||||
<span class="sd"> learner has been trained outside the quantifier.</span>
|
||||
<span class="sd"> :param val_split: specifies the data used for generating classifier predictions. This specification</span>
|
||||
<span class="sd"> can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span>
|
||||
<span class="sd"> be extracted from the training set; or as an integer (default 5), indicating that the predictions</span>
|
||||
<span class="sd"> are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span>
|
||||
<span class="sd"> for `k`); or as a tuple (X,y) defining the specific set of data to use for validation.</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="MS.condition"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS.condition">[docs]</a> <span class="k">def</span> <span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-></span> <span class="nb">float</span><span class="p">:</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="MS.condition">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS.condition">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-></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="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
|
||||
<span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">classif_predictions</span><span class="o">.</span><span class="n">Xy</span>
|
||||
|
||||
<div class="viewcode-block" id="MS.aggregation_fit">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS.aggregation_fit">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
|
||||
<span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span>
|
||||
<span class="c1"># keeps all candidates</span>
|
||||
<span class="n">tprs_fprs_thresholds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_eval_candidate_thresholds</span><span class="p">(</span><span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">tprs</span> <span class="o">=</span> <span class="n">tprs_fprs_thresholds</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span>
|
||||
|
|
@ -303,14 +368,21 @@
|
|||
<span class="bp">self</span><span class="o">.</span><span class="n">thresholds</span> <span class="o">=</span> <span class="n">tprs_fprs_thresholds</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
<div class="viewcode-block" id="MS.aggregate"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS.aggregate">[docs]</a> <span class="k">def</span> <span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="MS.aggregate">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS.aggregate">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
|
||||
<span class="n">prevalences</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aggregate_with_threshold</span><span class="p">(</span><span class="n">classif_predictions</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tprs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fprs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">thresholds</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">prevalences</span><span class="o">.</span><span class="n">ndim</span><span class="o">==</span><span class="mi">2</span><span class="p">:</span>
|
||||
<span class="n">prevalences</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">prevalences</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">prevalences</span></div></div>
|
||||
<span class="k">return</span> <span class="n">prevalences</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MS2"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS2">[docs]</a><span class="k">class</span> <span class="nc">MS2</span><span class="p">(</span><span class="n">MS</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="MS2">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS2">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">MS2</span><span class="p">(</span><span class="n">MS</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Median Sweep 2. Threshold Optimization variant for :class:`ACC` as proposed by</span>
|
||||
<span class="sd"> `Forman 2006 <https://dl.acm.org/doi/abs/10.1145/1150402.1150423>`_ and</span>
|
||||
|
|
@ -319,19 +391,26 @@
|
|||
<span class="sd"> which `tpr-fpr>0.25`</span>
|
||||
<span class="sd"> The goal is to bring improved stability to the denominator of the adjustment.</span>
|
||||
|
||||
<span class="sd"> :param classifier: a sklearn's Estimator that generates a classifier</span>
|
||||
<span class="sd"> :param val_split: indicates the proportion of data to be used as a stratified held-out validation set in which the</span>
|
||||
<span class="sd"> misclassification rates are to be estimated.</span>
|
||||
<span class="sd"> This parameter can be indicated as a real value (between 0 and 1), representing a proportion of</span>
|
||||
<span class="sd"> validation data, or as an integer, indicating that the misclassification rates should be estimated via</span>
|
||||
<span class="sd"> `k`-fold cross validation (this integer stands for the number of folds `k`, defaults 5), or as a</span>
|
||||
<span class="sd"> :class:`quapy.data.base.LabelledCollection` (the split itself).</span>
|
||||
<span class="sd"> :param classifier: a scikit-learn's BaseEstimator, or None, in which case the classifier is taken to be</span>
|
||||
<span class="sd"> the one indicated in `qp.environ['DEFAULT_CLS']`</span>
|
||||
<span class="sd"> :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
|
||||
<span class="sd"> learner has been trained outside the quantifier.</span>
|
||||
<span class="sd"> :param val_split: specifies the data used for generating classifier predictions. This specification</span>
|
||||
<span class="sd"> can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span>
|
||||
<span class="sd"> be extracted from the training set; or as an integer (default 5), indicating that the predictions</span>
|
||||
<span class="sd"> are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span>
|
||||
<span class="sd"> for `k`); or as a tuple (X,y) defining the specific set of data to use for validation.</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="MS2.discard"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS2.discard">[docs]</a> <span class="k">def</span> <span class="nf">discard</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-></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"><=</span> <span class="mf">0.25</span></div></div>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="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">-></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"><=</span> <span class="mf">0.25</span></div>
|
||||
</div>
|
||||
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
|
|
|
|||
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|
|
@ -1,23 +1,20 @@
|
|||
|
||||
|
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<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/js/theme.js"></script>
|
||||
<link rel="index" title="Index" href="../../../genindex.html" />
|
||||
<link rel="search" title="Search" href="../../../search.html" />
|
||||
|
|
@ -43,7 +40,13 @@
|
|||
</div>
|
||||
</div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../modules.html">quapy</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../index.html">Quickstart</a></li>
|
||||
</ul>
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../manuals.html">Manuals</a></li>
|
||||
</ul>
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../quapy.html">API</a></li>
|
||||
</ul>
|
||||
|
||||
</div>
|
||||
|
|
@ -71,63 +74,94 @@
|
|||
<div itemprop="articleBody">
|
||||
|
||||
<h1>Source code for quapy.method.base</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">ABCMeta</span><span class="p">,</span> <span class="n">abstractmethod</span>
|
||||
<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
|
||||
<span></span><span class="kn">from</span><span class="w"> </span><span class="nn">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 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">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">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="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="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="nc">BaseQuantifier</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="BaseQuantifier">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BaseQuantifier">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">BaseQuantifier</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Abstract Quantifier. A quantifier is defined as an object of a class that implements the method :meth:`fit` on</span>
|
||||
<span class="sd"> :class:`quapy.data.base.LabelledCollection`, the method :meth:`quantify`, and the :meth:`set_params` and</span>
|
||||
<span class="sd"> a pair X, y, the method :meth:`predict`, and the :meth:`set_params` and</span>
|
||||
<span class="sd"> :meth:`get_params` for model selection (see :meth:`quapy.model_selection.GridSearchQ`)</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<div class="viewcode-block" id="BaseQuantifier.fit"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BaseQuantifier.fit">[docs]</a> <span class="nd">@abstractmethod</span>
|
||||
<span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="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="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Trains a quantifier.</span>
|
||||
<span class="sd"> Generates a quantifier.</span>
|
||||
|
||||
<span class="sd"> :param data: a :class:`quapy.data.base.LabelledCollection` consisting of the training data</span>
|
||||
<span class="sd"> :param X: array-like, the training instances</span>
|
||||
<span class="sd"> :param y: array-like, the labels</span>
|
||||
<span class="sd"> :return: self</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="o">...</span></div>
|
||||
|
||||
<div class="viewcode-block" id="BaseQuantifier.quantify"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BaseQuantifier.quantify">[docs]</a> <span class="nd">@abstractmethod</span>
|
||||
<span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="BaseQuantifier.predict">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BaseQuantifier.predict">[docs]</a>
|
||||
<span class="nd">@abstractmethod</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Generate class prevalence estimates for the sample's instances</span>
|
||||
|
||||
<span class="sd"> :param instances: array-like</span>
|
||||
<span class="sd"> :param X: array-like, the test instances</span>
|
||||
<span class="sd"> :return: `np.ndarray` of shape `(n_classes,)` with class prevalence estimates.</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="o">...</span></div></div>
|
||||
<span class="o">...</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="BinaryQuantifier"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BinaryQuantifier">[docs]</a><span class="k">class</span> <span class="nc">BinaryQuantifier</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="BaseQuantifier.quantify">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BaseQuantifier.quantify">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Alias to :meth:`predict`, for old compatibility</span>
|
||||
|
||||
<span class="sd"> :param X: array-like</span>
|
||||
<span class="sd"> :return: `np.ndarray` of shape `(n_classes,)` with class prevalence estimates.</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="BinaryQuantifier">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BinaryQuantifier">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">BinaryQuantifier</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_check_binary</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">quantifier_name</span><span class="p">):</span>
|
||||
<span class="k">assert</span> <span class="n">data</span><span class="o">.</span><span class="n">binary</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'</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. '</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="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">'</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. '</span> \
|
||||
<span class="sa">f</span><span class="s1">'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.'</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="OneVsAll"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.OneVsAll">[docs]</a><span class="k">class</span> <span class="nc">OneVsAll</span><span class="p">:</span>
|
||||
|
||||
<div class="viewcode-block" id="OneVsAll">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.OneVsAll">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">OneVsAll</span><span class="p">:</span>
|
||||
<span class="k">pass</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="newOneVsAll"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.newOneVsAll">[docs]</a><span class="k">def</span> <span class="nf">newOneVsAll</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="newOneVsAll">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.newOneVsAll">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">newOneVsAll</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">BaseQuantifier</span><span class="p">),</span> \
|
||||
<span class="sa">f</span><span class="s1">'</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'</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">qp</span><span class="o">.</span><span class="n">method</span><span class="o">.</span><span class="n">aggregative</span><span class="o">.</span><span class="n">AggregativeQuantifier</span><span class="p">):</span>
|
||||
|
|
@ -136,13 +170,16 @@
|
|||
<span class="k">return</span> <span class="n">OneVsAllGeneric</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">n_jobs</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="OneVsAllGeneric"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.OneVsAllGeneric">[docs]</a><span class="k">class</span> <span class="nc">OneVsAllGeneric</span><span class="p">(</span><span class="n">OneVsAll</span><span class="p">,</span> <span class="n">BaseQuantifier</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="OneVsAllGeneric">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.OneVsAllGeneric">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">OneVsAllGeneric</span><span class="p">(</span><span class="n">OneVsAll</span><span class="p">,</span> <span class="n">BaseQuantifier</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Allows any binary quantifier to perform quantification on single-label datasets. The method maintains one binary</span>
|
||||
<span class="sd"> quantifier for each class, and then l1-normalizes the outputs so that the class prevelence values sum up to 1.</span>
|
||||
<span class="sd"> quantifier for each class, and then l1-normalizes the outputs so that the class prevalence values sum up to 1.</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">binary_quantifier</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">BaseQuantifier</span><span class="p">),</span> \
|
||||
<span class="sa">f</span><span class="s1">'</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'</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">qp</span><span class="o">.</span><span class="n">method</span><span class="o">.</span><span class="n">aggregative</span><span class="o">.</span><span class="n">AggregativeQuantifier</span><span class="p">):</span>
|
||||
|
|
@ -151,35 +188,42 @@
|
|||
<span class="bp">self</span><span class="o">.</span><span class="n">binary_quantifier</span> <span class="o">=</span> <span class="n">binary_quantifier</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_njobs</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="OneVsAllGeneric.fit"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.OneVsAllGeneric.fit">[docs]</a> <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="k">assert</span> <span class="ow">not</span> <span class="n">data</span><span class="o">.</span><span class="n">binary</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'</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'</span>
|
||||
<span class="k">assert</span> <span class="n">fit_classifier</span> <span class="o">==</span> <span class="kc">True</span><span class="p">,</span> <span class="s1">'fit_classifier must be True'</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="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">'</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'</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">dict_binary_quantifiers</span> <span class="o">=</span> <span class="p">{</span><span class="n">c</span><span class="p">:</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">binary_quantifier</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">classes_</span><span class="p">}</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_parallel</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_delayed_binary_fit</span><span class="p">,</span> <span class="n">data</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">dict_binary_quantifiers</span> <span class="o">=</span> <span class="p">{</span><span class="n">c</span><span class="p">:</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">binary_quantifier</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">classes</span><span class="p">}</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_parallel</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_delayed_binary_fit</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_parallel</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_parallel</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span>
|
||||
<span class="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">'threading'</span><span class="p">)(</span>
|
||||
<span class="n">delayed</span><span class="p">(</span><span class="n">func</span><span class="p">)(</span><span class="n">c</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span>
|
||||
<span class="n">delayed</span><span class="p">(</span><span class="n">func</span><span class="p">)(</span><span class="n">c</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">classes</span>
|
||||
<span class="p">)</span>
|
||||
<span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="OneVsAllGeneric.quantify"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.OneVsAllGeneric.quantify">[docs]</a> <span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
|
||||
<span class="n">prevalences</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_parallel</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_delayed_binary_predict</span><span class="p">,</span> <span class="n">instances</span><span class="p">)</span>
|
||||
<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="n">prevalences</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_parallel</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_delayed_binary_predict</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">qp</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">normalize_prevalence</span><span class="p">(</span><span class="n">prevalences</span><span class="p">)</span></div>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">classes_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dict_binary_quantifiers</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_delayed_binary_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">dict_binary_quantifiers</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">X</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="c1"># @property</span>
|
||||
<span class="c1"># def classes_(self):</span>
|
||||
<span class="c1"># return sorted(self.dict_binary_quantifiers.keys())</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_delayed_binary_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">dict_binary_quantifiers</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_delayed_binary_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
<span class="n">bindata</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">==</span> <span class="n">c</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="p">[</span><span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">])</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">dict_binary_quantifiers</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="o">*</span><span class="n">bindata</span><span class="o">.</span><span class="n">Xy</span><span class="p">)</span></div>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_delayed_binary_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
|
||||
<span class="n">bindata</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">labels</span> <span class="o">==</span> <span class="n">c</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="p">[</span><span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">])</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">dict_binary_quantifiers</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">bindata</span><span class="p">)</span></div>
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
|
|
|
|||
|
|
@ -1,23 +1,20 @@
|
|||
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en">
|
||||
<html class="writer-html5" lang="en" data-content_root="../../../">
|
||||
<head>
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>quapy.method.meta — QuaPy: A Python-based open-source framework for quantification 0.1.8 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
|
||||
<link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css" />
|
||||
<title>quapy.method.meta — QuaPy: A Python-based open-source framework for quantification 0.2.1 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=b86133f3" />
|
||||
<link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css?v=9edc463e" />
|
||||
|
||||
|
||||
<!--[if lt IE 9]>
|
||||
<script src="../../../_static/js/html5shiv.min.js"></script>
|
||||
<![endif]-->
|
||||
|
||||
<script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
|
||||
<script src="../../../_static/jquery.js"></script>
|
||||
<script src="../../../_static/underscore.js"></script>
|
||||
<script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
|
||||
<script src="../../../_static/doctools.js"></script>
|
||||
<script src="../../../_static/sphinx_highlight.js"></script>
|
||||
<script src="../../../_static/jquery.js?v=5d32c60e"></script>
|
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<script src="../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<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/js/theme.js"></script>
|
||||
<link rel="index" title="Index" href="../../../genindex.html" />
|
||||
<link rel="search" title="Search" href="../../../search.html" />
|
||||
|
|
@ -43,7 +40,13 @@
|
|||
</div>
|
||||
</div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../modules.html">quapy</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../index.html">Quickstart</a></li>
|
||||
</ul>
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../manuals.html">Manuals</a></li>
|
||||
</ul>
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../quapy.html">API</a></li>
|
||||
</ul>
|
||||
|
||||
</div>
|
||||
|
|
@ -71,24 +74,24 @@
|
|||
<div itemprop="articleBody">
|
||||
|
||||
<h1>Source code for quapy.method.meta</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">import</span> <span class="nn">itertools</span>
|
||||
<span 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">Union</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.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">f1_score</span><span class="p">,</span> <span class="n">make_scorer</span><span class="p">,</span> <span class="n">accuracy_score</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">GridSearchCV</span><span class="p">,</span> <span class="n">cross_val_predict</span>
|
||||
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
|
||||
<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">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">Union</span><span class="p">,</span> <span class="n">List</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.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LogisticRegression</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">f1_score</span><span class="p">,</span> <span class="n">make_scorer</span><span class="p">,</span> <span class="n">accuracy_score</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">GridSearchCV</span><span class="p">,</span> <span class="n">cross_val_predict</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">tqdm</span><span class="w"> </span><span class="kn">import</span> <span class="n">tqdm</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy</span> <span class="kn">import</span> <span class="n">functional</span> <span class="k">as</span> <span class="n">F</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.data</span> <span class="kn">import</span> <span class="n">LabelledCollection</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.model_selection</span> <span class="kn">import</span> <span class="n">GridSearchQ</span>
|
||||
<span class="kn">from</span> <span class="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.aggregative</span> <span class="kn">import</span> <span class="n">CC</span><span class="p">,</span> <span class="n">ACC</span><span class="p">,</span> <span class="n">PACC</span><span class="p">,</span> <span class="n">HDy</span><span class="p">,</span> <span class="n">EMQ</span><span class="p">,</span> <span class="n">AggregativeQuantifier</span>
|
||||
<span class="kn">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">functional</span> <span class="k">as</span> <span class="n">F</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.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.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">GridSearchQ</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.aggregative</span><span class="w"> </span><span class="kn">import</span> <span class="n">CC</span><span class="p">,</span> <span class="n">ACC</span><span class="p">,</span> <span class="n">PACC</span><span class="p">,</span> <span class="n">HDy</span><span class="p">,</span> <span class="n">EMQ</span><span class="p">,</span> <span class="n">AggregativeQuantifier</span><span class="p">,</span> <span class="n">AggregativeSoftQuantifier</span>
|
||||
|
||||
<span class="k">try</span><span class="p">:</span>
|
||||
<span class="kn">from</span> <span class="nn">.</span> <span class="kn">import</span> <span class="n">_neural</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">.</span><span class="w"> </span><span class="kn">import</span> <span class="n">_neural</span>
|
||||
<span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
|
||||
<span class="n">_neural</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
|
||||
|
|
@ -99,7 +102,9 @@
|
|||
<span class="n">QuaNet</span> <span class="o">=</span> <span class="s2">"QuaNet is not available due to missing torch package"</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MedianEstimator2"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator2">[docs]</a><span class="k">class</span> <span class="nc">MedianEstimator2</span><span class="p">(</span><span class="n">BinaryQuantifier</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="MedianEstimator2">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator2">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">MedianEstimator2</span><span class="p">(</span><span class="n">BinaryQuantifier</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> This method is a meta-quantifier that returns, as the estimated class prevalence values, the median of the</span>
|
||||
<span class="sd"> estimation returned by differently (hyper)parameterized base quantifiers.</span>
|
||||
|
|
@ -111,54 +116,69 @@
|
|||
<span class="sd"> :param param_grid: the grid or parameters towards which the median will be computed</span>
|
||||
<span class="sd"> :param n_jobs: number of parllel workes</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base_quantifier</span><span class="p">:</span> <span class="n">BinaryQuantifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base_quantifier</span><span class="p">:</span> <span class="n">BinaryQuantifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span> <span class="o">=</span> <span class="n">base_quantifier</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span> <span class="o">=</span> <span class="n">param_grid</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_njobs</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="MedianEstimator2.get_params"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator2.get_params">[docs]</a> <span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="MedianEstimator2.get_params">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator2.get_params">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="o">.</span><span class="n">get_params</span><span class="p">(</span><span class="n">deep</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="MedianEstimator2.set_params"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator2.set_params">[docs]</a> <span class="k">def</span> <span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">params</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="MedianEstimator2.set_params">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator2.set_params">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">params</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span></div>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_delayed_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_delayed_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
|
||||
<span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">):</span>
|
||||
<span class="n">params</span><span class="p">,</span> <span class="n">training</span> <span class="o">=</span> <span class="n">args</span>
|
||||
<span class="n">params</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">args</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">training</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">model</span>
|
||||
|
||||
<div class="viewcode-block" id="MedianEstimator2.fit"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator2.fit">[docs]</a> <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">training</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_check_binary</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
|
||||
<div class="viewcode-block" id="MedianEstimator2.fit">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator2.fit">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_check_binary</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
|
||||
|
||||
<span class="n">configs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">model_selection</span><span class="o">.</span><span class="n">expand_grid</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">models</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_delayed_fit</span><span class="p">,</span>
|
||||
<span class="p">((</span><span class="n">params</span><span class="p">,</span> <span class="n">training</span><span class="p">)</span> <span class="k">for</span> <span class="n">params</span> <span class="ow">in</span> <span class="n">configs</span><span class="p">),</span>
|
||||
<span class="p">((</span><span class="n">params</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="k">for</span> <span class="n">params</span> <span class="ow">in</span> <span class="n">configs</span><span class="p">),</span>
|
||||
<span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'_R_SEED'</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
|
||||
<span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span>
|
||||
<span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_delayed_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
|
||||
<span class="n">model</span><span class="p">,</span> <span class="n">instances</span> <span class="o">=</span> <span class="n">args</span>
|
||||
<span class="k">return</span> <span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="MedianEstimator2.quantify"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator2.quantify">[docs]</a> <span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_delayed_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
|
||||
<span class="n">model</span><span class="p">,</span> <span class="n">instances</span> <span class="o">=</span> <span class="n">args</span>
|
||||
<span class="k">return</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="MedianEstimator2.predict">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator2.predict">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="n">prev_preds</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_delayed_predict</span><span class="p">,</span>
|
||||
<span class="p">((</span><span class="n">model</span><span class="p">,</span> <span class="n">instances</span><span class="p">)</span> <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="p">),</span>
|
||||
<span class="p">((</span><span class="n">model</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span> <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="p">),</span>
|
||||
<span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'_R_SEED'</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
|
||||
<span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span>
|
||||
<span class="p">)</span>
|
||||
<span class="n">prev_preds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prev_preds</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">prev_preds</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span></div></div>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">prev_preds</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MedianEstimator"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator">[docs]</a><span class="k">class</span> <span class="nc">MedianEstimator</span><span class="p">(</span><span class="n">BinaryQuantifier</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="MedianEstimator">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">MedianEstimator</span><span class="p">(</span><span class="n">BinaryQuantifier</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> This method is a meta-quantifier that returns, as the estimated class prevalence values, the median of the</span>
|
||||
<span class="sd"> estimation returned by differently (hyper)parameterized base quantifiers.</span>
|
||||
|
|
@ -168,100 +188,73 @@
|
|||
<span class="sd"> :param base_quantifier: the base, binary quantifier</span>
|
||||
<span class="sd"> :param random_state: a seed to be set before fitting any base quantifier (default None)</span>
|
||||
<span class="sd"> :param param_grid: the grid or parameters towards which the median will be computed</span>
|
||||
<span class="sd"> :param n_jobs: number of parllel workes</span>
|
||||
<span class="sd"> :param n_jobs: number of parallel workers</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base_quantifier</span><span class="p">:</span> <span class="n">BinaryQuantifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">base_quantifier</span><span class="p">:</span> <span class="n">BinaryQuantifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span> <span class="o">=</span> <span class="n">base_quantifier</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span> <span class="o">=</span> <span class="n">param_grid</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_njobs</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="MedianEstimator.get_params"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator.get_params">[docs]</a> <span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="MedianEstimator.get_params">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator.get_params">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="o">.</span><span class="n">get_params</span><span class="p">(</span><span class="n">deep</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="MedianEstimator.set_params"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator.set_params">[docs]</a> <span class="k">def</span> <span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">params</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="MedianEstimator.set_params">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator.set_params">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">params</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span></div>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_delayed_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_delayed_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
|
||||
<span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">):</span>
|
||||
<span class="n">params</span><span class="p">,</span> <span class="n">training</span> <span class="o">=</span> <span class="n">args</span>
|
||||
<span class="n">params</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">args</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">training</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">model</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_delayed_fit_classifier</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
|
||||
<span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">):</span>
|
||||
<span class="n">cls_params</span><span class="p">,</span> <span class="n">training</span> <span class="o">=</span> <span class="n">args</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">cls_params</span><span class="p">)</span>
|
||||
<span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">classifier_fit_predict</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="n">predict_on</span><span class="o">=</span><span class="n">model</span><span class="o">.</span><span class="n">val_split</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">predictions</span><span class="p">)</span>
|
||||
<div class="viewcode-block" id="MedianEstimator.fit">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator.fit">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_check_binary</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_delayed_fit_aggregation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
|
||||
<span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">):</span>
|
||||
<span class="p">((</span><span class="n">model</span><span class="p">,</span> <span class="n">predictions</span><span class="p">),</span> <span class="n">q_params</span><span class="p">),</span> <span class="n">training</span> <span class="o">=</span> <span class="n">args</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">q_params</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">aggregation_fit</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="n">training</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">model</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MedianEstimator.fit"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator.fit">[docs]</a> <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">training</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_check_binary</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span><span class="p">,</span> <span class="n">AggregativeQuantifier</span><span class="p">):</span>
|
||||
<span class="n">cls_configs</span><span class="p">,</span> <span class="n">q_configs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">model_selection</span><span class="o">.</span><span class="n">group_params</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">cls_configs</span><span class="p">)</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">models_preds</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_delayed_fit_classifier</span><span class="p">,</span>
|
||||
<span class="p">((</span><span class="n">params</span><span class="p">,</span> <span class="n">training</span><span class="p">)</span> <span class="k">for</span> <span class="n">params</span> <span class="ow">in</span> <span class="n">cls_configs</span><span class="p">),</span>
|
||||
<span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'_R_SEED'</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
|
||||
<span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">,</span>
|
||||
<span class="n">asarray</span><span class="o">=</span><span class="kc">False</span>
|
||||
<span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">base_quantifier</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">cls_configs</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
|
||||
<span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">classifier_fit_predict</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="n">predict_on</span><span class="o">=</span><span class="n">model</span><span class="o">.</span><span class="n">val_split</span><span class="p">)</span>
|
||||
<span class="n">models_preds</span> <span class="o">=</span> <span class="p">[(</span><span class="n">model</span><span class="p">,</span> <span class="n">predictions</span><span class="p">)]</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">models</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_delayed_fit_aggregation</span><span class="p">,</span>
|
||||
<span class="p">((</span><span class="n">setup</span><span class="p">,</span> <span class="n">training</span><span class="p">)</span> <span class="k">for</span> <span class="n">setup</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">product</span><span class="p">(</span><span class="n">models_preds</span><span class="p">,</span> <span class="n">q_configs</span><span class="p">)),</span>
|
||||
<span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'_R_SEED'</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
|
||||
<span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">,</span>
|
||||
<span class="n">asarray</span><span class="o">=</span><span class="kc">False</span>
|
||||
<span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">configs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">model_selection</span><span class="o">.</span><span class="n">expand_grid</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">models</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_delayed_fit</span><span class="p">,</span>
|
||||
<span class="p">((</span><span class="n">params</span><span class="p">,</span> <span class="n">training</span><span class="p">)</span> <span class="k">for</span> <span class="n">params</span> <span class="ow">in</span> <span class="n">configs</span><span class="p">),</span>
|
||||
<span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'_R_SEED'</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
|
||||
<span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">,</span>
|
||||
<span class="n">asarray</span><span class="o">=</span><span class="kc">False</span>
|
||||
<span class="p">)</span>
|
||||
<span class="n">configs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">model_selection</span><span class="o">.</span><span class="n">expand_grid</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">models</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_delayed_fit</span><span class="p">,</span>
|
||||
<span class="p">((</span><span class="n">params</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="k">for</span> <span class="n">params</span> <span class="ow">in</span> <span class="n">configs</span><span class="p">),</span>
|
||||
<span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'_R_SEED'</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
|
||||
<span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">,</span>
|
||||
<span class="n">asarray</span><span class="o">=</span><span class="kc">False</span>
|
||||
<span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_delayed_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
|
||||
<span class="n">model</span><span class="p">,</span> <span class="n">instances</span> <span class="o">=</span> <span class="n">args</span>
|
||||
<span class="k">return</span> <span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="MedianEstimator.quantify"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator.quantify">[docs]</a> <span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_delayed_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
|
||||
<span class="n">model</span><span class="p">,</span> <span class="n">instances</span> <span class="o">=</span> <span class="n">args</span>
|
||||
<span class="k">return</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="MedianEstimator.predict">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MedianEstimator.predict">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="n">prev_preds</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_delayed_predict</span><span class="p">,</span>
|
||||
<span class="p">((</span><span class="n">model</span><span class="p">,</span> <span class="n">instances</span><span class="p">)</span> <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="p">),</span>
|
||||
<span class="p">((</span><span class="n">model</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span> <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="p">),</span>
|
||||
<span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'_R_SEED'</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
|
||||
<span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">,</span>
|
||||
<span class="n">asarray</span><span class="o">=</span><span class="kc">False</span>
|
||||
<span class="p">)</span>
|
||||
<span class="n">prev_preds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prev_preds</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">prev_preds</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span></div></div>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">prev_preds</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="Ensemble"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.Ensemble">[docs]</a><span class="k">class</span> <span class="nc">Ensemble</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="Ensemble">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.Ensemble">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">Ensemble</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
|
||||
<span class="n">VALID_POLICIES</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'ave'</span><span class="p">,</span> <span class="s1">'ptr'</span><span class="p">,</span> <span class="s1">'ds'</span><span class="p">}</span> <span class="o">|</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">QUANTIFICATION_ERROR_NAMES</span>
|
||||
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
|
|
@ -301,7 +294,7 @@
|
|||
<span class="sd"> :param verbose: set to True (default is False) to get some information in standard output</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
|
||||
<span class="n">quantifier</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span>
|
||||
<span class="n">size</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span>
|
||||
<span class="n">red_size</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span>
|
||||
|
|
@ -326,17 +319,20 @@
|
|||
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_sample_size</span> <span class="o">=</span> <span class="n">max_sample_size</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_sout</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">msg</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_sout</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">msg</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'[Ensemble]'</span> <span class="o">+</span> <span class="n">msg</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="Ensemble.fit"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.Ensemble.fit">[docs]</a> <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">val_split</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="p">,</span> <span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="Ensemble.fit">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.Ensemble.fit">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">policy</span> <span class="o">==</span> <span class="s1">'ds'</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">data</span><span class="o">.</span><span class="n">binary</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'ds policy is only defined for binary quantification, but this dataset is not binary'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">val_split</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">val_split</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">val_split</span>
|
||||
<span class="n">val_split</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">val_split</span>
|
||||
|
||||
<span class="c1"># randomly chooses the prevalences for each member of the ensemble (preventing classes with less than</span>
|
||||
<span class="c1"># min_pos positive examples)</span>
|
||||
|
|
@ -367,20 +363,26 @@
|
|||
<span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="s1">'Fit [Done]'</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
<div class="viewcode-block" id="Ensemble.quantify"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.Ensemble.quantify">[docs]</a> <span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="Ensemble.predict">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.Ensemble.predict">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="n">predictions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span>
|
||||
<span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span><span class="n">_delayed_quantify</span><span class="p">,</span> <span class="p">((</span><span class="n">Qi</span><span class="p">,</span> <span class="n">instances</span><span class="p">)</span> <span class="k">for</span> <span class="n">Qi</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ensemble</span><span class="p">),</span> <span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">)</span>
|
||||
<span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span><span class="n">_delayed_quantify</span><span class="p">,</span> <span class="p">((</span><span class="n">Qi</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span> <span class="k">for</span> <span class="n">Qi</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ensemble</span><span class="p">),</span> <span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">)</span>
|
||||
<span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">policy</span> <span class="o">==</span> <span class="s1">'ptr'</span><span class="p">:</span>
|
||||
<span class="n">predictions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ptr_policy</span><span class="p">(</span><span class="n">predictions</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">policy</span> <span class="o">==</span> <span class="s1">'ds'</span><span class="p">:</span>
|
||||
<span class="n">predictions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ds_policy</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="n">instances</span><span class="p">)</span>
|
||||
<span class="n">predictions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ds_policy</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
|
||||
|
||||
<span class="n">predictions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">normalize_prevalence</span><span class="p">(</span><span class="n">predictions</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="Ensemble.set_params"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.Ensemble.set_params">[docs]</a> <span class="k">def</span> <span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="Ensemble.set_params">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.Ensemble.set_params">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> This function should not be used within :class:`quapy.model_selection.GridSearchQ` (is here for compatibility</span>
|
||||
<span class="sd"> with the abstract class).</span>
|
||||
|
|
@ -396,7 +398,10 @@
|
|||
<span class="sa">f</span><span class="s1">'or Ensemble(Q(GridSearchCV(l))) with Q a quantifier class that has a classifier '</span>
|
||||
<span class="sa">f</span><span class="s1">'l optimized for classification (not recommended).'</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="Ensemble.get_params"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.Ensemble.get_params">[docs]</a> <span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="Ensemble.get_params">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.Ensemble.get_params">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> This function should not be used within :class:`quapy.model_selection.GridSearchQ` (is here for compatibility</span>
|
||||
<span class="sd"> with the abstract class).</span>
|
||||
|
|
@ -410,13 +415,14 @@
|
|||
|
||||
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_accuracy_policy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">error_name</span><span class="p">):</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_accuracy_policy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">error_name</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Selects the red_size best performant quantifiers in a static way (i.e., dropping all non-selected instances).</span>
|
||||
<span class="sd"> For each model in the ensemble, the performance is measured in terms of _error_name_ on the quantification of</span>
|
||||
<span class="sd"> the samples used for training the rest of the models in the ensemble.</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.evaluation</span> <span class="kn">import</span> <span class="n">evaluate_on_samples</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.evaluation</span><span class="w"> </span><span class="kn">import</span> <span class="n">evaluate_on_samples</span>
|
||||
<span class="n">error</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">from_name</span><span class="p">(</span><span class="n">error_name</span><span class="p">)</span>
|
||||
<span class="n">tests</span> <span class="o">=</span> <span class="p">[</span><span class="n">m</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ensemble</span><span class="p">]</span>
|
||||
<span class="n">scores</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
|
@ -426,7 +432,7 @@
|
|||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">ensemble</span> <span class="o">=</span> <span class="n">_select_k</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ensemble</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">red_size</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_ptr_policy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">predictions</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_ptr_policy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">predictions</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Selects the predictions made by models that have been trained on samples with a prevalence that is most similar</span>
|
||||
<span class="sd"> to a first approximation of the test prevalence as made by all models in the ensemble.</span>
|
||||
|
|
@ -437,7 +443,7 @@
|
|||
<span class="n">order</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">ptr_differences</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">_select_k</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">red_size</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_ds_policy_get_posteriors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_ds_policy_get_posteriors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> In the original article, there are some aspects regarding this method that are not mentioned. The paper says</span>
|
||||
<span class="sd"> that the distribution of posterior probabilities from training and test examples is compared by means of the</span>
|
||||
|
|
@ -468,7 +474,7 @@
|
|||
|
||||
<span class="k">return</span> <span class="n">posteriors</span><span class="p">,</span> <span class="n">posteriors_generator</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_ds_policy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">test</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_ds_policy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">test</span><span class="p">):</span>
|
||||
<span class="n">test_posteriors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">post_proba_fn</span><span class="p">(</span><span class="n">test</span><span class="p">)</span>
|
||||
<span class="n">test_distribution</span> <span class="o">=</span> <span class="n">get_probability_distribution</span><span class="p">(</span><span class="n">test_posteriors</span><span class="p">)</span>
|
||||
<span class="n">tr_distributions</span> <span class="o">=</span> <span class="p">[</span><span class="n">m</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ensemble</span><span class="p">]</span>
|
||||
|
|
@ -477,7 +483,7 @@
|
|||
<span class="k">return</span> <span class="n">_select_k</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">red_size</span><span class="p">)</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">aggregative</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">aggregative</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Indicates that the quantifier is not aggregative.</span>
|
||||
|
||||
|
|
@ -486,7 +492,7 @@
|
|||
<span class="k">return</span> <span class="kc">False</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">probabilistic</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">probabilistic</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Indicates that the quantifier is not probabilistic.</span>
|
||||
|
||||
|
|
@ -495,7 +501,10 @@
|
|||
<span class="k">return</span> <span class="kc">False</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="get_probability_distribution"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.get_probability_distribution">[docs]</a><span class="k">def</span> <span class="nf">get_probability_distribution</span><span class="p">(</span><span class="n">posterior_probabilities</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">8</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="get_probability_distribution">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.get_probability_distribution">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">get_probability_distribution</span><span class="p">(</span><span class="n">posterior_probabilities</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">8</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Gets a histogram out of the posterior probabilities (only for the binary case).</span>
|
||||
|
||||
|
|
@ -509,11 +518,12 @@
|
|||
<span class="k">return</span> <span class="n">distribution</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_select_k</span><span class="p">(</span><span class="n">elements</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_select_k</span><span class="p">(</span><span class="n">elements</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="p">[</span><span class="n">elements</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">order</span><span class="p">[:</span><span class="n">k</span><span class="p">]]</span>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_delayed_new_instance</span><span class="p">(</span><span class="n">args</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_delayed_new_instance</span><span class="p">(</span><span class="n">args</span><span class="p">):</span>
|
||||
<span class="n">base_quantifier</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">val_split</span><span class="p">,</span> <span class="n">prev</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">,</span> <span class="n">keep_samples</span><span class="p">,</span> <span class="n">verbose</span><span class="p">,</span> <span class="n">sample_size</span> <span class="o">=</span> <span class="n">args</span>
|
||||
<span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="se">\t</span><span class="s1">fit-start for prev </span><span class="si">{</span><span class="n">F</span><span class="o">.</span><span class="n">strprev</span><span class="p">(</span><span class="n">prev</span><span class="p">)</span><span class="si">}</span><span class="s1">, sample_size=</span><span class="si">{</span><span class="n">sample_size</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
|
|
@ -528,25 +538,25 @@
|
|||
<span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">sample_index</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">val_split</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="n">val_split</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="o">*</span><span class="n">sample</span><span class="o">.</span><span class="n">Xy</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="n">val_split</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="o">*</span><span class="n">sample</span><span class="o">.</span><span class="n">Xy</span><span class="p">)</span>
|
||||
|
||||
<span class="n">tr_prevalence</span> <span class="o">=</span> <span class="n">sample</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span>
|
||||
<span class="n">tr_distribution</span> <span class="o">=</span> <span class="n">get_probability_distribution</span><span class="p">(</span><span class="n">posteriors</span><span class="p">[</span><span class="n">sample_index</span><span class="p">])</span> <span class="k">if</span> <span class="p">(</span><span class="n">posteriors</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)</span> <span class="k">else</span> <span class="kc">None</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">verbose</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="se">\t</span><span class="s1">\--fit-ended for prev </span><span class="si">{</span><span class="n">F</span><span class="o">.</span><span class="n">strprev</span><span class="p">(</span><span class="n">prev</span><span class="p">)</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="se">\t</span><span class="s1">--fit-ended for prev </span><span class="si">{</span><span class="n">F</span><span class="o">.</span><span class="n">strprev</span><span class="p">(</span><span class="n">prev</span><span class="p">)</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">tr_prevalence</span><span class="p">,</span> <span class="n">tr_distribution</span><span class="p">,</span> <span class="n">sample</span> <span class="k">if</span> <span class="n">keep_samples</span> <span class="k">else</span> <span class="kc">None</span><span class="p">)</span>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_delayed_quantify</span><span class="p">(</span><span class="n">args</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_delayed_quantify</span><span class="p">(</span><span class="n">args</span><span class="p">):</span>
|
||||
<span class="n">quantifier</span><span class="p">,</span> <span class="n">instances</span> <span class="o">=</span> <span class="n">args</span>
|
||||
<span class="k">return</span> <span class="n">quantifier</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">quantifier</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_draw_simplex</span><span class="p">(</span><span class="n">ndim</span><span class="p">,</span> <span class="n">min_val</span><span class="p">,</span> <span class="n">max_trials</span><span class="o">=</span><span class="mi">100</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_draw_simplex</span><span class="p">(</span><span class="n">ndim</span><span class="p">,</span> <span class="n">min_val</span><span class="p">,</span> <span class="n">max_trials</span><span class="o">=</span><span class="mi">100</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns a uniform sampling from the ndim-dimensional simplex but guarantees that all dimensions</span>
|
||||
<span class="sd"> are >= min_class_prev (for min_val>0, this makes the sampling not truly uniform)</span>
|
||||
|
|
@ -571,7 +581,7 @@
|
|||
<span class="sa">f</span><span class="s1">'>= </span><span class="si">{</span><span class="n">min_val</span><span class="si">}</span><span class="s1"> is unlikely (it failed after </span><span class="si">{</span><span class="n">max_trials</span><span class="si">}</span><span class="s1"> trials)'</span><span class="p">)</span>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_instantiate_ensemble</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">base_quantifier_class</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">optim</span><span class="p">,</span> <span class="n">param_model_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_instantiate_ensemble</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">base_quantifier_class</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">optim</span><span class="p">,</span> <span class="n">param_model_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">optim</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">base_quantifier</span> <span class="o">=</span> <span class="n">base_quantifier_class</span><span class="p">(</span><span class="n">classifier</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">optim</span> <span class="ow">in</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">CLASSIFICATION_ERROR</span><span class="p">:</span>
|
||||
|
|
@ -590,7 +600,7 @@
|
|||
<span class="k">return</span> <span class="n">Ensemble</span><span class="p">(</span><span class="n">base_quantifier</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_check_error</span><span class="p">(</span><span class="n">error</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_check_error</span><span class="p">(</span><span class="n">error</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">error</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="kc">None</span>
|
||||
<span class="k">if</span> <span class="n">error</span> <span class="ow">in</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">QUANTIFICATION_ERROR</span> <span class="ow">or</span> <span class="n">error</span> <span class="ow">in</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">CLASSIFICATION_ERROR</span><span class="p">:</span>
|
||||
|
|
@ -602,7 +612,9 @@
|
|||
<span class="sa">f</span><span class="s1">'the name of an error function in </span><span class="si">{</span><span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">ERROR_NAMES</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ensembleFactory"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.ensembleFactory">[docs]</a><span class="k">def</span> <span class="nf">ensembleFactory</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">base_quantifier_class</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_model_sel</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
|
||||
<div class="viewcode-block" id="ensembleFactory">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.ensembleFactory">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">ensembleFactory</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">base_quantifier_class</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_model_sel</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
|
||||
<span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Ensemble factory. Provides a unified interface for instantiating ensembles that can be optimized (via model</span>
|
||||
|
|
@ -653,7 +665,10 @@
|
|||
<span class="k">return</span> <span class="n">_instantiate_ensemble</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">base_quantifier_class</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">error</span><span class="p">,</span> <span class="n">param_model_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ECC"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.ECC">[docs]</a><span class="k">def</span> <span class="nf">ECC</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="ECC">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.ECC">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">ECC</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Implements an ensemble of :class:`quapy.method.aggregative.CC` quantifiers, as used by</span>
|
||||
<span class="sd"> `Pérez-Gállego et al., 2019 <https://www.sciencedirect.com/science/article/pii/S1566253517303652>`_.</span>
|
||||
|
|
@ -676,7 +691,10 @@
|
|||
<span class="k">return</span> <span class="n">ensembleFactory</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">CC</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">optim</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="EACC"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.EACC">[docs]</a><span class="k">def</span> <span class="nf">EACC</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="EACC">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.EACC">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">EACC</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Implements an ensemble of :class:`quapy.method.aggregative.ACC` quantifiers, as used by</span>
|
||||
<span class="sd"> `Pérez-Gállego et al., 2019 <https://www.sciencedirect.com/science/article/pii/S1566253517303652>`_.</span>
|
||||
|
|
@ -699,7 +717,10 @@
|
|||
<span class="k">return</span> <span class="n">ensembleFactory</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">ACC</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">optim</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="EPACC"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.EPACC">[docs]</a><span class="k">def</span> <span class="nf">EPACC</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="EPACC">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.EPACC">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">EPACC</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Implements an ensemble of :class:`quapy.method.aggregative.PACC` quantifiers.</span>
|
||||
|
||||
|
|
@ -721,7 +742,10 @@
|
|||
<span class="k">return</span> <span class="n">ensembleFactory</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">PACC</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">optim</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="EHDy"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.EHDy">[docs]</a><span class="k">def</span> <span class="nf">EHDy</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="EHDy">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.EHDy">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">EHDy</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Implements an ensemble of :class:`quapy.method.aggregative.HDy` quantifiers, as used by</span>
|
||||
<span class="sd"> `Pérez-Gállego et al., 2019 <https://www.sciencedirect.com/science/article/pii/S1566253517303652>`_.</span>
|
||||
|
|
@ -744,7 +768,10 @@
|
|||
<span class="k">return</span> <span class="n">ensembleFactory</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">HDy</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">optim</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="EEMQ"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.EEMQ">[docs]</a><span class="k">def</span> <span class="nf">EEMQ</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="EEMQ">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.EEMQ">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">EEMQ</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Implements an ensemble of :class:`quapy.method.aggregative.EMQ` quantifiers.</span>
|
||||
|
||||
|
|
@ -764,6 +791,141 @@
|
|||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ensembleFactory</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">EMQ</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">optim</span><span class="p">,</span> <span class="n">param_mod_sel</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="merge">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.merge">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">merge</span><span class="p">(</span><span class="n">prev_predictions</span><span class="p">,</span> <span class="n">merge_fun</span><span class="p">):</span>
|
||||
<span class="n">prev_predictions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prev_predictions</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">merge_fun</span> <span class="o">==</span> <span class="s1">'median'</span><span class="p">:</span>
|
||||
<span class="n">prevalences</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">prev_predictions</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">prevalences</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">normalize_prevalence</span><span class="p">(</span><span class="n">prevalences</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">'l1'</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">merge_fun</span> <span class="o">==</span> <span class="s1">'mean'</span><span class="p">:</span>
|
||||
<span class="n">prevalences</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">prev_predictions</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'merge function </span><span class="si">{</span><span class="n">merge_fun</span><span class="si">}</span><span class="s1"> not implemented!'</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">prevalences</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="SCMQ">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.SCMQ">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">SCMQ</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">):</span>
|
||||
|
||||
<span class="n">MERGE_FUNCTIONS</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'median'</span><span class="p">,</span> <span class="s1">'mean'</span><span class="p">]</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">quantifiers</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">AggregativeSoftQuantifier</span><span class="p">],</span> <span class="n">merge_fun</span><span class="o">=</span><span class="s1">'median'</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">quantifiers</span> <span class="o">=</span> <span class="p">[</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">q</span><span class="p">)</span> <span class="k">for</span> <span class="n">q</span> <span class="ow">in</span> <span class="n">quantifiers</span><span class="p">]</span>
|
||||
<span class="k">assert</span> <span class="n">merge_fun</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">MERGE_FUNCTIONS</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'unknown </span><span class="si">{</span><span class="n">merge_fun</span><span class="si">=}</span><span class="s1">, valid ones are </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">MERGE_FUNCTIONS</span><span class="si">}</span><span class="s1">'</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">merge_fun</span> <span class="o">=</span> <span class="n">merge_fun</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
|
||||
|
||||
<div class="viewcode-block" id="SCMQ.aggregation_fit">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.SCMQ.aggregation_fit">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
|
||||
<span class="k">for</span> <span class="n">quantifier</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">quantifiers</span><span class="p">:</span>
|
||||
<span class="n">quantifier</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span>
|
||||
<span class="n">quantifier</span><span class="o">.</span><span class="n">aggregation_fit</span><span class="p">(</span><span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="SCMQ.aggregate">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.SCMQ.aggregate">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
|
||||
<span class="n">prev_predictions</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">quantifier_i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">quantifiers</span><span class="p">:</span>
|
||||
<span class="n">prevalence_i</span> <span class="o">=</span> <span class="n">quantifier_i</span><span class="o">.</span><span class="n">aggregate</span><span class="p">(</span><span class="n">classif_predictions</span><span class="p">)</span>
|
||||
<span class="n">prev_predictions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prevalence_i</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">merge</span><span class="p">(</span><span class="n">prev_predictions</span><span class="p">,</span> <span class="n">merge_fun</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">merge_fun</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MCSQ">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MCSQ">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">MCSQ</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifiers</span><span class="p">,</span> <span class="n">quantifier</span><span class="p">:</span> <span class="n">AggregativeSoftQuantifier</span><span class="p">,</span> <span class="n">merge_fun</span><span class="o">=</span><span class="s1">'median'</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">merge_fun</span> <span class="o">=</span> <span class="n">merge_fun</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">mcsqs</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">classifier</span> <span class="ow">in</span> <span class="n">classifiers</span><span class="p">:</span>
|
||||
<span class="n">quantifier</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">quantifier</span><span class="p">)</span>
|
||||
<span class="n">quantifier</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">mcsqs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">quantifier</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="MCSQ.fit">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MCSQ.fit">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
|
||||
<span class="k">for</span> <span class="n">q</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">mcsqs</span><span class="p">:</span>
|
||||
<span class="n">q</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">val_split</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MCSQ.quantify">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MCSQ.quantify">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
|
||||
<span class="n">prev_predictions</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">q</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">mcsqs</span><span class="p">:</span>
|
||||
<span class="n">prevalence_i</span> <span class="o">=</span> <span class="n">q</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
|
||||
<span class="n">prev_predictions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prevalence_i</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">merge</span><span class="p">(</span><span class="n">prev_predictions</span><span class="p">,</span> <span class="n">merge_fun</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">merge_fun</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MCMQ">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MCMQ">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">MCMQ</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifiers</span><span class="p">,</span> <span class="n">quantifiers</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">AggregativeSoftQuantifier</span><span class="p">],</span> <span class="n">merge_fun</span><span class="o">=</span><span class="s1">'median'</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">merge_fun</span> <span class="o">=</span> <span class="n">merge_fun</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">scmqs</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">classifier</span> <span class="ow">in</span> <span class="n">classifiers</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">scmqs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">SCMQ</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">quantifiers</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="n">val_split</span><span class="p">))</span>
|
||||
|
||||
<div class="viewcode-block" id="MCMQ.fit">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MCMQ.fit">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
|
||||
<span class="k">for</span> <span class="n">q</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">scmqs</span><span class="p">:</span>
|
||||
<span class="n">q</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MCMQ.quantify">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.meta.MCMQ.quantify">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
|
||||
<span class="n">prev_predictions</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">q</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">scmqs</span><span class="p">:</span>
|
||||
<span class="n">prevalence_i</span> <span class="o">=</span> <span class="n">q</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
|
||||
<span class="n">prev_predictions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prevalence_i</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">merge</span><span class="p">(</span><span class="n">prev_predictions</span><span class="p">,</span> <span class="n">merge_fun</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">merge_fun</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
|
|
|
|||
|
|
@ -1,23 +1,20 @@
|
|||
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en">
|
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<html class="writer-html5" lang="en" data-content_root="../../../">
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<meta charset="utf-8" />
|
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<title>quapy.method.non_aggregative — QuaPy: A Python-based open-source framework for quantification 0.1.8 documentation</title>
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<title>quapy.method.non_aggregative — QuaPy: A Python-based open-source framework for quantification 0.2.1 documentation</title>
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</div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../modules.html">quapy</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../index.html">Quickstart</a></li>
|
||||
</ul>
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../manuals.html">Manuals</a></li>
|
||||
</ul>
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../../quapy.html">API</a></li>
|
||||
</ul>
|
||||
|
||||
</div>
|
||||
|
|
@ -71,16 +74,25 @@
|
|||
<div itemprop="articleBody">
|
||||
|
||||
<h1>Source code for quapy.method.non_aggregative</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Callable</span>
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span></span><span class="kn">from</span><span class="w"> </span><span class="nn">itertools</span><span class="w"> </span><span class="kn">import</span> <span class="n">product</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">tqdm</span><span class="w"> </span><span class="kn">import</span> <span class="n">tqdm</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Counter</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.feature_extraction.text</span><span class="w"> </span><span class="kn">import</span> <span class="n">CountVectorizer</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.utils</span><span class="w"> </span><span class="kn">import</span> <span class="n">resample</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <span class="n">normalize</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">quapy.functional</span> <span class="kn">import</span> <span class="n">get_divergence</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.data</span> <span class="kn">import</span> <span class="n">LabelledCollection</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">import</span> <span class="nn">quapy.functional</span> <span class="k">as</span> <span class="nn">F</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">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">import</span><span class="w"> </span><span class="nn">quapy.functional</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">F</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">scipy.optimize</span><span class="w"> </span><span class="kn">import</span> <span class="n">lsq_linear</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">scipy</span><span class="w"> </span><span class="kn">import</span> <span class="n">sparse</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MaximumLikelihoodPrevalenceEstimation"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation">[docs]</a><span class="k">class</span> <span class="nc">MaximumLikelihoodPrevalenceEstimation</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="MaximumLikelihoodPrevalenceEstimation">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">MaximumLikelihoodPrevalenceEstimation</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -89,30 +101,41 @@
|
|||
<span class="sd"> any quantification method should beat.</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_classes_</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
|
||||
<div class="viewcode-block" id="MaximumLikelihoodPrevalenceEstimation.fit"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.fit">[docs]</a> <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="MaximumLikelihoodPrevalenceEstimation.fit">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.fit">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Computes the training prevalence and stores it.</span>
|
||||
|
||||
<span class="sd"> :param data: the training sample</span>
|
||||
<span class="sd"> :param X: array-like of shape `(n_samples, n_features)`, the training instances</span>
|
||||
<span class="sd"> :param y: array-like of shape `(n_samples,)`, the labels</span>
|
||||
<span class="sd"> :return: self</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">estimated_prevalence</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_classes_</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">classes_from_labels</span><span class="p">(</span><span class="n">labels</span><span class="o">=</span><span class="n">y</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">estimated_prevalence</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">prevalence_from_labels</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_classes_</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
<div class="viewcode-block" id="MaximumLikelihoodPrevalenceEstimation.quantify"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.quantify">[docs]</a> <span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="MaximumLikelihoodPrevalenceEstimation.predict">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.predict">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Ignores the input instances and returns, as the class prevalence estimantes, the training prevalence.</span>
|
||||
|
||||
<span class="sd"> :param instances: array-like (ignored)</span>
|
||||
<span class="sd"> :param X: array-like (ignored)</span>
|
||||
<span class="sd"> :return: the class prevalence seen during training</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">estimated_prevalence</span></div></div>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">estimated_prevalence</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="DMx"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.DMx">[docs]</a><span class="k">class</span> <span class="nc">DMx</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="DMx">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.DMx">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">DMx</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -125,15 +148,17 @@
|
|||
<span class="sd"> :param n_jobs: number of parallel workers (default None)</span>
|
||||
<span class="sd"> """</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">'HD'</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">'optim_minimize'</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">divergence</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">]</span><span class="o">=</span><span class="s1">'HD'</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">'optim_minimize'</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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">search</span> <span class="o">=</span> <span class="n">search</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
|
||||
|
||||
<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="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>
|
||||
<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="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> `Hellinger Distance x <https://www.sciencedirect.com/science/article/pii/S0020025512004069>`_ (HDx).</span>
|
||||
<span class="sd"> HDx is a method for training binary quantifiers, that models quantification as the problem of</span>
|
||||
|
|
@ -149,14 +174,15 @@
|
|||
<span class="sd"> :return: an instance of this class setup to mimick the performance of the HDx as originally proposed by</span>
|
||||
<span class="sd"> González-Castro, Alaiz-Rodríguez, Alegre (2013)</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="kn">from</span> <span class="nn">quapy.method.meta</span> <span class="kn">import</span> <span class="n">MedianEstimator</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.meta</span><span class="w"> </span><span class="kn">import</span> <span class="n">MedianEstimator</span>
|
||||
|
||||
<span class="n">dmx</span> <span class="o">=</span> <span class="n">DMx</span><span class="p">(</span><span class="n">divergence</span><span class="o">=</span><span class="s1">'HD'</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">'linear_search'</span><span class="p">)</span>
|
||||
<span class="n">nbins</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'nbins'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">110</span><span class="p">,</span> <span class="mi">11</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)}</span>
|
||||
<span class="n">hdx</span> <span class="o">=</span> <span class="n">MedianEstimator</span><span class="p">(</span><span class="n">base_quantifier</span><span class="o">=</span><span class="n">dmx</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">nbins</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">hdx</span></div>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__get_distributions</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">__get_distributions</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
|
||||
<span class="n">histograms</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">feat_idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nfeats</span><span class="p">):</span>
|
||||
|
|
@ -172,7 +198,9 @@
|
|||
|
||||
<span class="k">return</span> <span class="n">distributions</span>
|
||||
|
||||
<div class="viewcode-block" id="DMx.fit"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.DMx.fit">[docs]</a> <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="DMx.fit">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.DMx.fit">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -181,46 +209,222 @@
|
|||
<span class="sd"> training data labelled with class `i`; while `dij = di[j]` is the discrete distribution for feature j in</span>
|
||||
<span class="sd"> training data labelled with class `i`, and `dij[k]` is the fraction of instances with a value in the `k`-th bin.</span>
|
||||
|
||||
<span class="sd"> :param data: the training set</span>
|
||||
<span class="sd"> :param X: array-like of shape `(n_samples, n_features)`, the training instances</span>
|
||||
<span class="sd"> :param y: array-like of shape `(n_samples,)`, the labels</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">Xy</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">nfeats</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">feat_ranges</span> <span class="o">=</span> <span class="n">_get_features_range</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="n">n_classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">validation_distribution</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span>
|
||||
<span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">__get_distributions</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">y</span><span class="o">==</span><span class="n">cat</span><span class="p">])</span> <span class="k">for</span> <span class="n">cat</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">n_classes</span><span class="p">)]</span>
|
||||
<span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">__get_distributions</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">y</span><span class="o">==</span><span class="n">cat</span><span class="p">])</span> <span class="k">for</span> <span class="n">cat</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_classes</span><span class="p">)]</span>
|
||||
<span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
<div class="viewcode-block" id="DMx.quantify"><a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.DMx.quantify">[docs]</a> <span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="DMx.predict">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.DMx.predict">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Searches for the mixture model parameter (the sought prevalence values) that yields a validation distribution</span>
|
||||
<span class="sd"> (the mixture) that best matches the test distribution, in terms of the divergence measure of choice.</span>
|
||||
<span class="sd"> The matching is computed as the average dissimilarity (in terms of the dissimilarity measure of choice)</span>
|
||||
<span class="sd"> between all feature-specific discrete distributions.</span>
|
||||
|
||||
<span class="sd"> :param instances: instances in the sample</span>
|
||||
<span class="sd"> :param X: instances in the sample</span>
|
||||
<span class="sd"> :return: a vector of class prevalence estimates</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">assert</span> <span class="n">instances</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">nfeats</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'wrong shape; expected </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">nfeats</span><span class="si">}</span><span class="s1">, found </span><span class="si">{</span><span class="n">instances</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s1">'</span>
|
||||
<span class="k">assert</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">nfeats</span><span class="p">,</span> <span class="sa">f</span><span class="s1">'wrong shape; expected </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">nfeats</span><span class="si">}</span><span class="s1">, found </span><span class="si">{</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s1">'</span>
|
||||
|
||||
<span class="n">test_distribution</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__get_distributions</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
|
||||
<span class="n">test_distribution</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__get_distributions</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="n">divergence</span> <span class="o">=</span> <span class="n">get_divergence</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">divergence</span><span class="p">)</span>
|
||||
<span class="n">n_classes</span><span class="p">,</span> <span class="n">n_feats</span><span class="p">,</span> <span class="n">nbins</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">validation_distribution</span><span class="o">.</span><span class="n">shape</span>
|
||||
<span class="k">def</span> <span class="nf">loss</span><span class="p">(</span><span class="n">prev</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">loss</span><span class="p">(</span><span class="n">prev</span><span class="p">):</span>
|
||||
<span class="n">prev</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">prev</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">mixture_distribution</span> <span class="o">=</span> <span class="p">(</span><span class="n">prev</span> <span class="o">@</span> <span class="bp">self</span><span class="o">.</span><span class="n">validation_distribution</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">n_classes</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">n_feats</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">divs</span> <span class="o">=</span> <span class="p">[</span><span class="n">divergence</span><span class="p">(</span><span class="n">test_distribution</span><span class="p">[</span><span class="n">feat</span><span class="p">],</span> <span class="n">mixture_distribution</span><span class="p">[</span><span class="n">feat</span><span class="p">])</span> <span class="k">for</span> <span class="n">feat</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_feats</span><span class="p">)]</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">divs</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">argmin_prevalence</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">search</span><span class="p">)</span></div></div>
|
||||
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">argmin_prevalence</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">search</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_get_features_range</span><span class="p">(</span><span class="n">X</span><span class="p">):</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ReadMe">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.ReadMe">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">ReadMe</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">WithConfidenceABC</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> ReadMe is a non-aggregative quantification system proposed by</span>
|
||||
<span class="sd"> `Daniel Hopkins and Gary King, 2007. A method of automated nonparametric content analysis for</span>
|
||||
<span class="sd"> social science. American Journal of Political Science, 54(1):229–247.</span>
|
||||
<span class="sd"> <https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1540-5907.2009.00428.x>`_.</span>
|
||||
<span class="sd"> The idea is to estimate `Q(Y=i)` directly from:</span>
|
||||
|
||||
<span class="sd"> :math:`Q(X)=\\sum_{i=1} Q(X|Y=i) Q(Y=i)`</span>
|
||||
|
||||
<span class="sd"> via least-squares regression, i.e., without incurring the cost of computing posterior probabilities.</span>
|
||||
<span class="sd"> However, this poses a very difficult representation in which the vector `Q(X)` and the matrix `Q(X|Y=i)`</span>
|
||||
<span class="sd"> can be of very high dimensions. In order to render the problem tracktable, ReadMe performs bagging in</span>
|
||||
<span class="sd"> the feature space. ReadMe also combines bagging with bootstrap in order to derive confidence intervals</span>
|
||||
<span class="sd"> around point estimations.</span>
|
||||
|
||||
<span class="sd"> We use the same default parameters as in the official</span>
|
||||
<span class="sd"> `R implementation <https://github.com/iqss-research/ReadMeV1/blob/master/R/prototype.R>`_.</span>
|
||||
|
||||
<span class="sd"> :param prob_model: str ('naive', or 'full'), selects the modality in which the probabilities `Q(X)` and</span>
|
||||
<span class="sd"> `Q(X|Y)` are to be modelled. Options include "full", which corresponds to the original formulation of</span>
|
||||
<span class="sd"> ReadMe, in which X is constrained to be a binary matrix (e.g., of term presence/absence) and in which</span>
|
||||
<span class="sd"> `Q(X)` and `Q(X|Y)` are modelled, respectively, as matrices of `(2^K, 1)` and `(2^K, n)` values, where</span>
|
||||
<span class="sd"> `K` is the number of columns in the data matrix (i.e., `bagging_range`), and `n` is the number of classes.</span>
|
||||
<span class="sd"> Of course, this approach is computationally prohibited for large `K`, so the computation is restricted to data</span>
|
||||
<span class="sd"> matrices with `K<=25` (although we recommend even smaller values of `K`). A much faster model is "naive", which</span>
|
||||
<span class="sd"> considers the `Q(X)` and `Q(X|Y)` be multinomial distributions under the `bag-of-words` perspective. In this</span>
|
||||
<span class="sd"> case, `bagging_range` can be set to much larger values. Default is "full" (i.e., original ReadMe behavior).</span>
|
||||
<span class="sd"> :param bootstrap_trials: int, number of bootstrap trials (default 300)</span>
|
||||
<span class="sd"> :param bagging_trials: int, number of bagging trials (default 300)</span>
|
||||
<span class="sd"> :param bagging_range: int, number of features to keep for each bagging trial (default 15)</span>
|
||||
<span class="sd"> :param confidence_level: float, a value in (0,1) reflecting the desired confidence level (default 0.95)</span>
|
||||
<span class="sd"> :param region: str in 'intervals', 'ellipse', 'ellipse-clr'; indicates the preferred method for</span>
|
||||
<span class="sd"> defining the confidence region (see :class:`WithConfidenceABC`)</span>
|
||||
<span class="sd"> :param random_state: int or None, allows replicability (default None)</span>
|
||||
<span class="sd"> :param verbose: bool, whether to display information during the process (default False)</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">MAX_FEATURES_FOR_EMPIRICAL_ESTIMATION</span> <span class="o">=</span> <span class="mi">25</span>
|
||||
<span class="n">PROBABILISTIC_MODELS</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"naive"</span><span class="p">,</span> <span class="s2">"full"</span><span class="p">]</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
|
||||
<span class="n">prob_model</span><span class="o">=</span><span class="s2">"full"</span><span class="p">,</span>
|
||||
<span class="n">bootstrap_trials</span><span class="o">=</span><span class="mi">300</span><span class="p">,</span>
|
||||
<span class="n">bagging_trials</span><span class="o">=</span><span class="mi">300</span><span class="p">,</span>
|
||||
<span class="n">bagging_range</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span>
|
||||
<span class="n">confidence_level</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span>
|
||||
<span class="n">region</span><span class="o">=</span><span class="s1">'intervals'</span><span class="p">,</span>
|
||||
<span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
|
||||
<span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="k">assert</span> <span class="n">prob_model</span> <span class="ow">in</span> <span class="n">ReadMe</span><span class="o">.</span><span class="n">PROBABILISTIC_MODELS</span><span class="p">,</span> \
|
||||
<span class="sa">f</span><span class="s1">'unknown </span><span class="si">{</span><span class="n">prob_model</span><span class="si">=}</span><span class="s1">, valid ones are </span><span class="si">{</span><span class="n">ReadMe</span><span class="o">.</span><span class="n">PROBABILISTIC_MODELS</span><span class="si">=}</span><span class="s1">'</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">prob_model</span> <span class="o">=</span> <span class="n">prob_model</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">bootstrap_trials</span> <span class="o">=</span> <span class="n">bootstrap_trials</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">bagging_trials</span> <span class="o">=</span> <span class="n">bagging_trials</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">bagging_range</span> <span class="o">=</span> <span class="n">bagging_range</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">confidence_level</span> <span class="o">=</span> <span class="n">confidence_level</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">region</span> <span class="o">=</span> <span class="n">region</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span>
|
||||
|
||||
<div class="viewcode-block" id="ReadMe.fit">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.ReadMe.fit">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_check_matrix</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
|
||||
|
||||
|
||||
<span class="n">Xsize</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
|
||||
|
||||
<span class="c1"># Bootstrap loop</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">Xboots</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">yboots</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bootstrap_trials</span><span class="p">):</span>
|
||||
<span class="n">idx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rng</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">Xsize</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">Xsize</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">Xboots</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">yboots</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">y</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span>
|
||||
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ReadMe.predict_conf">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.ReadMe.predict_conf">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">predict_conf</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">confidence_level</span><span class="o">=</span><span class="mf">0.95</span><span class="p">)</span> <span class="o">-></span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">ConfidenceRegionABC</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_check_matrix</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
|
||||
|
||||
<span class="n">n_features</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="n">boots_prevalences</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">Xboots</span><span class="p">,</span> <span class="n">yboots</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span>
|
||||
<span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Xboots</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">yboots</span><span class="p">),</span>
|
||||
<span class="n">desc</span><span class="o">=</span><span class="s1">'bootstrap predictions'</span><span class="p">,</span> <span class="n">total</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">bootstrap_trials</span><span class="p">,</span> <span class="n">disable</span><span class="o">=</span><span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span>
|
||||
<span class="p">):</span>
|
||||
<span class="n">bagging_estimates</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bagging_trials</span><span class="p">):</span>
|
||||
<span class="n">feat_idx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rng</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">n_features</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">bagging_range</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
|
||||
<span class="n">Xboots_bagging</span> <span class="o">=</span> <span class="n">Xboots</span><span class="p">[:,</span> <span class="n">feat_idx</span><span class="p">]</span>
|
||||
<span class="n">X_boots_bagging</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="n">feat_idx</span><span class="p">]</span>
|
||||
<span class="n">bagging_prev</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_quantify_iteration</span><span class="p">(</span><span class="n">Xboots_bagging</span><span class="p">,</span> <span class="n">yboots</span><span class="p">,</span> <span class="n">X_boots_bagging</span><span class="p">)</span>
|
||||
<span class="n">bagging_estimates</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">bagging_prev</span><span class="p">)</span>
|
||||
|
||||
<span class="n">boots_prevalences</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">bagging_estimates</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
|
||||
|
||||
<span class="n">conf</span> <span class="o">=</span> <span class="n">WithConfidenceABC</span><span class="o">.</span><span class="n">construct_region</span><span class="p">(</span><span class="n">boots_prevalences</span><span class="p">,</span> <span class="n">confidence_level</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">region</span><span class="p">)</span>
|
||||
<span class="n">prev_estim</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">point_estimate</span><span class="p">()</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">prev_estim</span><span class="p">,</span> <span class="n">conf</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ReadMe.predict">
|
||||
<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.ReadMe.predict">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="n">prev_estim</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">predict_conf</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">prev_estim</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_quantify_iteration</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">Xtr</span><span class="p">,</span> <span class="n">ytr</span><span class="p">,</span> <span class="n">Xte</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Single ReadMe estimate."""</span>
|
||||
<span class="n">PX_given_Y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">_compute_P</span><span class="p">(</span><span class="n">Xtr</span><span class="p">[</span><span class="n">ytr</span> <span class="o">==</span> <span class="n">c</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">c</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)])</span>
|
||||
<span class="n">PX</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_P</span><span class="p">(</span><span class="n">Xte</span><span class="p">)</span>
|
||||
|
||||
<span class="n">res</span> <span class="o">=</span> <span class="n">lsq_linear</span><span class="p">(</span><span class="n">A</span><span class="o">=</span><span class="n">PX_given_Y</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="n">PX</span><span class="p">,</span> <span class="n">bounds</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
|
||||
<span class="n">pY</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">res</span><span class="o">.</span><span class="n">x</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">pY</span> <span class="o">/</span> <span class="n">pY</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_check_matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""the "full" model requires estimating empirical distributions; due to the high computational cost,</span>
|
||||
<span class="sd"> this function is only made available for binary matrices"""</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">'full'</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">'the empirical distribution can only be computed efficiently on binary matrices'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_is_binary_matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">data</span> <span class="k">if</span> <span class="n">sparse</span><span class="o">.</span><span class="n">issparse</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="k">else</span> <span class="n">X</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">((</span><span class="n">data</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">data</span> <span class="o">==</span> <span class="mi">1</span><span class="p">))</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_compute_P</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">prob_model</span> <span class="o">==</span> <span class="s1">'naive'</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">'full'</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_empirical_distribution</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'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">'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_empirical_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">></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">'the empirical distribution can only be computed efficiently for dimensions '</span>
|
||||
<span class="sa">f</span><span class="s1">'less or equal than </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">MAX_FEATURES_FOR_EMPIRICAL_ESTIMATION</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># we convert every binary row (e.g., 0 0 1 0 1) into the equivalent number (e.g., 5)</span>
|
||||
<span class="n">K</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="n">binary_powers</span> <span class="o">=</span> <span class="mi">1</span> <span class="o"><<</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">K</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># (2^K, ..., 32, 16, 8, 4, 2, 1)</span>
|
||||
<span class="n">X_as_binary_numbers</span> <span class="o">=</span> <span class="n">X</span> <span class="o">@</span> <span class="n">binary_powers</span>
|
||||
|
||||
<span class="c1"># count occurrences and compute probs</span>
|
||||
<span class="n">counts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">bincount</span><span class="p">(</span><span class="n">X_as_binary_numbers</span><span class="p">,</span> <span class="n">minlength</span><span class="o">=</span><span class="mi">2</span> <span class="o">**</span> <span class="n">K</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
|
||||
<span class="n">probs</span> <span class="o">=</span> <span class="n">counts</span> <span class="o">/</span> <span class="n">counts</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
|
||||
<span class="k">return</span> <span class="n">probs</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_multinomial_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="n">PX</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
|
||||
<span class="n">PX</span> <span class="o">=</span> <span class="n">normalize</span><span class="p">(</span><span class="n">PX</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="s1">'l1'</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">PX</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span></div>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_get_features_range</span><span class="p">(</span><span class="n">X</span><span class="p">):</span>
|
||||
<span class="n">feat_ranges</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">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>
|
||||
|
|
|
|||
|
|
@ -1,23 +1,20 @@
|
|||
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en">
|
||||
<html class="writer-html5" lang="en" data-content_root="../../">
|
||||
<head>
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>quapy.model_selection — QuaPy: A Python-based open-source framework for quantification 0.1.8 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="../../_static/pygments.css" />
|
||||
<link rel="stylesheet" type="text/css" href="../../_static/css/theme.css" />
|
||||
<title>quapy.model_selection — QuaPy: A Python-based open-source framework for quantification 0.2.1 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=b86133f3" />
|
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<link rel="stylesheet" type="text/css" href="../../_static/css/theme.css?v=9edc463e" />
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|
||||
<!--[if lt IE 9]>
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||||
<script src="../../_static/js/html5shiv.min.js"></script>
|
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<![endif]-->
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<script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js"></script>
|
||||
<script src="../../_static/jquery.js"></script>
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||||
<script src="../../_static/underscore.js"></script>
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<script src="../../_static/_sphinx_javascript_frameworks_compat.js"></script>
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<script src="../../_static/doctools.js"></script>
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<script src="../../_static/sphinx_highlight.js"></script>
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<script src="../../_static/jquery.js?v=5d32c60e"></script>
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<script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
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<script src="../../_static/documentation_options.js?v=37f418d5"></script>
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<script src="../../_static/doctools.js?v=fd6eb6e6"></script>
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<script src="../../_static/sphinx_highlight.js?v=6ffebe34"></script>
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<script src="../../_static/js/theme.js"></script>
|
||||
<link rel="index" title="Index" href="../../genindex.html" />
|
||||
<link rel="search" title="Search" href="../../search.html" />
|
||||
|
|
@ -43,7 +40,13 @@
|
|||
</div>
|
||||
</div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../modules.html">quapy</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../index.html">Quickstart</a></li>
|
||||
</ul>
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../manuals.html">Manuals</a></li>
|
||||
</ul>
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../quapy.html">API</a></li>
|
||||
</ul>
|
||||
|
||||
</div>
|
||||
|
|
@ -71,52 +74,67 @@
|
|||
<div itemprop="articleBody">
|
||||
|
||||
<h1>Source code for quapy.model_selection</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">import</span> <span class="nn">itertools</span>
|
||||
<span 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></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 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">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">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>
|
||||
<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>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="Status"><a class="viewcode-back" href="../../quapy.html#quapy.model_selection.Status">[docs]</a><span class="k">class</span> <span class="nc">Status</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="Status">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.Status">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">Status</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
|
||||
<span class="n">SUCCESS</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="n">TIMEOUT</span> <span class="o">=</span> <span class="mi">2</span>
|
||||
<span class="n">INVALID</span> <span class="o">=</span> <span class="mi">3</span>
|
||||
<span class="n">ERROR</span> <span class="o">=</span> <span class="mi">4</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ConfigStatus"><a class="viewcode-back" href="../../quapy.html#quapy.model_selection.ConfigStatus">[docs]</a><span class="k">class</span> <span class="nc">ConfigStatus</span><span class="p">:</span>
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">msg</span><span class="o">=</span><span class="s1">''</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="ConfigStatus">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.ConfigStatus">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">ConfigStatus</span><span class="p">:</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">msg</span><span class="o">=</span><span class="s1">''</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="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="sa">f</span><span class="s1">':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"> '</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">msg</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="ConfigStatus.success"><a class="viewcode-back" href="../../quapy.html#quapy.model_selection.ConfigStatus.success">[docs]</a> <span class="k">def</span> <span class="nf">success</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="ConfigStatus.success">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.ConfigStatus.success">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">success</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">status</span> <span class="o">==</span> <span class="n">Status</span><span class="o">.</span><span class="n">SUCCESS</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ConfigStatus.failed"><a class="viewcode-back" href="../../quapy.html#quapy.model_selection.ConfigStatus.failed">[docs]</a> <span class="k">def</span> <span class="nf">failed</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">status</span> <span class="o">!=</span> <span class="n">Status</span><span class="o">.</span><span class="n">SUCCESS</span></div></div>
|
||||
|
||||
<div class="viewcode-block" id="ConfigStatus.failed">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.ConfigStatus.failed">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">failed</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">status</span> <span class="o">!=</span> <span class="n">Status</span><span class="o">.</span><span class="n">SUCCESS</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="GridSearchQ"><a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ">[docs]</a><span class="k">class</span> <span class="nc">GridSearchQ</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="GridSearchQ">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">GridSearchQ</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""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>
|
||||
|
|
@ -139,7 +157,7 @@
|
|||
<span class="sd"> :param verbose: set to True to get information through the stdout</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
|
||||
<span class="n">model</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span>
|
||||
<span class="n">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>
|
||||
|
|
@ -158,14 +176,14 @@
|
|||
<span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_njobs</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">raise_errors</span> <span class="o">=</span> <span class="n">raise_errors</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">__check_error</span><span class="p">(</span><span class="n">error</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">__check_error_measure</span><span class="p">(</span><span class="n">error</span><span class="p">)</span>
|
||||
<span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">protocol</span><span class="p">,</span> <span class="n">AbstractProtocol</span><span class="p">),</span> <span class="s1">'unknown protocol'</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_sout</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">msg</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_sout</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">msg</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'[</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">'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__check_error</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">error</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">__check_error_measure</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">error</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">error</span> <span class="ow">in</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">QUANTIFICATION_ERROR</span><span class="p">:</span>
|
||||
<span class="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>
|
||||
|
|
@ -176,26 +194,27 @@
|
|||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'unexpected error type; must either be a callable function or a str representing</span><span class="se">\n</span><span class="s1">'</span>
|
||||
<span class="sa">f</span><span class="s1">'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">'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_prepare_classifier</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">cls_params</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_prepare_classifier</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">cls_params</span><span class="p">):</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">job</span><span class="p">(</span><span class="n">cls_params</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">job</span><span class="p">(</span><span class="n">cls_params</span><span class="p">):</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">cls_params</span><span class="p">)</span>
|
||||
<span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">classifier_fit_predict</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_training</span><span class="p">)</span>
|
||||
<span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">classifier_fit_predict</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_training_X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_training_y</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">predictions</span>
|
||||
|
||||
<span class="n">predictions</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_error_handler</span><span class="p">(</span><span class="n">job</span><span class="p">,</span> <span class="n">cls_params</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">'[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]'</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="nf">_prepare_aggregation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_prepare_aggregation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
|
||||
<span class="n">model</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">cls_took</span><span class="p">,</span> <span class="n">cls_params</span><span class="p">,</span> <span class="n">q_params</span> <span class="o">=</span> <span class="n">args</span>
|
||||
<span class="n">model</span> <span class="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="nf">job</span><span class="p">(</span><span class="n">q_params</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">job</span><span class="p">(</span><span class="n">q_params</span><span class="p">):</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">q_params</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">aggregation_fit</span><span class="p">(</span><span class="n">predictions</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_training</span><span class="p">)</span>
|
||||
<span class="n">P</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">predictions</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">aggregation_fit</span><span class="p">(</span><span class="n">P</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
|
||||
<span class="n">score</span> <span class="o">=</span> <span class="n">evaluation</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">protocol</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">protocol</span><span class="p">,</span> <span class="n">error_metric</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">error</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">score</span>
|
||||
|
||||
|
|
@ -203,12 +222,12 @@
|
|||
<span class="bp">self</span><span class="o">.</span><span class="n">_print_status</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">aggr_took</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">model</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="p">(</span><span class="n">cls_took</span><span class="o">+</span><span class="n">aggr_took</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_prepare_nonaggr_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_prepare_nonaggr_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">job</span><span class="p">(</span><span class="n">params</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">job</span><span class="p">(</span><span class="n">params</span><span class="p">):</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_training</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_training_X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_training_y</span><span class="p">)</span>
|
||||
<span class="n">score</span> <span class="o">=</span> <span class="n">evaluation</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">protocol</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">protocol</span><span class="p">,</span> <span class="n">error_metric</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">error</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">score</span>
|
||||
|
||||
|
|
@ -216,7 +235,7 @@
|
|||
<span class="bp">self</span><span class="o">.</span><span class="n">_print_status</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">model</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_break_down_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_break_down_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -231,17 +250,19 @@
|
|||
<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="nf">_compute_scores_aggregative</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">training</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_compute_scores_aggregative</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
<span class="c1"># break down the set of hyperparameters into two: classifier-specific, quantifier-specific</span>
|
||||
<span class="n">cls_configs</span><span class="p">,</span> <span class="n">q_configs</span> <span class="o">=</span> <span class="n">group_params</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># train all classifiers and get the predictions</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_training</span> <span class="o">=</span> <span class="n">training</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_training_X</span> <span class="o">=</span> <span class="n">X</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_training_y</span> <span class="o">=</span> <span class="n">y</span>
|
||||
<span class="n">cls_outs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_prepare_classifier</span><span class="p">,</span>
|
||||
<span class="n">cls_configs</span><span class="p">,</span>
|
||||
<span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'_R_SEED'</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
|
||||
<span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span>
|
||||
<span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">,</span>
|
||||
<span class="n">asarray</span><span class="o">=</span><span class="kc">False</span>
|
||||
<span class="p">)</span>
|
||||
|
||||
<span class="c1"># filter out classifier configurations that yielded any error</span>
|
||||
|
|
@ -266,9 +287,10 @@
|
|||
|
||||
<span class="k">return</span> <span class="n">aggr_outs</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_compute_scores_nonaggregative</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">training</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_compute_scores_nonaggregative</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
<span class="n">configs</span> <span class="o">=</span> <span class="n">expand_grid</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_training</span> <span class="o">=</span> <span class="n">training</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_training_X</span> <span class="o">=</span> <span class="n">X</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_training_y</span> <span class="o">=</span> <span class="n">y</span>
|
||||
<span class="n">scores</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_prepare_nonaggr_model</span><span class="p">,</span>
|
||||
<span class="n">configs</span><span class="p">,</span>
|
||||
|
|
@ -277,17 +299,20 @@
|
|||
<span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">scores</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_print_status</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_print_status</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">status</span><span class="o">.</span><span class="n">success</span><span class="p">():</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">'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]'</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">'error=</span><span class="si">{</span><span class="n">status</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="GridSearchQ.fit"><a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.fit">[docs]</a> <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">training</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="GridSearchQ.fit">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.fit">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">""" Learning routine. Fits methods with all combinations of hyperparameters and selects the one minimizing</span>
|
||||
<span class="sd"> the error metric.</span>
|
||||
|
||||
<span class="sd"> :param training: the training set on which to optimize the hyperparameters</span>
|
||||
<span class="sd"> :param X: array-like, training covariates</span>
|
||||
<span class="sd"> :param y: array-like, labels of training data</span>
|
||||
<span class="sd"> :return: self</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
|
|
@ -303,9 +328,9 @@
|
|||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">'starting model selection with n_jobs=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_break_down_fit</span><span class="p">():</span>
|
||||
<span class="n">results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_scores_aggregative</span><span class="p">(</span><span class="n">training</span><span class="p">)</span>
|
||||
<span class="n">results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_scores_aggregative</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_scores_nonaggregative</span><span class="p">(</span><span class="n">training</span><span class="p">)</span>
|
||||
<span class="n">results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_scores_nonaggregative</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">param_scores_</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">best_score_</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
|
|
@ -320,13 +345,13 @@
|
|||
<span class="bp">self</span><span class="o">.</span><span class="n">param_scores_</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">params</span><span class="p">)]</span> <span class="o">=</span> <span class="n">status</span><span class="o">.</span><span class="n">status</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">error_collector</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">status</span><span class="p">)</span>
|
||||
|
||||
<span class="n">tend</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span><span class="o">-</span><span class="n">tinit</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">fit_time_</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span><span class="o">-</span><span class="n">tinit</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">best_score_</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'no combination of hyperparameters seemed to work'</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">'optimization finished: best params </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">best_params_</span><span class="si">}</span><span class="s1"> (score=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">best_score_</span><span class="si">:</span><span class="s1">.5f</span><span class="si">}</span><span class="s1">) '</span>
|
||||
<span class="sa">f</span><span class="s1">'[took </span><span class="si">{</span><span class="n">tend</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">s]'</span><span class="p">)</span>
|
||||
<span class="sa">f</span><span class="s1">'[took </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">fit_time_</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">s]'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">no_errors</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">error_collector</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">no_errors</span><span class="o">></span><span class="mi">0</span><span class="p">:</span>
|
||||
|
|
@ -338,7 +363,10 @@
|
|||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">protocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
|
||||
<span class="n">tinit</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">'refitting on the whole development set'</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">best_model_</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">training</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">protocol</span><span class="o">.</span><span class="n">get_labelled_collection</span><span class="p">())</span>
|
||||
<span class="n">validation_collection</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">protocol</span><span class="o">.</span><span class="n">get_labelled_collection</span><span class="p">()</span>
|
||||
<span class="n">training_collection</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="n">validation_collection</span><span class="o">.</span><span class="n">classes</span><span class="p">)</span>
|
||||
<span class="n">devel_collection</span> <span class="o">=</span> <span class="n">training_collection</span> <span class="o">+</span> <span class="n">validation_collection</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">best_model_</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="o">*</span><span class="n">devel_collection</span><span class="o">.</span><span class="n">Xy</span><span class="p">)</span>
|
||||
<span class="n">tend</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">tinit</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">refit_time_</span> <span class="o">=</span> <span class="n">tend</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
|
|
@ -347,24 +375,33 @@
|
|||
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
<div class="viewcode-block" id="GridSearchQ.quantify"><a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.quantify">[docs]</a> <span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="GridSearchQ.predict">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.predict">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Estimate class prevalence values using the best model found after calling the :meth:`fit` method.</span>
|
||||
|
||||
<span class="sd"> :param instances: sample contanining the instances</span>
|
||||
<span class="sd"> :param X: sample contanining the instances</span>
|
||||
<span class="sd"> :return: a ndarray of shape `(n_classes)` with class prevalence estimates as according to the best model found</span>
|
||||
<span class="sd"> by the model selection process.</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">'best_model_'</span><span class="p">),</span> <span class="s1">'quantify called before fit'</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">best_model</span><span class="p">()</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span></div>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">best_model</span><span class="p">()</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="GridSearchQ.set_params"><a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.set_params">[docs]</a> <span class="k">def</span> <span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="GridSearchQ.set_params">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.set_params">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Sets the hyper-parameters to explore.</span>
|
||||
|
||||
<span class="sd"> :param parameters: a dictionary with keys the parameter names and values the list of values to explore</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span> <span class="o">=</span> <span class="n">parameters</span></div>
|
||||
|
||||
<div class="viewcode-block" id="GridSearchQ.get_params"><a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.get_params">[docs]</a> <span class="k">def</span> <span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="GridSearchQ.get_params">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.get_params">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""Returns the dictionary of hyper-parameters to explore (`param_grid`)</span>
|
||||
|
||||
<span class="sd"> :param deep: Unused</span>
|
||||
|
|
@ -372,7 +409,10 @@
|
|||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span></div>
|
||||
|
||||
<div class="viewcode-block" id="GridSearchQ.best_model"><a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.best_model">[docs]</a> <span class="k">def</span> <span class="nf">best_model</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="GridSearchQ.best_model">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.best_model">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">best_model</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -383,7 +423,8 @@
|
|||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">best_model_</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'best_model called before fit'</span><span class="p">)</span></div>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_error_handler</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_error_handler</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Endorses one job with two returned values: the status, and the time of execution</span>
|
||||
|
||||
|
|
@ -396,11 +437,11 @@
|
|||
|
||||
<span class="n">output</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_handle</span><span class="p">(</span><span class="n">status</span><span class="p">,</span> <span class="n">exception</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_handle</span><span class="p">(</span><span class="n">status</span><span class="p">,</span> <span class="n">exception</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">raise_errors</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="n">exception</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">ConfigStatus</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">status</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">ConfigStatus</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">msg</span><span class="o">=</span><span class="nb">str</span><span class="p">(</span><span class="n">exception</span><span class="p">))</span>
|
||||
|
||||
<span class="k">try</span><span class="p">:</span>
|
||||
<span class="k">with</span> <span class="n">timeout</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">timeout</span><span class="p">):</span>
|
||||
|
|
@ -421,7 +462,10 @@
|
|||
<span class="k">return</span> <span class="n">output</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="cross_val_predict"><a class="viewcode-back" href="../../quapy.html#quapy.model_selection.cross_val_predict">[docs]</a><span class="k">def</span> <span class="nf">cross_val_predict</span><span class="p">(</span><span class="n">quantifier</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">nfolds</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="cross_val_predict">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.cross_val_predict">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">cross_val_predict</span><span class="p">(</span><span class="n">quantifier</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">nfolds</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Akin to `scikit-learn's cross_val_predict <https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_predict.html>`_</span>
|
||||
<span class="sd"> but for quantification.</span>
|
||||
|
|
@ -436,15 +480,18 @@
|
|||
<span class="n">total_prev</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">n_classes</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">kFCV</span><span class="p">(</span><span class="n">nfolds</span><span class="o">=</span><span class="n">nfolds</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">):</span>
|
||||
<span class="n">quantifier</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train</span><span class="p">)</span>
|
||||
<span class="n">fold_prev</span> <span class="o">=</span> <span class="n">quantifier</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="n">quantifier</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="o">*</span><span class="n">train</span><span class="o">.</span><span class="n">Xy</span><span class="p">)</span>
|
||||
<span class="n">fold_prev</span> <span class="o">=</span> <span class="n">quantifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="n">rel_size</span> <span class="o">=</span> <span class="mf">1.</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">test</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
<span class="n">total_prev</span> <span class="o">+=</span> <span class="n">fold_prev</span><span class="o">*</span><span class="n">rel_size</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">total_prev</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="expand_grid"><a class="viewcode-back" href="../../quapy.html#quapy.model_selection.expand_grid">[docs]</a><span class="k">def</span> <span class="nf">expand_grid</span><span class="p">(</span><span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="expand_grid">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.expand_grid">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">expand_grid</span><span class="p">(</span><span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Expands a param_grid dictionary as a list of configurations.</span>
|
||||
<span class="sd"> Example:</span>
|
||||
|
|
@ -463,7 +510,10 @@
|
|||
<span class="k">return</span> <span class="n">configs</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="group_params"><a class="viewcode-back" href="../../quapy.html#quapy.model_selection.group_params">[docs]</a><span class="k">def</span> <span class="nf">group_params</span><span class="p">(</span><span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="group_params">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.group_params">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">group_params</span><span class="p">(</span><span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -484,6 +534,7 @@
|
|||
|
||||
<span class="k">return</span> <span class="n">classifier_configs</span><span class="p">,</span> <span class="n">quantifier_configs</span></div>
|
||||
|
||||
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
|
|
|
|||
|
|
@ -1,22 +1,20 @@
|
|||
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en" data-content_root="../../">
|
||||
<head>
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>quapy.plot — QuaPy: A Python-based open-source framework for quantification 0.1.8 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=92fd9be5" />
|
||||
<link rel="stylesheet" type="text/css" href="../../_static/css/theme.css?v=19f00094" />
|
||||
<title>quapy.plot — QuaPy: A Python-based open-source framework for quantification 0.2.1 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=b86133f3" />
|
||||
<link rel="stylesheet" type="text/css" href="../../_static/css/theme.css?v=9edc463e" />
|
||||
|
||||
|
||||
<!--[if lt IE 9]>
|
||||
<script src="../../_static/js/html5shiv.min.js"></script>
|
||||
<![endif]-->
|
||||
|
||||
<script src="../../_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<script src="../../_static/documentation_options.js?v=22607128"></script>
|
||||
<script src="../../_static/doctools.js?v=9a2dae69"></script>
|
||||
<script src="../../_static/sphinx_highlight.js?v=dc90522c"></script>
|
||||
<script src="../../_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<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/js/theme.js"></script>
|
||||
<link rel="index" title="Index" href="../../genindex.html" />
|
||||
<link rel="search" title="Search" href="../../search.html" />
|
||||
|
|
@ -42,7 +40,13 @@
|
|||
</div>
|
||||
</div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../modules.html">quapy</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../index.html">Quickstart</a></li>
|
||||
</ul>
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../manuals.html">Manuals</a></li>
|
||||
</ul>
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../quapy.html">API</a></li>
|
||||
</ul>
|
||||
|
||||
</div>
|
||||
|
|
@ -70,16 +74,16 @@
|
|||
<div itemprop="articleBody">
|
||||
|
||||
<h1>Source code for quapy.plot</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
|
||||
<span 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.cm</span> <span class="kn">import</span> <span class="n">get_cmap</span>
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">cm</span>
|
||||
<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="kn">import</span> <span class="n">ttest_ind_from_stats</span>
|
||||
<span class="kn">from</span> <span class="nn">matplotlib.ticker</span> <span class="kn">import</span> <span class="n">ScalarFormatter</span>
|
||||
<span class="kn">import</span> <span class="nn">math</span>
|
||||
<span></span><span class="kn">from</span><span class="w"> </span><span class="nn">collections</span><span class="w"> </span><span class="kn">import</span> <span class="n">defaultdict</span>
|
||||
<span class="kn">import</span><span class="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.pyplot</span><span class="w"> </span><span class="kn">import</span> <span class="n">get_cmap</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">matplotlib</span><span class="w"> </span><span class="kn">import</span> <span class="n">cm</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="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">math</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
|
||||
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">'figure.figsize'</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">'figure.dpi'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">200</span>
|
||||
|
|
@ -88,7 +92,7 @@
|
|||
|
||||
<div class="viewcode-block" id="binary_diagonal">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.plot.binary_diagonal">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">binary_diagonal</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show_std</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">binary_diagonal</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">show_std</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
|
||||
<span class="n">train_prev</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">savepath</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">method_order</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> The diagonal plot displays the predicted prevalence values (along the y-axis) as a function of the true prevalence</span>
|
||||
|
|
@ -97,21 +101,29 @@
|
|||
<span class="sd"> indicating which class is to be taken as the positive class. (For multiclass quantification problems, other plots</span>
|
||||
<span class="sd"> like the :meth:`error_by_drift` might be preferable though).</span>
|
||||
|
||||
<span class="sd"> The format convention is as follows: `method_names`, `true_prevs`, and `estim_prevs` are array-like of the same</span>
|
||||
<span class="sd"> length, with the ith element describing the output of an independent experiment. The elements of `true_prevs`, and</span>
|
||||
<span class="sd"> `estim_prevs` are `ndarrays` with coherent shape for the same experiment. Experiments for the same method on</span>
|
||||
<span class="sd"> different datasets can be used, in which case the method name can appear more than once in `method_names`.</span>
|
||||
|
||||
<span class="sd"> :param method_names: array-like with the method names for each experiment</span>
|
||||
<span class="sd"> :param true_prevs: array-like with the true prevalence values (each being a ndarray with n_classes components) for</span>
|
||||
<span class="sd"> each experiment</span>
|
||||
<span class="sd"> :param estim_prevs: array-like with the estimated prevalence values (each being a ndarray with n_classes components)</span>
|
||||
<span class="sd"> for each experiment</span>
|
||||
<span class="sd"> :param pos_class: index of the positive class</span>
|
||||
<span class="sd"> :param title: the title to be displayed in the plot</span>
|
||||
<span class="sd"> :param show_std: whether or not to show standard deviations (represented by color bands). This might be inconvenient</span>
|
||||
<span class="sd"> :param true_prevs: array-like with the true prevalence values for each experiment. Each entry is a ndarray of</span>
|
||||
<span class="sd"> shape `(n_samples, n_classes)` components.</span>
|
||||
<span class="sd"> :param estim_prevs: array-like with the estimated prevalence values for each experiment. Each entry is a ndarray of</span>
|
||||
<span class="sd"> shape `(n_samples, n_classes)` components and `n_samples` must coincide with the corresponding entry in</span>
|
||||
<span class="sd"> `true_prevs`.</span>
|
||||
<span class="sd"> :param pos_class: index of the positive class (default 1)</span>
|
||||
<span class="sd"> :param title: the title to be displayed in the plot (default None)</span>
|
||||
<span class="sd"> :param show_std: whether to show standard deviations (represented by color bands). This might be inconvenient</span>
|
||||
<span class="sd"> for cases in which many methods are compared, or when the standard deviations are high -- default True)</span>
|
||||
<span class="sd"> :param legend: whether or not to display the leyend (default True)</span>
|
||||
<span class="sd"> :param train_prev: if indicated (default is None), the training prevalence (for the positive class) is hightlighted</span>
|
||||
<span class="sd"> in the plot. This is convenient when all the experiments have been conducted in the same dataset.</span>
|
||||
<span class="sd"> :param legend: whether to display the legend (default True)</span>
|
||||
<span class="sd"> :param train_prev: if indicated (default is None), the training prevalence (for the positive class) is highlighted</span>
|
||||
<span class="sd"> in the plot. This is convenient when all the experiments have been conducted in the same dataset, or in</span>
|
||||
<span class="sd"> datasets with the same training prevalence.</span>
|
||||
<span class="sd"> :param savepath: path where to save the plot. If not indicated (as default), the plot is shown.</span>
|
||||
<span class="sd"> :param method_order: if indicated (default is None), imposes the order in which the methods are processed (i.e.,</span>
|
||||
<span class="sd"> listed in the legend and associated with matplotlib colors).</span>
|
||||
<span class="sd"> :return: returns (fig, ax) matplotlib objects for eventual customisation</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_aspect</span><span class="p">(</span><span class="s1">'equal'</span><span class="p">)</span>
|
||||
|
|
@ -152,31 +164,34 @@
|
|||
|
||||
<span class="k">if</span> <span class="n">legend</span><span class="p">:</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'center left'</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">))</span>
|
||||
<span class="c1"># box = ax.get_position()</span>
|
||||
<span class="c1"># ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])</span>
|
||||
<span class="c1"># ax.legend(loc='lower center',</span>
|
||||
<span class="c1"># bbox_to_anchor=(1, -0.5),</span>
|
||||
<span class="c1"># ncol=(len(method_names)+1)//2)</span>
|
||||
|
||||
<span class="n">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">)</span></div>
|
||||
<span class="n">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="binary_bias_global">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.plot.binary_bias_global">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">binary_bias_global</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">savepath</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">binary_bias_global</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">savepath</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Box-plots displaying the global bias (i.e., signed error computed as the estimated value minus the true value)</span>
|
||||
<span class="sd"> for each quantification method with respect to a given positive class.</span>
|
||||
|
||||
<span class="sd"> The format convention is as follows: `method_names`, `true_prevs`, and `estim_prevs` are array-like of the same</span>
|
||||
<span class="sd"> length, with the ith element describing the output of an independent experiment. The elements of `true_prevs`, and</span>
|
||||
<span class="sd"> `estim_prevs` are `ndarrays` with coherent shape for the same experiment. Experiments for the same method on</span>
|
||||
<span class="sd"> different datasets can be used, in which case the method name can appear more than once in `method_names`.</span>
|
||||
|
||||
<span class="sd"> :param method_names: array-like with the method names for each experiment</span>
|
||||
<span class="sd"> :param true_prevs: array-like with the true prevalence values (each being a ndarray with n_classes components) for</span>
|
||||
<span class="sd"> each experiment</span>
|
||||
<span class="sd"> :param estim_prevs: array-like with the estimated prevalence values (each being a ndarray with n_classes components)</span>
|
||||
<span class="sd"> for each experiment</span>
|
||||
<span class="sd"> :param true_prevs: array-like with the true prevalence values for each experiment. Each entry is a ndarray of</span>
|
||||
<span class="sd"> shape `(n_samples, n_classes)` components.</span>
|
||||
<span class="sd"> :param estim_prevs: array-like with the estimated prevalence values for each experiment. Each entry is a ndarray of</span>
|
||||
<span class="sd"> shape `(n_samples, n_classes)` components and `n_samples` must coincide with the corresponding entry in</span>
|
||||
<span class="sd"> `true_prevs`.</span>
|
||||
<span class="sd"> :param pos_class: index of the positive class</span>
|
||||
<span class="sd"> :param title: the title to be displayed in the plot</span>
|
||||
<span class="sd"> :param title: the title to be displayed in the plot (default None)</span>
|
||||
<span class="sd"> :param savepath: path where to save the plot. If not indicated (as default), the plot is shown.</span>
|
||||
<span class="sd"> :return: returns (fig, ax) matplotlib objects for eventual customisation</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span> <span class="o">=</span> <span class="n">_merge</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">)</span>
|
||||
|
|
@ -195,32 +210,41 @@
|
|||
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">ylabel</span><span class="o">=</span><span class="s1">'error bias'</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="n">title</span><span class="p">)</span>
|
||||
|
||||
<span class="n">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">)</span></div>
|
||||
<span class="n">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="binary_bias_bins">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.plot.binary_bias_bins">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">binary_bias_bins</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">colormap</span><span class="o">=</span><span class="n">cm</span><span class="o">.</span><span class="n">tab10</span><span class="p">,</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">binary_bias_bins</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">pos_class</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">colormap</span><span class="o">=</span><span class="n">cm</span><span class="o">.</span><span class="n">tab10</span><span class="p">,</span>
|
||||
<span class="n">vertical_xticks</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">savepath</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Box-plots displaying the local bias (i.e., signed error computed as the estimated value minus the true value)</span>
|
||||
<span class="sd"> for different bins of (true) prevalence of the positive classs, for each quantification method.</span>
|
||||
<span class="sd"> for different bins of (true) prevalence of the positive class, for each quantification method.</span>
|
||||
|
||||
<span class="sd"> The format convention is as follows: `method_names`, `true_prevs`, and `estim_prevs` are array-like of the same</span>
|
||||
<span class="sd"> length, with the ith element describing the output of an independent experiment. The elements of `true_prevs`, and</span>
|
||||
<span class="sd"> `estim_prevs` are `ndarrays` with coherent shape for the same experiment. Experiments for the same method on</span>
|
||||
<span class="sd"> different datasets can be used, in which case the method name can appear more than once in `method_names`.</span>
|
||||
|
||||
<span class="sd"> :param method_names: array-like with the method names for each experiment</span>
|
||||
<span class="sd"> :param true_prevs: array-like with the true prevalence values (each being a ndarray with n_classes components) for</span>
|
||||
<span class="sd"> each experiment</span>
|
||||
<span class="sd"> :param estim_prevs: array-like with the estimated prevalence values (each being a ndarray with n_classes components)</span>
|
||||
<span class="sd"> for each experiment</span>
|
||||
<span class="sd"> :param true_prevs: array-like with the true prevalence values for each experiment. Each entry is a ndarray of</span>
|
||||
<span class="sd"> shape `(n_samples, n_classes)` components.</span>
|
||||
<span class="sd"> :param estim_prevs: array-like with the estimated prevalence values for each experiment. Each entry is a ndarray of</span>
|
||||
<span class="sd"> shape `(n_samples, n_classes)` components and `n_samples` must coincide with the corresponding entry in</span>
|
||||
<span class="sd"> `true_prevs`.</span>
|
||||
<span class="sd"> :param pos_class: index of the positive class</span>
|
||||
<span class="sd"> :param title: the title to be displayed in the plot</span>
|
||||
<span class="sd"> :param nbins: number of bins</span>
|
||||
<span class="sd"> :param title: the title to be displayed in the plot (default None)</span>
|
||||
<span class="sd"> :param nbins: number of bins (default 5)</span>
|
||||
<span class="sd"> :param colormap: the matplotlib colormap to use (default cm.tab10)</span>
|
||||
<span class="sd"> :param vertical_xticks: whether or not to add secondary grid (default is False)</span>
|
||||
<span class="sd"> :param legend: whether or not to display the legend (default is True)</span>
|
||||
<span class="sd"> :param savepath: path where to save the plot. If not indicated (as default), the plot is shown.</span>
|
||||
<span class="sd"> :return: returns (fig, ax) matplotlib objects for eventual customisation</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="kn">from</span> <span class="nn">pylab</span> <span class="kn">import</span> <span class="n">boxplot</span><span class="p">,</span> <span class="n">plot</span><span class="p">,</span> <span class="n">setp</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">pylab</span><span class="w"> </span><span class="kn">import</span> <span class="n">boxplot</span><span class="p">,</span> <span class="n">plot</span><span class="p">,</span> <span class="n">setp</span>
|
||||
|
||||
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
|
||||
|
|
@ -288,18 +312,20 @@
|
|||
<span class="n">ax</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">xlabel</span><span class="o">=</span><span class="s1">'prevalence'</span><span class="p">,</span> <span class="n">ylabel</span><span class="o">=</span><span class="s1">'error bias'</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="n">title</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="n">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">)</span></div>
|
||||
<span class="n">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="error_by_drift">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.plot.error_by_drift">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">error_by_drift</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">tr_prevs</span><span class="p">,</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">error_by_drift</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">tr_prevs</span><span class="p">,</span>
|
||||
<span class="n">n_bins</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">error_name</span><span class="o">=</span><span class="s1">'ae'</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>
|
||||
<span class="n">logscale</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
|
||||
<span class="n">title</span><span class="o">=</span><span class="sa">f</span><span class="s1">'Quantification error as a function of distribution shift'</span><span class="p">,</span>
|
||||
<span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
|
||||
<span class="n">vlines</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
|
||||
<span class="n">method_order</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
|
||||
<span class="n">savepath</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
|
|
@ -310,11 +336,17 @@
|
|||
<span class="sd"> fare in different regions of the prior probability shift spectrum (e.g., in the low-shift regime vs. in the</span>
|
||||
<span class="sd"> high-shift regime).</span>
|
||||
|
||||
<span class="sd"> The format convention is as follows: `method_names`, `true_prevs`, and `estim_prevs` are array-like of the same</span>
|
||||
<span class="sd"> length, with the ith element describing the output of an independent experiment. The elements of `true_prevs`, and</span>
|
||||
<span class="sd"> `estim_prevs` are `ndarrays` with coherent shape for the same experiment. Experiments for the same method on</span>
|
||||
<span class="sd"> different datasets can be used, in which case the method name can appear more than once in `method_names`.</span>
|
||||
|
||||
<span class="sd"> :param method_names: array-like with the method names for each experiment</span>
|
||||
<span class="sd"> :param true_prevs: array-like with the true prevalence values (each being a ndarray with n_classes components) for</span>
|
||||
<span class="sd"> each experiment</span>
|
||||
<span class="sd"> :param estim_prevs: array-like with the estimated prevalence values (each being a ndarray with n_classes components)</span>
|
||||
<span class="sd"> for each experiment</span>
|
||||
<span class="sd"> :param true_prevs: array-like with the true prevalence values for each experiment. Each entry is a ndarray of</span>
|
||||
<span class="sd"> shape `(n_samples, n_classes)` components.</span>
|
||||
<span class="sd"> :param estim_prevs: array-like with the estimated prevalence values for each experiment. Each entry is a ndarray of</span>
|
||||
<span class="sd"> shape `(n_samples, n_classes)` components and `n_samples` must coincide with the corresponding entry in</span>
|
||||
<span class="sd"> `true_prevs`.</span>
|
||||
<span class="sd"> :param tr_prevs: training prevalence of each experiment</span>
|
||||
<span class="sd"> :param n_bins: number of bins in which the y-axis is to be divided (default is 20)</span>
|
||||
<span class="sd"> :param error_name: a string representing the name of an error function (as defined in `quapy.error`, default is "ae")</span>
|
||||
|
|
@ -322,12 +354,13 @@
|
|||
<span class="sd"> :param show_density: whether or not to display the distribution of experiments for each bin (default is True)</span>
|
||||
<span class="sd"> :param show_density: whether or not to display the legend of the chart (default is True)</span>
|
||||
<span class="sd"> :param logscale: whether or not to log-scale the y-error measure (default is False)</span>
|
||||
<span class="sd"> :param title: title of the plot (default is "Quantification error as a function of distribution shift")</span>
|
||||
<span class="sd"> :param title: title of the plot (default is None)</span>
|
||||
<span class="sd"> :param vlines: array-like list of values (default is None). If indicated, highlights some regions of the space</span>
|
||||
<span class="sd"> using vertical dotted lines.</span>
|
||||
<span class="sd"> :param method_order: if indicated (default is None), imposes the order in which the methods are processed (i.e.,</span>
|
||||
<span class="sd"> listed in the legend and associated with matplotlib colors).</span>
|
||||
<span class="sd"> :param savepath: path where to save the plot. If not indicated (as default), the plot is shown.</span>
|
||||
<span class="sd"> :return: returns (fig, ax) matplotlib objects for eventual customisation</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
|
||||
|
|
@ -336,14 +369,14 @@
|
|||
<span class="n">x_error</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">ae</span>
|
||||
<span class="n">y_error</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="p">,</span> <span class="n">error_name</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">method_order</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">method_order</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="c1"># get all data as a dictionary {'m':{'x':ndarray, 'y':ndarray}} where 'm' is a method name (in the same</span>
|
||||
<span class="c1"># order as in method_order (if specified), and where 'x' are the train-test shifts (computed as according to</span>
|
||||
<span class="c1"># x_error function) and 'y' is the estim-test shift (computed as according to y_error)</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">_join_data_by_drift</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">tr_prevs</span><span class="p">,</span> <span class="n">x_error</span><span class="p">,</span> <span class="n">y_error</span><span class="p">,</span> <span class="n">method_order</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">method_order</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">method_order</span> <span class="o">=</span> <span class="n">method_names</span>
|
||||
|
||||
<span class="n">_set_colors</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">n_methods</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">method_order</span><span class="p">))</span>
|
||||
|
||||
<span class="n">bins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">n_bins</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
|
||||
|
|
@ -396,11 +429,11 @@
|
|||
<span class="n">ax2</span><span class="o">.</span><span class="n">spines</span><span class="p">[</span><span class="s1">'right'</span><span class="p">]</span><span class="o">.</span><span class="n">set_color</span><span class="p">(</span><span class="s1">'g'</span><span class="p">)</span>
|
||||
<span class="n">ax2</span><span class="o">.</span><span class="n">tick_params</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="s1">'y'</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="s1">'g'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">xlabel</span><span class="o">=</span><span class="sa">f</span><span class="s1">'Distribution shift between training set and test sample'</span><span class="p">,</span>
|
||||
<span class="n">ylabel</span><span class="o">=</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">error_name</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span><span class="si">}</span><span class="s1"> (true distribution, predicted distribution)'</span><span class="p">,</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">xlabel</span><span class="o">=</span><span class="sa">f</span><span class="s1">'Prior shift between training set and test sample'</span><span class="p">,</span>
|
||||
<span class="n">ylabel</span><span class="o">=</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">error_name</span><span class="o">.</span><span class="n">upper</span><span class="p">()</span><span class="si">}</span><span class="s1"> (true prev, predicted prev)'</span><span class="p">,</span>
|
||||
<span class="n">title</span><span class="o">=</span><span class="n">title</span><span class="p">)</span>
|
||||
<span class="n">box</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_position</span><span class="p">()</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_position</span><span class="p">([</span><span class="n">box</span><span class="o">.</span><span class="n">x0</span><span class="p">,</span> <span class="n">box</span><span class="o">.</span><span class="n">y0</span><span class="p">,</span> <span class="n">box</span><span class="o">.</span><span class="n">width</span> <span class="o">*</span> <span class="mf">0.8</span><span class="p">,</span> <span class="n">box</span><span class="o">.</span><span class="n">height</span><span class="p">])</span>
|
||||
<span class="c1"># box = ax.get_position()</span>
|
||||
<span class="c1"># ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])</span>
|
||||
<span class="k">if</span> <span class="n">vlines</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">vline</span> <span class="ow">in</span> <span class="n">vlines</span><span class="p">:</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">vline</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'k'</span><span class="p">)</span>
|
||||
|
|
@ -410,19 +443,20 @@
|
|||
<span class="c1">#nice scale for the logaritmic axis</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">10</span> <span class="o">**</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="n">max_y</span><span class="p">)))</span>
|
||||
|
||||
|
||||
<span class="k">if</span> <span class="n">show_legend</span><span class="p">:</span>
|
||||
<span class="n">fig</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'lower center'</span><span class="p">,</span>
|
||||
<span class="n">fig</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'center left'</span><span class="p">,</span>
|
||||
<span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">),</span>
|
||||
<span class="n">ncol</span><span class="o">=</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">method_names</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">//</span><span class="mi">2</span><span class="p">)</span>
|
||||
|
||||
<span class="n">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">)</span></div>
|
||||
<span class="n">ncol</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="n">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="brokenbar_supremacy_by_drift">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.plot.brokenbar_supremacy_by_drift">[docs]</a>
|
||||
<span class="k">def</span> <span class="nf">brokenbar_supremacy_by_drift</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">tr_prevs</span><span class="p">,</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">brokenbar_supremacy_by_drift</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">tr_prevs</span><span class="p">,</span>
|
||||
<span class="n">n_bins</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">binning</span><span class="o">=</span><span class="s1">'isomerous'</span><span class="p">,</span>
|
||||
<span class="n">x_error</span><span class="o">=</span><span class="s1">'ae'</span><span class="p">,</span> <span class="n">y_error</span><span class="o">=</span><span class="s1">'ae'</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>
|
||||
|
|
@ -436,11 +470,17 @@
|
|||
<span class="sd"> plot is displayed on top, that displays the distribution of experiments for each bin (when binning="isometric") or</span>
|
||||
<span class="sd"> the percentiles points of the distribution (when binning="isomerous").</span>
|
||||
|
||||
<span class="sd"> The format convention is as follows: `method_names`, `true_prevs`, and `estim_prevs` are array-like of the same</span>
|
||||
<span class="sd"> length, with the ith element describing the output of an independent experiment. The elements of `true_prevs`, and</span>
|
||||
<span class="sd"> `estim_prevs` are `ndarrays` with coherent shape for the same experiment. Experiments for the same method on</span>
|
||||
<span class="sd"> different datasets can be used, in which case the method name can appear more than once in `method_names`.</span>
|
||||
|
||||
<span class="sd"> :param method_names: array-like with the method names for each experiment</span>
|
||||
<span class="sd"> :param true_prevs: array-like with the true prevalence values (each being a ndarray with n_classes components) for</span>
|
||||
<span class="sd"> each experiment</span>
|
||||
<span class="sd"> :param estim_prevs: array-like with the estimated prevalence values (each being a ndarray with n_classes components)</span>
|
||||
<span class="sd"> for each experiment</span>
|
||||
<span class="sd"> :param true_prevs: array-like with the true prevalence values for each experiment. Each entry is a ndarray of</span>
|
||||
<span class="sd"> shape `(n_samples, n_classes)` components.</span>
|
||||
<span class="sd"> :param estim_prevs: array-like with the estimated prevalence values for each experiment. Each entry is a ndarray of</span>
|
||||
<span class="sd"> shape `(n_samples, n_classes)` components and `n_samples` must coincide with the corresponding entry in</span>
|
||||
<span class="sd"> `true_prevs`.</span>
|
||||
<span class="sd"> :param tr_prevs: training prevalence of each experiment</span>
|
||||
<span class="sd"> :param n_bins: number of bins in which the y-axis is to be divided (default is 20)</span>
|
||||
<span class="sd"> :param binning: type of binning, either "isomerous" (default) or "isometric"</span>
|
||||
|
|
@ -457,13 +497,16 @@
|
|||
<span class="sd"> :param method_order: if indicated (default is None), imposes the order in which the methods are processed (i.e.,</span>
|
||||
<span class="sd"> listed in the legend and associated with matplotlib colors).</span>
|
||||
<span class="sd"> :param savepath: path where to save the plot. If not indicated (as default), the plot is shown.</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> :return: returns (fig, ax) matplotlib objects for eventual customisation</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">assert</span> <span class="n">binning</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'isomerous'</span><span class="p">,</span> <span class="s1">'isometric'</span><span class="p">],</span> <span class="s1">'unknown binning type; valid types are "isomerous" and "isometric"'</span>
|
||||
|
||||
<span class="n">x_error</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="p">,</span> <span class="n">x_error</span><span class="p">)</span>
|
||||
<span class="n">y_error</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="p">,</span> <span class="n">y_error</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">method_order</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">method_order</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="c1"># get all data as a dictionary {'m':{'x':ndarray, 'y':ndarray}} where 'm' is a method name (in the same</span>
|
||||
<span class="c1"># order as in method_order (if specified), and where 'x' are the train-test shifts (computed as according to</span>
|
||||
<span class="c1"># x_error function) and 'y' is the estim-test shift (computed as according to y_error)</span>
|
||||
|
|
@ -602,11 +645,13 @@
|
|||
<span class="n">ax</span><span class="o">.</span><span class="n">get_xaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">wspace</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">hspace</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
|
||||
<span class="n">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">)</span></div>
|
||||
<span class="n">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span></div>
|
||||
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_merge</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_merge</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">):</span>
|
||||
<span class="n">ndims</span> <span class="o">=</span> <span class="n">true_prevs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="n">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">'true'</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">'estim'</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>
|
||||
|
|
@ -620,13 +665,14 @@
|
|||
<span class="k">return</span> <span class="n">method_order</span><span class="p">,</span> <span class="n">true_prevs_</span><span class="p">,</span> <span class="n">estim_prevs_</span>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_set_colors</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">n_methods</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_set_colors</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">n_methods</span><span class="p">):</span>
|
||||
<span class="n">NUM_COLORS</span> <span class="o">=</span> <span class="n">n_methods</span>
|
||||
<span class="n">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">'tab20'</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">if</span> <span class="n">NUM_COLORS</span><span class="o">></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">'tab20'</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_prop_cycle</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="p">[</span><span class="n">cm</span><span class="p">(</span><span class="mf">1.</span> <span class="o">*</span> <span class="n">i</span> <span class="o">/</span> <span class="n">NUM_COLORS</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">NUM_COLORS</span><span class="p">)])</span>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_save_or_show</span><span class="p">(</span><span class="n">savepath</span><span class="p">):</span>
|
||||
<span class="c1"># if savepath is specified, then saves the plot in that path; otherwise the plot is shown</span>
|
||||
<span class="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>
|
||||
|
|
@ -636,7 +682,7 @@
|
|||
<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="nf">_join_data_by_drift</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">tr_prevs</span><span class="p">,</span> <span class="n">x_error</span><span class="p">,</span> <span class="n">y_error</span><span class="p">,</span> <span class="n">method_order</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_join_data_by_drift</span><span class="p">(</span><span class="n">method_names</span><span class="p">,</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">tr_prevs</span><span class="p">,</span> <span class="n">x_error</span><span class="p">,</span> <span class="n">y_error</span><span class="p">,</span> <span class="n">method_order</span><span class="p">):</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="p">{</span><span class="s1">'x'</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">'y'</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>
|
||||
|
|
@ -655,6 +701,45 @@
|
|||
<span class="n">method_order</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">method</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">data</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="calibration_plot">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.plot.calibration_plot">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">calibration_plot</span><span class="p">(</span><span class="n">prob_classifier</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">savepath</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="n">posteriors</span> <span class="o">=</span> <span class="n">prob_classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
|
||||
<span class="k">assert</span> <span class="n">posteriors</span><span class="o">.</span><span class="n">ndim</span><span class="o">==</span><span class="mi">2</span><span class="p">,</span> <span class="s1">'calibration plot only works for binary problems'</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">>=</span><span class="mf">0.5</span>
|
||||
<span class="n">bins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">nbins</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">binned_values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">digitize</span><span class="p">(</span><span class="n">posteriors</span><span class="p">,</span> <span class="n">bins</span><span class="p">,</span> <span class="n">right</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">binned_values</span><span class="p">))</span>
|
||||
<span class="n">correct</span> <span class="o">=</span> <span class="n">pred_y</span> <span class="o">==</span> <span class="n">y</span>
|
||||
<span class="n">bin_centers</span> <span class="o">=</span> <span class="p">(</span><span class="n">bins</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">bins</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span> <span class="o">/</span> <span class="mi">2</span>
|
||||
<span class="n">bins_names</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">nbins</span><span class="p">)</span>
|
||||
<span class="n">y_axis</span> <span class="o">=</span> <span class="p">[</span><span class="n">correct</span><span class="p">[</span><span class="n">binned_values</span><span class="o">==</span><span class="nb">bin</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span> <span class="k">for</span> <span class="nb">bin</span> <span class="ow">in</span> <span class="n">bins_names</span><span class="p">]</span>
|
||||
<span class="n">y_axis</span> <span class="o">=</span> <span class="p">[</span><span class="n">v</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">else</span> <span class="mi">0</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">y_axis</span><span class="p">]</span>
|
||||
<span class="c1"># Crear el gráfico de barras</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">bin_centers</span><span class="p">,</span> <span class="n">y_axis</span><span class="p">,</span> <span class="n">width</span><span class="o">=</span><span class="n">bins</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">bins</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'black'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># Etiquetas y título</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"Bin"</span><span class="p">)</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"Value"</span><span class="p">)</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Bar plot of calculated values per bin"</span><span class="p">)</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">bin_centers</span><span class="p">,</span> <span class="p">[</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">b</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span> <span class="k">for</span> <span class="n">b</span> <span class="ow">in</span> <span class="n">bin_centers</span><span class="p">],</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># Mostrar el gráfico</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span></div>
|
||||
|
||||
|
||||
<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">'__main__'</span><span class="p">:</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LogisticRegression</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_UCIBinaryDataset</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">UCI_BINARY_DATASETS</span><span class="p">[</span><span class="mi">6</span><span class="p">])</span>
|
||||
<span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">train_test</span>
|
||||
<span class="n">classifier</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">()</span>
|
||||
<span class="n">classifier</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="o">*</span><span class="n">train</span><span class="o">.</span><span class="n">Xy</span><span class="p">)</span>
|
||||
<span class="n">calibration_plot</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="o">*</span><span class="n">test</span><span class="o">.</span><span class="n">Xy</span><span class="p">)</span>
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
|
|
|
|||
|
|
@ -1,23 +1,20 @@
|
|||
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en">
|
||||
<html class="writer-html5" lang="en" data-content_root="../../">
|
||||
<head>
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>quapy.protocol — QuaPy: A Python-based open-source framework for quantification 0.1.8 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="../../_static/pygments.css" />
|
||||
<link rel="stylesheet" type="text/css" href="../../_static/css/theme.css" />
|
||||
<title>quapy.protocol — QuaPy: A Python-based open-source framework for quantification 0.2.1 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=b86133f3" />
|
||||
<link rel="stylesheet" type="text/css" href="../../_static/css/theme.css?v=9edc463e" />
|
||||
|
||||
|
||||
<!--[if lt IE 9]>
|
||||
<script src="../../_static/js/html5shiv.min.js"></script>
|
||||
<![endif]-->
|
||||
|
||||
<script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js"></script>
|
||||
<script src="../../_static/jquery.js"></script>
|
||||
<script src="../../_static/underscore.js"></script>
|
||||
<script src="../../_static/_sphinx_javascript_frameworks_compat.js"></script>
|
||||
<script src="../../_static/doctools.js"></script>
|
||||
<script src="../../_static/sphinx_highlight.js"></script>
|
||||
<script src="../../_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<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/js/theme.js"></script>
|
||||
<link rel="index" title="Index" href="../../genindex.html" />
|
||||
<link rel="search" title="Search" href="../../search.html" />
|
||||
|
|
@ -43,7 +40,13 @@
|
|||
</div>
|
||||
</div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../modules.html">quapy</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../index.html">Quickstart</a></li>
|
||||
</ul>
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../manuals.html">Manuals</a></li>
|
||||
</ul>
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../quapy.html">API</a></li>
|
||||
</ul>
|
||||
|
||||
</div>
|
||||
|
|
@ -71,25 +74,31 @@
|
|||
<div itemprop="articleBody">
|
||||
|
||||
<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">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></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>
|
||||
|
||||
<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>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="AbstractProtocol"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractProtocol">[docs]</a><span class="k">class</span> <span class="nc">AbstractProtocol</span><span class="p">(</span><span class="n">metaclass</span><span class="o">=</span><span class="n">ABCMeta</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="AbstractProtocol">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractProtocol">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">AbstractProtocol</span><span class="p">(</span><span class="n">metaclass</span><span class="o">=</span><span class="n">ABCMeta</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Abstract parent class for sample generation protocols.</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="nd">@abstractmethod</span>
|
||||
<span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Implements the protocol. Yields one sample at a time along with its prevalence</span>
|
||||
|
||||
|
|
@ -98,25 +107,31 @@
|
|||
<span class="sd"> """</span>
|
||||
<span class="o">...</span>
|
||||
|
||||
<div class="viewcode-block" id="AbstractProtocol.total"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractProtocol.total">[docs]</a> <span class="k">def</span> <span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="AbstractProtocol.total">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractProtocol.total">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Indicates the total number of samples that the protocol generates.</span>
|
||||
|
||||
<span class="sd"> :return: The number of samples to generate if known, or `None` otherwise.</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="kc">None</span></div></div>
|
||||
<span class="k">return</span> <span class="kc">None</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="IterateProtocol"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.IterateProtocol">[docs]</a><span class="k">class</span> <span class="nc">IterateProtocol</span><span class="p">(</span><span class="n">AbstractProtocol</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="IterateProtocol">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.IterateProtocol">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">IterateProtocol</span><span class="p">(</span><span class="n">AbstractProtocol</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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"> """</span>
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">samples</span><span class="p">:</span> <span class="p">[</span><span class="n">LabelledCollection</span><span class="p">]):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">samples</span><span class="p">:</span> <span class="p">[</span><span class="n">LabelledCollection</span><span class="p">]):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">samples</span> <span class="o">=</span> <span class="n">samples</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Yields one sample from the initial list at a time</span>
|
||||
|
||||
|
|
@ -126,16 +141,58 @@
|
|||
<span class="k">for</span> <span class="n">sample</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">samples</span><span class="p">:</span>
|
||||
<span class="k">yield</span> <span class="n">sample</span><span class="o">.</span><span class="n">Xp</span>
|
||||
|
||||
<div class="viewcode-block" id="IterateProtocol.total"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.IterateProtocol.total">[docs]</a> <span class="k">def</span> <span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="IterateProtocol.total">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.IterateProtocol.total">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns the number of samples in this protocol</span>
|
||||
|
||||
<span class="sd"> :return: int</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">samples</span><span class="p">)</span></div></div>
|
||||
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">samples</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="AbstractStochasticSeededProtocol"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractStochasticSeededProtocol">[docs]</a><span class="k">class</span> <span class="nc">AbstractStochasticSeededProtocol</span><span class="p">(</span><span class="n">AbstractProtocol</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="ProtocolFromIndex">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.ProtocolFromIndex">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">ProtocolFromIndex</span><span class="p">(</span><span class="n">AbstractProtocol</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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"> """</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">indexes</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">indexes</span> <span class="o">=</span> <span class="n">indexes</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Yields one sample at a time extracted using the indexes</span>
|
||||
|
||||
<span class="sd"> :return: yields a tuple `(sample, prev) at a time, where `sample` is a set of instances</span>
|
||||
<span class="sd"> and in which `prev` is an `nd.array` with the class prevalence values</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">for</span> <span class="n">index</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexes</span><span class="p">:</span>
|
||||
<span class="k">yield</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">index</span><span class="p">)</span><span class="o">.</span><span class="n">Xp</span>
|
||||
|
||||
<div class="viewcode-block" id="ProtocolFromIndex.total">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.ProtocolFromIndex.total">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns the number of samples in this protocol</span>
|
||||
|
||||
<span class="sd"> :return: int</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">indexes</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="AbstractStochasticSeededProtocol">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractStochasticSeededProtocol">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">AbstractStochasticSeededProtocol</span><span class="p">(</span><span class="n">AbstractProtocol</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -152,19 +209,21 @@
|
|||
|
||||
<span class="n">_random_state</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span> <span class="c1"># means "not set"</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">random_state</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">random_state</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_random_state</span>
|
||||
|
||||
<span class="nd">@random_state</span><span class="o">.</span><span class="n">setter</span>
|
||||
<span class="k">def</span> <span class="nf">random_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">random_state</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">random_state</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">random_state</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_random_state</span> <span class="o">=</span> <span class="n">random_state</span>
|
||||
|
||||
<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="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</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="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> This function has to return all the necessary parameters to replicate the samples</span>
|
||||
|
||||
|
|
@ -172,8 +231,11 @@
|
|||
<span class="sd"> """</span>
|
||||
<span class="o">...</span></div>
|
||||
|
||||
<div class="viewcode-block" id="AbstractStochasticSeededProtocol.sample"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.sample">[docs]</a> <span class="nd">@abstractmethod</span>
|
||||
<span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="AbstractStochasticSeededProtocol.sample">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.sample">[docs]</a>
|
||||
<span class="nd">@abstractmethod</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Extract one sample determined by the given parameters</span>
|
||||
|
||||
|
|
@ -182,7 +244,8 @@
|
|||
<span class="sd"> """</span>
|
||||
<span class="o">...</span></div>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -197,9 +260,11 @@
|
|||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">stack</span><span class="o">.</span><span class="n">enter_context</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">))</span>
|
||||
<span class="k">for</span> <span class="n">params</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">samples_parameters</span><span class="p">():</span>
|
||||
<span class="k">yield</span> <span class="bp">self</span><span class="o">.</span><span class="n">collator</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">params</span><span class="p">))</span>
|
||||
<span class="k">yield</span> <span class="bp">self</span><span class="o">.</span><span class="n">collator</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">params</span><span class="p">),</span> <span class="n">params</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="AbstractStochasticSeededProtocol.collator"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.collator">[docs]</a> <span class="k">def</span> <span class="nf">collator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="AbstractStochasticSeededProtocol.collator">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.collator">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">collator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -209,17 +274,23 @@
|
|||
<span class="sd"> :param args: additional arguments</span>
|
||||
<span class="sd"> :return: the sample adhering to a desired output format (in this case, the sample is returned as it is)</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="n">sample</span></div></div>
|
||||
<span class="k">return</span> <span class="n">sample</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="OnLabelledCollectionProtocol"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.OnLabelledCollectionProtocol">[docs]</a><span class="k">class</span> <span class="nc">OnLabelledCollectionProtocol</span><span class="p">:</span>
|
||||
|
||||
<div class="viewcode-block" id="OnLabelledCollectionProtocol">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.OnLabelledCollectionProtocol">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">OnLabelledCollectionProtocol</span><span class="p">(</span><span class="n">AbstractStochasticSeededProtocol</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Protocols that generate samples from a :class:`qp.data.LabelledCollection` object.</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">RETURN_TYPES</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'sample_prev'</span><span class="p">,</span> <span class="s1">'labelled_collection'</span><span class="p">,</span> <span class="s1">'index'</span><span class="p">]</span>
|
||||
|
||||
<div class="viewcode-block" id="OnLabelledCollectionProtocol.get_labelled_collection"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.OnLabelledCollectionProtocol.get_labelled_collection">[docs]</a> <span class="k">def</span> <span class="nf">get_labelled_collection</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="OnLabelledCollectionProtocol.get_labelled_collection">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.OnLabelledCollectionProtocol.get_labelled_collection">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">get_labelled_collection</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns the labelled collection on which this protocol acts.</span>
|
||||
|
||||
|
|
@ -227,7 +298,10 @@
|
|||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span></div>
|
||||
|
||||
<div class="viewcode-block" id="OnLabelledCollectionProtocol.on_preclassified_instances"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.OnLabelledCollectionProtocol.on_preclassified_instances">[docs]</a> <span class="k">def</span> <span class="nf">on_preclassified_instances</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pre_classifications</span><span class="p">,</span> <span class="n">in_place</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="OnLabelledCollectionProtocol.on_preclassified_instances">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.OnLabelledCollectionProtocol.on_preclassified_instances">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">on_preclassified_instances</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pre_classifications</span><span class="p">,</span> <span class="n">in_place</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -250,8 +324,11 @@
|
|||
<span class="n">new</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">new</span><span class="o">.</span><span class="n">on_preclassified_instances</span><span class="p">(</span><span class="n">pre_classifications</span><span class="p">,</span> <span class="n">in_place</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="OnLabelledCollectionProtocol.get_collator"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.OnLabelledCollectionProtocol.get_collator">[docs]</a> <span class="nd">@classmethod</span>
|
||||
<span class="k">def</span> <span class="nf">get_collator</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">return_type</span><span class="o">=</span><span class="s1">'sample_prev'</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="OnLabelledCollectionProtocol.get_collator">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.OnLabelledCollectionProtocol.get_collator">[docs]</a>
|
||||
<span class="nd">@classmethod</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">get_collator</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">return_type</span><span class="o">=</span><span class="s1">'sample_prev'</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns a collator function, i.e., a function that prepares the yielded data</span>
|
||||
|
||||
|
|
@ -264,12 +341,30 @@
|
|||
<span class="k">assert</span> <span class="n">return_type</span> <span class="ow">in</span> <span class="bp">cls</span><span class="o">.</span><span class="n">RETURN_TYPES</span><span class="p">,</span> \
|
||||
<span class="sa">f</span><span class="s1">'unknown return type passed as argument; valid ones are </span><span class="si">{</span><span class="bp">cls</span><span class="o">.</span><span class="n">RETURN_TYPES</span><span class="si">}</span><span class="s1">'</span>
|
||||
<span class="k">if</span> <span class="n">return_type</span><span class="o">==</span><span class="s1">'sample_prev'</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="k">lambda</span> <span class="n">lc</span><span class="p">:</span><span class="n">lc</span><span class="o">.</span><span class="n">Xp</span>
|
||||
<span class="k">return</span> <span class="k">lambda</span> <span class="n">lc</span><span class="p">,</span><span class="n">params</span><span class="p">:</span><span class="n">lc</span><span class="o">.</span><span class="n">Xp</span>
|
||||
<span class="k">elif</span> <span class="n">return_type</span><span class="o">==</span><span class="s1">'labelled_collection'</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="k">lambda</span> <span class="n">lc</span><span class="p">:</span><span class="n">lc</span></div></div>
|
||||
<span class="k">return</span> <span class="k">lambda</span> <span class="n">lc</span><span class="p">,</span><span class="n">params</span><span class="p">:</span><span class="n">lc</span>
|
||||
<span class="k">elif</span> <span class="n">return_type</span><span class="o">==</span><span class="s1">'index'</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="k">lambda</span> <span class="n">lc</span><span class="p">,</span><span class="n">params</span><span class="p">:</span><span class="n">params</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="APP"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.APP">[docs]</a><span class="k">class</span> <span class="nc">APP</span><span class="p">(</span><span class="n">AbstractStochasticSeededProtocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="OnLabelledCollectionProtocol.sample">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.OnLabelledCollectionProtocol.sample">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Realizes the sample given the index of the instances.</span>
|
||||
|
||||
<span class="sd"> :param index: indexes of the instances to select</span>
|
||||
<span class="sd"> :return: an instance of :class:`qp.data.LabelledCollection`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">index</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="APP">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.APP">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">APP</span><span class="p">(</span><span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -293,7 +388,7 @@
|
|||
<span class="sd"> to "labelled_collection" to get instead instances of LabelledCollection</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_prevalences</span><span class="o">=</span><span class="mi">21</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_prevalences</span><span class="o">=</span><span class="mi">21</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
|
||||
<span class="n">smooth_limits_epsilon</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">sanity_check</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">return_type</span><span class="o">=</span><span class="s1">'sample_prev'</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>
|
||||
|
|
@ -313,7 +408,9 @@
|
|||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">collator</span> <span class="o">=</span> <span class="n">OnLabelledCollectionProtocol</span><span class="o">.</span><span class="n">get_collator</span><span class="p">(</span><span class="n">return_type</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="APP.prevalence_grid"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.APP.prevalence_grid">[docs]</a> <span class="k">def</span> <span class="nf">prevalence_grid</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="APP.prevalence_grid">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.APP.prevalence_grid">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">prevalence_grid</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -338,7 +435,10 @@
|
|||
<span class="n">prevs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">repeats</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">prevs</span></div>
|
||||
|
||||
<div class="viewcode-block" id="APP.samples_parameters"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.APP.samples_parameters">[docs]</a> <span class="k">def</span> <span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="APP.samples_parameters">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.APP.samples_parameters">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Return all the necessary parameters to replicate the samples as according to the APP protocol.</span>
|
||||
|
||||
|
|
@ -350,25 +450,23 @@
|
|||
<span class="n">indexes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">index</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">indexes</span></div>
|
||||
|
||||
<div class="viewcode-block" id="APP.sample"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.APP.sample">[docs]</a> <span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Realizes the sample given the index of the instances.</span>
|
||||
|
||||
<span class="sd"> :param index: indexes of the instances to select</span>
|
||||
<span class="sd"> :return: an instance of :class:`qp.data.LabelledCollection`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">index</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="APP.total"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.APP.total">[docs]</a> <span class="k">def</span> <span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<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="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns the number of samples that will be generated</span>
|
||||
|
||||
<span class="sd"> :return: int</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">num_prevalence_combinations</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_prevalences</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">n_classes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">repeats</span><span class="p">)</span></div></div>
|
||||
<span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">num_prevalence_combinations</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_prevalences</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">n_classes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">repeats</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="NPP"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.NPP">[docs]</a><span class="k">class</span> <span class="nc">NPP</span><span class="p">(</span><span class="n">AbstractStochasticSeededProtocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="NPP">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.NPP">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">NPP</span><span class="p">(</span><span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -383,7 +481,7 @@
|
|||
<span class="sd"> to "labelled_collection" to get instead instances of LabelledCollection</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span><span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span><span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
|
||||
<span class="n">return_type</span><span class="o">=</span><span class="s1">'sample_prev'</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>
|
||||
|
|
@ -392,7 +490,9 @@
|
|||
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">collator</span> <span class="o">=</span> <span class="n">OnLabelledCollectionProtocol</span><span class="o">.</span><span class="n">get_collator</span><span class="p">(</span><span class="n">return_type</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="NPP.samples_parameters"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.NPP.samples_parameters">[docs]</a> <span class="k">def</span> <span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="NPP.samples_parameters">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.NPP.samples_parameters">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Return all the necessary parameters to replicate the samples as according to the NPP protocol.</span>
|
||||
|
||||
|
|
@ -404,25 +504,23 @@
|
|||
<span class="n">indexes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">index</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">indexes</span></div>
|
||||
|
||||
<div class="viewcode-block" id="NPP.sample"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.NPP.sample">[docs]</a> <span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Realizes the sample given the index of the instances.</span>
|
||||
|
||||
<span class="sd"> :param index: indexes of the instances to select</span>
|
||||
<span class="sd"> :return: an instance of :class:`qp.data.LabelledCollection`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">index</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="NPP.total"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.NPP.total">[docs]</a> <span class="k">def</span> <span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<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="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns the number of samples that will be generated (equals to "repeats")</span>
|
||||
|
||||
<span class="sd"> :return: int</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">repeats</span></div></div>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">repeats</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="UPP"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.UPP">[docs]</a><span class="k">class</span> <span class="nc">UPP</span><span class="p">(</span><span class="n">AbstractStochasticSeededProtocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="UPP">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.UPP">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">UPP</span><span class="p">(</span><span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -441,7 +539,7 @@
|
|||
<span class="sd"> to "labelled_collection" to get instead instances of LabelledCollection</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
|
||||
<span class="n">return_type</span><span class="o">=</span><span class="s1">'sample_prev'</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>
|
||||
|
|
@ -450,7 +548,9 @@
|
|||
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">collator</span> <span class="o">=</span> <span class="n">OnLabelledCollectionProtocol</span><span class="o">.</span><span class="n">get_collator</span><span class="p">(</span><span class="n">return_type</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="UPP.samples_parameters"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.UPP.samples_parameters">[docs]</a> <span class="k">def</span> <span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="UPP.samples_parameters">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.UPP.samples_parameters">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Return all the necessary parameters to replicate the samples as according to the UPP protocol.</span>
|
||||
|
||||
|
|
@ -462,25 +562,96 @@
|
|||
<span class="n">indexes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">index</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">indexes</span></div>
|
||||
|
||||
<div class="viewcode-block" id="UPP.sample"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.UPP.sample">[docs]</a> <span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Realizes the sample given the index of the instances.</span>
|
||||
|
||||
<span class="sd"> :param index: indexes of the instances to select</span>
|
||||
<span class="sd"> :return: an instance of :class:`qp.data.LabelledCollection`</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">index</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="UPP.total"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.UPP.total">[docs]</a> <span class="k">def</span> <span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<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="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns the number of samples that will be generated (equals to "repeats")</span>
|
||||
|
||||
<span class="sd"> :return: int</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">repeats</span></div></div>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">repeats</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="DomainMixer"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.DomainMixer">[docs]</a><span class="k">class</span> <span class="nc">DomainMixer</span><span class="p">(</span><span class="n">AbstractStochasticSeededProtocol</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="DirichletProtocol">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.DirichletProtocol">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">DirichletProtocol</span><span class="p">(</span><span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> A protocol that establishes a prior Dirichlet distribution for the prevalence of the samples.</span>
|
||||
<span class="sd"> Note that providing an all-ones vector of Dirichlet parameters is equivalent to invoking the</span>
|
||||
<span class="sd"> APP protocol (although each protocol will generate a different series of samples given a</span>
|
||||
<span class="sd"> fixed seed, since the implementation is different).</span>
|
||||
|
||||
<span class="sd"> :param data: a `LabelledCollection` from which the samples will be drawn</span>
|
||||
<span class="sd"> :param alpha: an array-like of shape (n_classes,) with the parameters of the Dirichlet distribution</span>
|
||||
<span class="sd"> :param sample_size: integer, the number of instances in each sample; if None (default) then it is taken from</span>
|
||||
<span class="sd"> qp.environ["SAMPLE_SIZE"]. If this is not set, a ValueError exception is raised.</span>
|
||||
<span class="sd"> :param repeats: the number of samples to generate. Default is 100.</span>
|
||||
<span class="sd"> :param random_state: allows replicating samples across runs (default 0, meaning that the sequence of samples</span>
|
||||
<span class="sd"> will be the same every time the protocol is called)</span>
|
||||
<span class="sd"> :param return_type: set to "sample_prev" (default) to get the pairs of (sample, prevalence) at each iteration, or</span>
|
||||
<span class="sd"> to "labelled_collection" to get instead instances of LabelledCollection</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">alpha</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
|
||||
<span class="n">return_type</span><span class="o">=</span><span class="s1">'sample_prev'</span><span class="p">):</span>
|
||||
<span class="c1">#assert ((isinstance(alpha, str) and alpha == 'uniform') 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'wrong type for {alpha=}; expected "uniform", a real scalar, or an array-like of real values'</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">'uniform'</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">n_classes</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">alpha</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">n_classes</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="n">alpha</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">alpha</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">1</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span> <span class="o">!=</span> <span class="n">n_classes</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
|
||||
<span class="sa">f</span><span class="s1">'wrong shape for alpha; expected </span><span class="si">{</span><span class="n">n_classes</span><span class="si">}</span><span class="s1"> values, found shape </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s1">'</span>
|
||||
<span class="p">)</span>
|
||||
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">DirichletProtocol</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">random_state</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span>
|
||||
<span class="c1">#self.alpha = alpha</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">sample_size</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_sample_size</span><span class="p">(</span><span class="n">sample_size</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">repeats</span> <span class="o">=</span> <span class="n">repeats</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">collator</span> <span class="o">=</span> <span class="n">OnLabelledCollectionProtocol</span><span class="o">.</span><span class="n">get_collator</span><span class="p">(</span><span class="n">return_type</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="DirichletProtocol.samples_parameters">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.DirichletProtocol.samples_parameters">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Return all the necessary parameters to replicate the samples.</span>
|
||||
|
||||
<span class="sd"> :return: a list of indexes that realize the sampling</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">prevs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">dirichlet</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">repeats</span><span class="p">)</span>
|
||||
<span class="n">indexes</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">sampling_index</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sample_size</span><span class="p">,</span> <span class="o">*</span><span class="n">prevs_i</span><span class="p">)</span> <span class="k">for</span> <span class="n">prevs_i</span> <span class="ow">in</span> <span class="n">prevs</span><span class="p">]</span>
|
||||
<span class="k">return</span> <span class="n">indexes</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="DirichletProtocol.total">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.DirichletProtocol.total">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns the number of samples that will be generated (equals to "repeats")</span>
|
||||
|
||||
<span class="sd"> :return: int</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">repeats</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="DomainMixer">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.DomainMixer">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">DomainMixer</span><span class="p">(</span><span class="n">AbstractStochasticSeededProtocol</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Generates mixtures of two domains (A and B) at controlled rates, but preserving the original class prevalence.</span>
|
||||
|
||||
|
|
@ -489,7 +660,7 @@
|
|||
<span class="sd"> :param sample_size: integer, the number of instances in each sample; if None (default) then it is taken from</span>
|
||||
<span class="sd"> qp.environ["SAMPLE_SIZE"]. If this is not set, a ValueError exception is raised.</span>
|
||||
<span class="sd"> :param repeats: int, number of samples to draw for every mixture rate</span>
|
||||
<span class="sd"> :param prevalence: the prevalence to preserv along the mixtures. If specified, should be an array containing</span>
|
||||
<span class="sd"> :param prevalence: the prevalence to preserve along the mixtures. If specified, should be an array containing</span>
|
||||
<span class="sd"> one prevalence value (positive float) for each class and summing up to one. If not specified, the prevalence</span>
|
||||
<span class="sd"> will be taken from the domain A (default).</span>
|
||||
<span class="sd"> :param mixture_points: an integer indicating the number of points to take from a linear scale (e.g., 21 will</span>
|
||||
|
|
@ -499,7 +670,7 @@
|
|||
<span class="sd"> will be the same every time the protocol is called)</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span>
|
||||
<span class="bp">self</span><span class="p">,</span>
|
||||
<span class="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>
|
||||
|
|
@ -531,7 +702,9 @@
|
|||
<span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">collator</span> <span class="o">=</span> <span class="n">OnLabelledCollectionProtocol</span><span class="o">.</span><span class="n">get_collator</span><span class="p">(</span><span class="n">return_type</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="DomainMixer.samples_parameters"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.DomainMixer.samples_parameters">[docs]</a> <span class="k">def</span> <span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="DomainMixer.samples_parameters">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.DomainMixer.samples_parameters">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Return all the necessary parameters to replicate the samples as according to the this protocol.</span>
|
||||
|
||||
|
|
@ -548,7 +721,10 @@
|
|||
<span class="n">indexesB</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sampleBidx</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">indexesA</span><span class="p">,</span> <span class="n">indexesB</span><span class="p">))</span></div>
|
||||
|
||||
<div class="viewcode-block" id="DomainMixer.sample"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.DomainMixer.sample">[docs]</a> <span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indexes</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="DomainMixer.sample">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.DomainMixer.sample">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indexes</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Realizes the sample given a pair of indexes of the instances from A and B.</span>
|
||||
|
||||
|
|
@ -560,13 +736,18 @@
|
|||
<span class="n">sampleB</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">B</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">indexesB</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">sampleA</span><span class="o">+</span><span class="n">sampleB</span></div>
|
||||
|
||||
<div class="viewcode-block" id="DomainMixer.total"><a class="viewcode-back" href="../../quapy.html#quapy.protocol.DomainMixer.total">[docs]</a> <span class="k">def</span> <span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="DomainMixer.total">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.protocol.DomainMixer.total">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Returns the number of samples that will be generated (equals to "repeats * mixture_points")</span>
|
||||
|
||||
<span class="sd"> :return: int</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">repeats</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mixture_points</span><span class="p">)</span></div></div>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">repeats</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mixture_points</span><span class="p">)</span></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<span class="c1"># aliases</span>
|
||||
|
|
|
|||
|
|
@ -1,23 +1,20 @@
|
|||
|
||||
|
||||
<!DOCTYPE html>
|
||||
<html class="writer-html5" lang="en">
|
||||
<html class="writer-html5" lang="en" data-content_root="../../">
|
||||
<head>
|
||||
<meta charset="utf-8" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<title>quapy.util — QuaPy: A Python-based open-source framework for quantification 0.1.8 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="../../_static/pygments.css" />
|
||||
<link rel="stylesheet" type="text/css" href="../../_static/css/theme.css" />
|
||||
<title>quapy.util — QuaPy: A Python-based open-source framework for quantification 0.2.1 documentation</title>
|
||||
<link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=b86133f3" />
|
||||
<link rel="stylesheet" type="text/css" href="../../_static/css/theme.css?v=9edc463e" />
|
||||
|
||||
|
||||
<!--[if lt IE 9]>
|
||||
<script src="../../_static/js/html5shiv.min.js"></script>
|
||||
<![endif]-->
|
||||
|
||||
<script data-url_root="../../" id="documentation_options" src="../../_static/documentation_options.js"></script>
|
||||
<script src="../../_static/jquery.js"></script>
|
||||
<script src="../../_static/underscore.js"></script>
|
||||
<script src="../../_static/_sphinx_javascript_frameworks_compat.js"></script>
|
||||
<script src="../../_static/doctools.js"></script>
|
||||
<script src="../../_static/sphinx_highlight.js"></script>
|
||||
<script src="../../_static/jquery.js?v=5d32c60e"></script>
|
||||
<script src="../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
|
||||
<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/js/theme.js"></script>
|
||||
<link rel="index" title="Index" href="../../genindex.html" />
|
||||
<link rel="search" title="Search" href="../../search.html" />
|
||||
|
|
@ -43,7 +40,13 @@
|
|||
</div>
|
||||
</div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../modules.html">quapy</a></li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../index.html">Quickstart</a></li>
|
||||
</ul>
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../manuals.html">Manuals</a></li>
|
||||
</ul>
|
||||
<ul>
|
||||
<li class="toctree-l1"><a class="reference internal" href="../../quapy.html">API</a></li>
|
||||
</ul>
|
||||
|
||||
</div>
|
||||
|
|
@ -71,23 +74,26 @@
|
|||
<div itemprop="articleBody">
|
||||
|
||||
<h1>Source code for quapy.util</h1><div class="highlight"><pre>
|
||||
<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="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
|
||||
<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 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="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="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">from</span><span class="w"> </span><span class="nn">joblib</span><span class="w"> </span><span class="kn">import</span> <span class="n">Parallel</span><span class="p">,</span> <span class="n">delayed</span>
|
||||
<span class="kn">from</span><span class="w"> </span><span class="nn">time</span><span class="w"> </span><span class="kn">import</span> <span class="n">time</span>
|
||||
<span class="kn">import</span><span class="w"> </span><span class="nn">signal</span>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_get_parallel_slices</span><span class="p">(</span><span class="n">n_tasks</span><span class="p">,</span> <span class="n">n_jobs</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_get_parallel_slices</span><span class="p">(</span><span class="n">n_tasks</span><span class="p">,</span> <span class="n">n_jobs</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">n_jobs</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
|
||||
<span class="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>
|
||||
|
|
@ -95,7 +101,9 @@
|
|||
<span class="k">return</span> <span class="p">[</span><span class="nb">slice</span><span class="p">(</span><span class="n">job</span> <span class="o">*</span> <span class="n">batch</span><span class="p">,</span> <span class="p">(</span><span class="n">job</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">batch</span> <span class="o">+</span> <span class="p">(</span><span class="n">remainder</span> <span class="k">if</span> <span class="n">job</span> <span class="o">==</span> <span class="n">n_jobs</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">else</span> <span class="mi">0</span><span class="p">))</span> <span class="k">for</span> <span class="n">job</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)]</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="map_parallel"><a class="viewcode-back" href="../../quapy.html#quapy.util.map_parallel">[docs]</a><span class="k">def</span> <span class="nf">map_parallel</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">n_jobs</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="map_parallel">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.util.map_parallel">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">map_parallel</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">n_jobs</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -113,7 +121,10 @@
|
|||
<span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">chain</span><span class="o">.</span><span class="n">from_iterable</span><span class="p">(</span><span class="n">results</span><span class="p">))</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="parallel"><a class="viewcode-back" href="../../quapy.html#quapy.util.parallel">[docs]</a><span class="k">def</span> <span class="nf">parallel</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">n_jobs</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">asarray</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s1">'loky'</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="parallel">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.util.parallel">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">parallel</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">n_jobs</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">asarray</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s1">'loky'</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> A wrapper of multiprocessing:</span>
|
||||
|
||||
|
|
@ -129,8 +140,9 @@
|
|||
<span class="sd"> :param seed: the numeric seed</span>
|
||||
<span class="sd"> :param asarray: set to True to return a np.ndarray instead of a list</span>
|
||||
<span class="sd"> :param backend: indicates the backend used for handling parallel works</span>
|
||||
<span class="sd"> :param open_args: if True, then the delayed function is called on *args_i, instead of on args_i</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">def</span> <span class="nf">func_dec</span><span class="p">(</span><span class="n">environ</span><span class="p">,</span> <span class="n">seed</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">func_dec</span><span class="p">(</span><span class="n">environ</span><span class="p">,</span> <span class="n">seed</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span>
|
||||
<span class="n">qp</span><span class="o">.</span><span class="n">environ</span> <span class="o">=</span> <span class="n">environ</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
|
||||
<span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'N_JOBS'</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>
|
||||
|
|
@ -147,8 +159,48 @@
|
|||
<span class="k">return</span> <span class="n">out</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="temp_seed"><a class="viewcode-back" href="../../quapy.html#quapy.util.temp_seed">[docs]</a><span class="nd">@contextlib</span><span class="o">.</span><span class="n">contextmanager</span>
|
||||
<span class="k">def</span> <span class="nf">temp_seed</span><span class="p">(</span><span class="n">random_state</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="parallel_unpack">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.util.parallel_unpack">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">parallel_unpack</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">n_jobs</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">asarray</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s1">'loky'</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> A wrapper of multiprocessing:</span>
|
||||
|
||||
<span class="sd"> >>> Parallel(n_jobs=n_jobs)(</span>
|
||||
<span class="sd"> >>> delayed(func)(*args_i) for args_i in args</span>
|
||||
<span class="sd"> >>> )</span>
|
||||
|
||||
<span class="sd"> that takes the `quapy.environ` variable as input silently.</span>
|
||||
<span class="sd"> Seeds the child processes to ensure reproducibility when n_jobs>1.</span>
|
||||
|
||||
<span class="sd"> :param func: callable</span>
|
||||
<span class="sd"> :param args: args of func</span>
|
||||
<span class="sd"> :param seed: the numeric seed</span>
|
||||
<span class="sd"> :param asarray: set to True to return a np.ndarray instead of a list</span>
|
||||
<span class="sd"> :param backend: indicates the backend used for handling parallel works</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">func_dec</span><span class="p">(</span><span class="n">environ</span><span class="p">,</span> <span class="n">seed</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span>
|
||||
<span class="n">qp</span><span class="o">.</span><span class="n">environ</span> <span class="o">=</span> <span class="n">environ</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
|
||||
<span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'N_JOBS'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="c1"># set a context with a temporal seed to ensure results are reproducibles in parallel</span>
|
||||
<span class="k">with</span> <span class="n">ExitStack</span><span class="p">()</span> <span class="k">as</span> <span class="n">stack</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">seed</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">stack</span><span class="o">.</span><span class="n">enter_context</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">))</span>
|
||||
<span class="k">return</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span>
|
||||
|
||||
<span class="n">out</span> <span class="o">=</span> <span class="n">Parallel</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="n">backend</span><span class="p">)(</span>
|
||||
<span class="n">delayed</span><span class="p">(</span><span class="n">func_dec</span><span class="p">)(</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">,</span> <span class="kc">None</span> <span class="k">if</span> <span class="n">seed</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">seed</span> <span class="o">+</span> <span class="n">i</span><span class="p">,</span> <span class="o">*</span><span class="n">args_i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">args_i</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">args</span><span class="p">)</span>
|
||||
<span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">asarray</span><span class="p">:</span>
|
||||
<span class="n">out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">out</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="temp_seed">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.util.temp_seed">[docs]</a>
|
||||
<span class="nd">@contextlib</span><span class="o">.</span><span class="n">contextmanager</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">temp_seed</span><span class="p">(</span><span class="n">random_state</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Can be used in a "with" context to set a temporal seed without modifying the outer numpy's current state. E.g.:</span>
|
||||
|
||||
|
|
@ -169,14 +221,17 @@
|
|||
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">set_state</span><span class="p">(</span><span class="n">state</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="download_file"><a class="viewcode-back" href="../../quapy.html#quapy.util.download_file">[docs]</a><span class="k">def</span> <span class="nf">download_file</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">archive_filename</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="download_file">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.util.download_file">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">download_file</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">archive_filename</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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"> """</span>
|
||||
<span class="k">def</span> <span class="nf">progress</span><span class="p">(</span><span class="n">blocknum</span><span class="p">,</span> <span class="n">bs</span><span class="p">,</span> <span class="n">size</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">progress</span><span class="p">(</span><span class="n">blocknum</span><span class="p">,</span> <span class="n">bs</span><span class="p">,</span> <span class="n">size</span><span class="p">):</span>
|
||||
<span class="n">total_sz_mb</span> <span class="o">=</span> <span class="s1">'</span><span class="si">%.2f</span><span class="s1"> MB'</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">'</span><span class="si">%.2f</span><span class="s1"> MB'</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">'</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">'</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">''</span><span class="p">)</span>
|
||||
|
|
@ -185,7 +240,10 @@
|
|||
<span class="nb">print</span><span class="p">(</span><span class="s2">""</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="download_file_if_not_exists"><a class="viewcode-back" href="../../quapy.html#quapy.util.download_file_if_not_exists">[docs]</a><span class="k">def</span> <span class="nf">download_file_if_not_exists</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">archive_filename</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="download_file_if_not_exists">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.util.download_file_if_not_exists">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">download_file_if_not_exists</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">archive_filename</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Dowloads a function (using :meth:`download_file`) if the file does not exist.</span>
|
||||
|
||||
|
|
@ -198,7 +256,10 @@
|
|||
<span class="n">download_file</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">archive_filename</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="create_if_not_exist"><a class="viewcode-back" href="../../quapy.html#quapy.util.create_if_not_exist">[docs]</a><span class="k">def</span> <span class="nf">create_if_not_exist</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="create_if_not_exist">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.util.create_if_not_exist">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">create_if_not_exist</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> An alias to `os.makedirs(path, exist_ok=True)` that also returns the path. This is useful in cases like, e.g.:</span>
|
||||
|
||||
|
|
@ -211,7 +272,10 @@
|
|||
<span class="k">return</span> <span class="n">path</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="get_quapy_home"><a class="viewcode-back" href="../../quapy.html#quapy.util.get_quapy_home">[docs]</a><span class="k">def</span> <span class="nf">get_quapy_home</span><span class="p">():</span>
|
||||
|
||||
<div class="viewcode-block" id="get_quapy_home">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.util.get_quapy_home">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">get_quapy_home</span><span class="p">():</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -223,7 +287,10 @@
|
|||
<span class="k">return</span> <span class="n">home</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="create_parent_dir"><a class="viewcode-back" href="../../quapy.html#quapy.util.create_parent_dir">[docs]</a><span class="k">def</span> <span class="nf">create_parent_dir</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="create_parent_dir">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.util.create_parent_dir">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">create_parent_dir</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -235,7 +302,10 @@
|
|||
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">parentdir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="save_text_file"><a class="viewcode-back" href="../../quapy.html#quapy.util.save_text_file">[docs]</a><span class="k">def</span> <span class="nf">save_text_file</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">text</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="save_text_file">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.util.save_text_file">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">save_text_file</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">text</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Saves a text file to disk, given its full path, and creates the parent directory if missing.</span>
|
||||
|
||||
|
|
@ -243,11 +313,14 @@
|
|||
<span class="sd"> :param text: text to save.</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">create_parent_dir</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
|
||||
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">text</span><span class="p">,</span> <span class="s1">'wt'</span><span class="p">)</span> <span class="k">as</span> <span class="n">fout</span><span class="p">:</span>
|
||||
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">'wt'</span><span class="p">)</span> <span class="k">as</span> <span class="n">fout</span><span class="p">:</span>
|
||||
<span class="n">fout</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">text</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="pickled_resource"><a class="viewcode-back" href="../../quapy.html#quapy.util.pickled_resource">[docs]</a><span class="k">def</span> <span class="nf">pickled_resource</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">:</span><span class="nb">str</span><span class="p">,</span> <span class="n">generation_func</span><span class="p">:</span><span class="n">callable</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="pickled_resource">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.util.pickled_resource">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">pickled_resource</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">:</span><span class="nb">str</span><span class="p">,</span> <span class="n">generation_func</span><span class="p">:</span><span class="nb">callable</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -266,15 +339,18 @@
|
|||
<span class="k">return</span> <span class="n">generation_func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">))</span>
|
||||
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">fin</span><span class="p">:</span>
|
||||
<span class="n">instance</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fin</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">instance</span> <span class="o">=</span> <span class="n">generation_func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span>
|
||||
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">Path</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">)</span><span class="o">.</span><span class="n">parent</span><span class="p">),</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">instance</span><span class="p">,</span> <span class="nb">open</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">,</span> <span class="s1">'wb'</span><span class="p">),</span> <span class="n">pickle</span><span class="o">.</span><span class="n">HIGHEST_PROTOCOL</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">instance</span></div>
|
||||
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">,</span> <span class="s1">'wb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">foo</span><span class="p">:</span>
|
||||
<span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">instance</span><span class="p">,</span> <span class="n">foo</span><span class="p">,</span> <span class="n">pickle</span><span class="o">.</span><span class="n">HIGHEST_PROTOCOL</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">instance</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_check_sample_size</span><span class="p">(</span><span class="n">sample_size</span><span class="p">):</span>
|
||||
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">_check_sample_size</span><span class="p">(</span><span class="n">sample_size</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">sample_size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="k">assert</span> <span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'SAMPLE_SIZE'</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">'error: sample_size set to None, and cannot be resolved from the environment'</span>
|
||||
|
|
@ -284,7 +360,34 @@
|
|||
<span class="k">return</span> <span class="n">sample_size</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="EarlyStop"><a class="viewcode-back" href="../../quapy.html#quapy.util.EarlyStop">[docs]</a><span class="k">class</span> <span class="nc">EarlyStop</span><span class="p">:</span>
|
||||
<div class="viewcode-block" id="load_report">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.util.load_report">[docs]</a>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">load_report</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">as_dict</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">str2prev_arr</span><span class="p">(</span><span class="n">strprev</span><span class="p">):</span>
|
||||
<span class="n">within</span> <span class="o">=</span> <span class="n">strprev</span><span class="o">.</span><span class="n">strip</span><span class="p">(</span><span class="s1">'[]'</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
|
||||
<span class="n">float_list</span> <span class="o">=</span> <span class="p">[</span><span class="nb">float</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">within</span><span class="p">]</span>
|
||||
<span class="n">float_list</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.</span> <span class="o">-</span> <span class="nb">sum</span><span class="p">(</span><span class="n">float_list</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">float_list</span><span class="p">)</span>
|
||||
|
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">df</span><span class="p">[</span><span class="s1">'true-prev'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'true-prev'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">str2prev_arr</span><span class="p">)</span>
|
||||
<span class="n">df</span><span class="p">[</span><span class="s1">'estim-prev'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'estim-prev'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">str2prev_arr</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">as_dict</span><span class="p">:</span>
|
||||
<span class="n">d</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="p">:</span>
|
||||
<span class="n">vals</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="k">if</span> <span class="n">col</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">'true-prev'</span><span class="p">,</span> <span class="s1">'estim-prev'</span><span class="p">]:</span>
|
||||
<span class="n">vals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span><span class="n">vals</span><span class="p">)</span>
|
||||
<span class="n">d</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">vals</span>
|
||||
<span class="k">return</span> <span class="n">d</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">df</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="EarlyStop">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.util.EarlyStop">[docs]</a>
|
||||
<span class="k">class</span><span class="w"> </span><span class="nc">EarlyStop</span><span class="p">:</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> A class implementing the early-stopping condition typically used for training neural networks.</span>
|
||||
|
||||
|
|
@ -309,7 +412,7 @@
|
|||
<span class="sd"> :ivar IMPROVED: flag (boolean) indicating whether there was an improvement in the last call</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">patience</span><span class="p">,</span> <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">patience</span><span class="p">,</span> <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">PATIENCE_LIMIT</span> <span class="o">=</span> <span class="n">patience</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">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"><</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">></span><span class="n">b</span>
|
||||
|
|
@ -319,7 +422,7 @@
|
|||
<span class="bp">self</span><span class="o">.</span><span class="n">STOP</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">IMPROVED</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
|
||||
<span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">watch_score</span><span class="p">,</span> <span class="n">epoch</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">watch_score</span><span class="p">,</span> <span class="n">epoch</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</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>
|
||||
|
|
@ -339,8 +442,11 @@
|
|||
<span class="bp">self</span><span class="o">.</span><span class="n">STOP</span> <span class="o">=</span> <span class="kc">True</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="timeout"><a class="viewcode-back" href="../../quapy.html#quapy.util.timeout">[docs]</a><span class="nd">@contextlib</span><span class="o">.</span><span class="n">contextmanager</span>
|
||||
<span class="k">def</span> <span class="nf">timeout</span><span class="p">(</span><span class="n">seconds</span><span class="p">):</span>
|
||||
|
||||
<div class="viewcode-block" id="timeout">
|
||||
<a class="viewcode-back" href="../../quapy.html#quapy.util.timeout">[docs]</a>
|
||||
<span class="nd">@contextlib</span><span class="o">.</span><span class="n">contextmanager</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">timeout</span><span class="p">(</span><span class="n">seconds</span><span class="p">):</span>
|
||||
<span class="w"> </span><span class="sd">"""</span>
|
||||
<span class="sd"> Opens a context that will launch an exception if not closed after a given number of seconds</span>
|
||||
|
||||
|
|
@ -359,7 +465,7 @@
|
|||
<span class="sd"> :param seconds: number of seconds, set to <=0 to ignore the timer</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="n">seconds</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="k">def</span> <span class="nf">handler</span><span class="p">(</span><span class="n">signum</span><span class="p">,</span> <span class="n">frame</span><span class="p">):</span>
|
||||
<span class="k">def</span><span class="w"> </span><span class="nf">handler</span><span class="p">(</span><span class="n">signum</span><span class="p">,</span> <span class="n">frame</span><span class="p">):</span>
|
||||
<span class="k">raise</span> <span class="ne">TimeoutError</span><span class="p">()</span>
|
||||
|
||||
<span class="n">signal</span><span class="o">.</span><span class="n">signal</span><span class="p">(</span><span class="n">signal</span><span class="o">.</span><span class="n">SIGALRM</span><span class="p">,</span> <span class="n">handler</span><span class="p">)</span>
|
||||
|
|
@ -370,6 +476,7 @@
|
|||
<span class="k">if</span> <span class="n">seconds</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">signal</span><span class="o">.</span><span class="n">alarm</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
|
|
|
|||
|
|
@ -1,20 +1,9 @@
|
|||
/*
|
||||
* _sphinx_javascript_frameworks_compat.js
|
||||
* ~~~~~~~~~~
|
||||
*
|
||||
* Compatability shim for jQuery and underscores.js.
|
||||
*
|
||||
* WILL BE REMOVED IN Sphinx 6.0
|
||||
* xref RemovedInSphinx60Warning
|
||||
/* Compatability shim for jQuery and underscores.js.
|
||||
*
|
||||
* Copyright Sphinx contributors
|
||||
* Released under the two clause BSD licence
|
||||
*/
|
||||
|
||||
/**
|
||||
* select a different prefix for underscore
|
||||
*/
|
||||
$u = _.noConflict();
|
||||
|
||||
|
||||
/**
|
||||
* small helper function to urldecode strings
|
||||
*
|
||||
|
|
|
|||
|
|
@ -1 +1 @@
|
|||
.clearfix{*zoom:1}.clearfix:after,.clearfix:before{display:table;content:""}.clearfix:after{clear:both}@font-face{font-family:FontAwesome;font-style:normal;font-weight:400;src:url(fonts/fontawesome-webfont.eot?674f50d287a8c48dc19ba404d20fe713?#iefix) format("embedded-opentype"),url(fonts/fontawesome-webfont.woff2?af7ae505a9eed503f8b8e6982036873e) format("woff2"),url(fonts/fontawesome-webfont.woff?fee66e712a8a08eef5805a46892932ad) format("woff"),url(fonts/fontawesome-webfont.ttf?b06871f281fee6b241d60582ae9369b9) format("truetype"),url(fonts/fontawesome-webfont.svg?912ec66d7572ff821749319396470bde#FontAwesome) format("svg")}.fa:before{font-family:FontAwesome;font-style:normal;font-weight:400;line-height:1}.fa:before,a .fa{text-decoration:inherit}.fa:before,a .fa,li .fa{display:inline-block}li .fa-large:before{width:1.875em}ul.fas{list-style-type:none;margin-left:2em;text-indent:-.8em}ul.fas li .fa{width:.8em}ul.fas li .fa-large:before{vertical-align:baseline}.fa-book:before,.icon-book:before{content:"\f02d"}.fa-caret-down:before,.icon-caret-down:before{content:"\f0d7"}.fa-caret-up:before,.icon-caret-up:before{content:"\f0d8"}.fa-caret-left:before,.icon-caret-left:before{content:"\f0d9"}.fa-caret-right:before,.icon-caret-right:before{content:"\f0da"}.rst-versions{position:fixed;bottom:0;left:0;width:300px;color:#fcfcfc;background:#1f1d1d;font-family:Lato,proxima-nova,Helvetica Neue,Arial,sans-serif;z-index:400}.rst-versions a{color:#2980b9;text-decoration:none}.rst-versions .rst-badge-small{display:none}.rst-versions .rst-current-version{padding:12px;background-color:#272525;display:block;text-align:right;font-size:90%;cursor:pointer;color:#27ae60}.rst-versions .rst-current-version:after{clear:both;content:"";display:block}.rst-versions .rst-current-version .fa{color:#fcfcfc}.rst-versions .rst-current-version .fa-book,.rst-versions .rst-current-version .icon-book{float:left}.rst-versions .rst-current-version.rst-out-of-date{background-color:#e74c3c;color:#fff}.rst-versions .rst-current-version.rst-active-old-version{background-color:#f1c40f;color:#000}.rst-versions.shift-up{height:auto;max-height:100%;overflow-y:scroll}.rst-versions.shift-up .rst-other-versions{display:block}.rst-versions .rst-other-versions{font-size:90%;padding:12px;color:grey;display:none}.rst-versions .rst-other-versions hr{display:block;height:1px;border:0;margin:20px 0;padding:0;border-top:1px solid #413d3d}.rst-versions .rst-other-versions dd{display:inline-block;margin:0}.rst-versions .rst-other-versions dd a{display:inline-block;padding:6px;color:#fcfcfc}.rst-versions.rst-badge{width:auto;bottom:20px;right:20px;left:auto;border:none;max-width:300px;max-height:90%}.rst-versions.rst-badge .fa-book,.rst-versions.rst-badge .icon-book{float:none;line-height:30px}.rst-versions.rst-badge.shift-up .rst-current-version{text-align:right}.rst-versions.rst-badge.shift-up .rst-current-version .fa-book,.rst-versions.rst-badge.shift-up .rst-current-version .icon-book{float:left}.rst-versions.rst-badge>.rst-current-version{width:auto;height:30px;line-height:30px;padding:0 6px;display:block;text-align:center}@media screen and (max-width:768px){.rst-versions{width:85%;display:none}.rst-versions.shift{display:block}}
|
||||
.clearfix{*zoom:1}.clearfix:after,.clearfix:before{display:table;content:""}.clearfix:after{clear:both}@font-face{font-family:FontAwesome;font-style:normal;font-weight:400;src:url(fonts/fontawesome-webfont.eot?674f50d287a8c48dc19ba404d20fe713?#iefix) format("embedded-opentype"),url(fonts/fontawesome-webfont.woff2?af7ae505a9eed503f8b8e6982036873e) format("woff2"),url(fonts/fontawesome-webfont.woff?fee66e712a8a08eef5805a46892932ad) format("woff"),url(fonts/fontawesome-webfont.ttf?b06871f281fee6b241d60582ae9369b9) format("truetype"),url(fonts/fontawesome-webfont.svg?912ec66d7572ff821749319396470bde#FontAwesome) format("svg")}.fa:before{font-family:FontAwesome;font-style:normal;font-weight:400;line-height:1}.fa:before,a .fa{text-decoration:inherit}.fa:before,a .fa,li .fa{display:inline-block}li .fa-large:before{width:1.875em}ul.fas{list-style-type:none;margin-left:2em;text-indent:-.8em}ul.fas li .fa{width:.8em}ul.fas li .fa-large:before{vertical-align:baseline}.fa-book:before,.icon-book:before{content:"\f02d"}.fa-caret-down:before,.icon-caret-down:before{content:"\f0d7"}.fa-caret-up:before,.icon-caret-up:before{content:"\f0d8"}.fa-caret-left:before,.icon-caret-left:before{content:"\f0d9"}.fa-caret-right:before,.icon-caret-right:before{content:"\f0da"}.rst-versions{position:fixed;bottom:0;left:0;width:300px;color:#fcfcfc;background:#1f1d1d;font-family:Lato,proxima-nova,Helvetica Neue,Arial,sans-serif;z-index:400}.rst-versions a{color:#2980b9;text-decoration:none}.rst-versions .rst-badge-small{display:none}.rst-versions .rst-current-version{padding:12px;background-color:#272525;display:block;text-align:right;font-size:90%;cursor:pointer;color:#27ae60}.rst-versions .rst-current-version:after{clear:both;content:"";display:block}.rst-versions .rst-current-version .fa{color:#fcfcfc}.rst-versions .rst-current-version .fa-book,.rst-versions .rst-current-version .icon-book{float:left}.rst-versions .rst-current-version.rst-out-of-date{background-color:#e74c3c;color:#fff}.rst-versions .rst-current-version.rst-active-old-version{background-color:#f1c40f;color:#000}.rst-versions.shift-up{height:auto;max-height:100%;overflow-y:scroll}.rst-versions.shift-up .rst-other-versions{display:block}.rst-versions .rst-other-versions{font-size:90%;padding:12px;color:grey;display:none}.rst-versions .rst-other-versions hr{display:block;height:1px;border:0;margin:20px 0;padding:0;border-top:1px solid #413d3d}.rst-versions .rst-other-versions dd{display:inline-block;margin:0}.rst-versions .rst-other-versions dd a{display:inline-block;padding:6px;color:#fcfcfc}.rst-versions .rst-other-versions .rtd-current-item{font-weight:700}.rst-versions.rst-badge{width:auto;bottom:20px;right:20px;left:auto;border:none;max-width:300px;max-height:90%}.rst-versions.rst-badge .fa-book,.rst-versions.rst-badge .icon-book{float:none;line-height:30px}.rst-versions.rst-badge.shift-up .rst-current-version{text-align:right}.rst-versions.rst-badge.shift-up .rst-current-version .fa-book,.rst-versions.rst-badge.shift-up .rst-current-version .icon-book{float:left}.rst-versions.rst-badge>.rst-current-version{width:auto;height:30px;line-height:30px;padding:0 6px;display:block;text-align:center}@media screen and (max-width:768px){.rst-versions{width:85%;display:none}.rst-versions.shift{display:block}}#flyout-search-form{padding:6px}
|
||||
File diff suppressed because one or more lines are too long
|
|
@ -1,7 +1,7 @@
|
|||
/* Highlighting utilities for Sphinx HTML documentation. */
|
||||
"use strict";
|
||||
|
||||
const SPHINX_HIGHLIGHT_ENABLED = true
|
||||
const SPHINX_HIGHLIGHT_ENABLED = true;
|
||||
|
||||
/**
|
||||
* highlight a given string on a node by wrapping it in
|
||||
|
|
@ -13,9 +13,9 @@ const _highlight = (node, addItems, text, className) => {
|
|||
const parent = node.parentNode;
|
||||
const pos = val.toLowerCase().indexOf(text);
|
||||
if (
|
||||
pos >= 0 &&
|
||||
!parent.classList.contains(className) &&
|
||||
!parent.classList.contains("nohighlight")
|
||||
pos >= 0
|
||||
&& !parent.classList.contains(className)
|
||||
&& !parent.classList.contains("nohighlight")
|
||||
) {
|
||||
let span;
|
||||
|
||||
|
|
@ -29,19 +29,18 @@ const _highlight = (node, addItems, text, className) => {
|
|||
}
|
||||
|
||||
span.appendChild(document.createTextNode(val.substr(pos, text.length)));
|
||||
parent.insertBefore(
|
||||
span,
|
||||
parent.insertBefore(
|
||||
document.createTextNode(val.substr(pos + text.length)),
|
||||
node.nextSibling
|
||||
)
|
||||
);
|
||||
const rest = document.createTextNode(val.substr(pos + text.length));
|
||||
parent.insertBefore(span, parent.insertBefore(rest, node.nextSibling));
|
||||
node.nodeValue = val.substr(0, pos);
|
||||
/* There may be more occurrences of search term in this node. So call this
|
||||
* function recursively on the remaining fragment.
|
||||
*/
|
||||
_highlight(rest, addItems, text, className);
|
||||
|
||||
if (isInSVG) {
|
||||
const rect = document.createElementNS(
|
||||
"http://www.w3.org/2000/svg",
|
||||
"rect"
|
||||
"rect",
|
||||
);
|
||||
const bbox = parent.getBBox();
|
||||
rect.x.baseVal.value = bbox.x;
|
||||
|
|
@ -60,7 +59,7 @@ const _highlightText = (thisNode, text, className) => {
|
|||
let addItems = [];
|
||||
_highlight(thisNode, addItems, text, className);
|
||||
addItems.forEach((obj) =>
|
||||
obj.parent.insertAdjacentElement("beforebegin", obj.target)
|
||||
obj.parent.insertAdjacentElement("beforebegin", obj.target),
|
||||
);
|
||||
};
|
||||
|
||||
|
|
@ -68,25 +67,31 @@ const _highlightText = (thisNode, text, className) => {
|
|||
* Small JavaScript module for the documentation.
|
||||
*/
|
||||
const SphinxHighlight = {
|
||||
|
||||
/**
|
||||
* highlight the search words provided in localstorage in the text
|
||||
*/
|
||||
highlightSearchWords: () => {
|
||||
if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight
|
||||
if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight
|
||||
|
||||
// get and clear terms from localstorage
|
||||
const url = new URL(window.location);
|
||||
const highlight =
|
||||
localStorage.getItem("sphinx_highlight_terms")
|
||||
|| url.searchParams.get("highlight")
|
||||
|| "";
|
||||
localStorage.removeItem("sphinx_highlight_terms")
|
||||
url.searchParams.delete("highlight");
|
||||
window.history.replaceState({}, "", url);
|
||||
localStorage.getItem("sphinx_highlight_terms")
|
||||
|| url.searchParams.get("highlight")
|
||||
|| "";
|
||||
localStorage.removeItem("sphinx_highlight_terms");
|
||||
// Update history only if '?highlight' is present; otherwise it
|
||||
// clears text fragments (not set in window.location by the browser)
|
||||
if (url.searchParams.has("highlight")) {
|
||||
url.searchParams.delete("highlight");
|
||||
window.history.replaceState({}, "", url);
|
||||
}
|
||||
|
||||
// get individual terms from highlight string
|
||||
const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
|
||||
const terms = highlight
|
||||
.toLowerCase()
|
||||
.split(/\s+/)
|
||||
.filter((x) => x);
|
||||
if (terms.length === 0) return; // nothing to do
|
||||
|
||||
// There should never be more than one element matching "div.body"
|
||||
|
|
@ -102,11 +107,11 @@ const SphinxHighlight = {
|
|||
document
|
||||
.createRange()
|
||||
.createContextualFragment(
|
||||
'<p class="highlight-link">' +
|
||||
'<a href="javascript:SphinxHighlight.hideSearchWords()">' +
|
||||
_("Hide Search Matches") +
|
||||
"</a></p>"
|
||||
)
|
||||
'<p class="highlight-link">'
|
||||
+ '<a href="javascript:SphinxHighlight.hideSearchWords()">'
|
||||
+ _("Hide Search Matches")
|
||||
+ "</a></p>",
|
||||
),
|
||||
);
|
||||
},
|
||||
|
||||
|
|
@ -120,7 +125,7 @@ const SphinxHighlight = {
|
|||
document
|
||||
.querySelectorAll("span.highlighted")
|
||||
.forEach((el) => el.classList.remove("highlighted"));
|
||||
localStorage.removeItem("sphinx_highlight_terms")
|
||||
localStorage.removeItem("sphinx_highlight_terms");
|
||||
},
|
||||
|
||||
initEscapeListener: () => {
|
||||
|
|
@ -129,10 +134,15 @@ const SphinxHighlight = {
|
|||
|
||||
document.addEventListener("keydown", (event) => {
|
||||
// bail for input elements
|
||||
if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
|
||||
if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName))
|
||||
return;
|
||||
// bail with special keys
|
||||
if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
|
||||
if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
|
||||
if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey)
|
||||
return;
|
||||
if (
|
||||
DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
|
||||
&& event.key === "Escape"
|
||||
) {
|
||||
SphinxHighlight.hideSearchWords();
|
||||
event.preventDefault();
|
||||
}
|
||||
|
|
@ -140,5 +150,10 @@ const SphinxHighlight = {
|
|||
},
|
||||
};
|
||||
|
||||
_ready(SphinxHighlight.highlightSearchWords);
|
||||
_ready(SphinxHighlight.initEscapeListener);
|
||||
_ready(() => {
|
||||
/* Do not call highlightSearchWords() when we are on the search page.
|
||||
* It will highlight words from the *previous* search query.
|
||||
*/
|
||||
if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
|
||||
SphinxHighlight.initEscapeListener();
|
||||
});
|
||||
|
|
|
|||
|
|
@ -61,7 +61,8 @@ exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
|
|||
# -- Options for HTML output -------------------------------------------------
|
||||
# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
|
||||
|
||||
html_theme = 'sphinx_rtd_theme'
|
||||
#html_theme = 'sphinx_rtd_theme'
|
||||
html_theme = 'pydata_sphinx_theme'
|
||||
# html_theme = 'furo'
|
||||
# need to be installed: pip install furo (not working...)
|
||||
# html_static_path = ['_static']
|
||||
|
|
|
|||
|
|
@ -177,6 +177,11 @@ def msre(prevs_true, prevs_hat, prevs_train, eps=0.):
|
|||
def aitchisondist(prevs_true, prevs_hat):
|
||||
"""
|
||||
Computes the Aitchison distance between two prevalence vectors.
|
||||
The Aitchison distance between prevalence vectors :math:`p` and
|
||||
:math:`\\hat{p}` is computed as
|
||||
:math:`d_A(p,\\hat{p})=\\|\\mathrm{clr}(p)-\\mathrm{clr}(\\hat{p})\\|_2`,
|
||||
where :math:`\\mathrm{clr}(p)_i=\\log p_i-\\frac{1}{|\\mathcal{Y}|}
|
||||
\\sum_{j \\in \\mathcal{Y}} \\log p_j`.
|
||||
|
||||
:param prevs_true: array-like with the true prevalence values
|
||||
:param prevs_hat: array-like with the predicted prevalence values
|
||||
|
|
@ -191,7 +196,9 @@ def aitchisondist(prevs_true, prevs_hat):
|
|||
def maitchisondist(prevs_true, prevs_hat):
|
||||
"""
|
||||
Computes the mean Aitchison distance (see :meth:`quapy.error.aitchisondist`)
|
||||
across the sample pairs.
|
||||
across the sample pairs, i.e.,
|
||||
:math:`\\mathrm{mAitchisonDist}=\\frac{1}{n}\\sum_{i=1}^n
|
||||
d_A(p_i,\\hat{p}_i)`.
|
||||
|
||||
:param prevs_true: array-like with the true prevalence values
|
||||
:param prevs_hat: array-like with the predicted prevalence values
|
||||
|
|
|
|||
|
|
@ -430,7 +430,7 @@ def argmin_prevalence(loss: Callable,
|
|||
:param method: string indicating the search strategy. Possible values are::
|
||||
'optim_minimize': uses scipy.optim
|
||||
'linear_search': carries out a linear search for binary problems in the space [0, 0.01, 0.02, ..., 1]
|
||||
'ternary_search': implements the ternary search (not yet implemented)
|
||||
'ternary_search': carries out a ternary search for binary problems in the interval [0,1]
|
||||
:return: np.ndarray, a prevalence vector
|
||||
"""
|
||||
if method == 'optim_minimize':
|
||||
|
|
@ -438,7 +438,7 @@ def argmin_prevalence(loss: Callable,
|
|||
elif method == 'linear_search':
|
||||
return linear_search(loss, n_classes)
|
||||
elif method == 'ternary_search':
|
||||
ternary_search(loss, n_classes)
|
||||
return ternary_search(loss, n_classes)
|
||||
else:
|
||||
raise NotImplementedError()
|
||||
|
||||
|
|
@ -493,7 +493,32 @@ def linear_search(loss: Callable, n_classes: int):
|
|||
|
||||
|
||||
def ternary_search(loss: Callable, n_classes: int):
|
||||
raise NotImplementedError()
|
||||
"""
|
||||
Performs a ternary search for the best prevalence value in binary problems.
|
||||
This search assumes the loss is unimodal over the interval [0,1].
|
||||
|
||||
:param loss: (callable) the function to minimize
|
||||
:param n_classes: (int) the number of classes, i.e., the dimensionality of the prevalence vector
|
||||
:return: (ndarray) the best prevalence vector found
|
||||
"""
|
||||
assert n_classes == 2, 'ternary search is only available for binary problems'
|
||||
|
||||
left, right = 0., 1.
|
||||
tol = 1e-5
|
||||
while abs(right - left) >= tol:
|
||||
left_third = left + (right - left) / 3
|
||||
right_third = right - (right - left) / 3
|
||||
|
||||
left_loss = loss(np.asarray([1 - left_third, left_third]))
|
||||
right_loss = loss(np.asarray([1 - right_third, right_third]))
|
||||
|
||||
if left_loss < right_loss:
|
||||
right = right_third
|
||||
else:
|
||||
left = left_third
|
||||
|
||||
prev = (left + right) / 2
|
||||
return np.asarray([1 - prev, prev])
|
||||
|
||||
|
||||
# ------------------------------------------------------------------------------------------
|
||||
|
|
|
|||
|
|
@ -15,6 +15,7 @@ AGGREGATIVE_METHODS = {
|
|||
aggregative.ACC,
|
||||
aggregative.PCC,
|
||||
aggregative.PACC,
|
||||
aggregative.RLLS,
|
||||
aggregative.EMQ,
|
||||
aggregative.HDy,
|
||||
aggregative.DyS,
|
||||
|
|
@ -52,6 +53,7 @@ MULTICLASS_METHODS = {
|
|||
aggregative.ACC,
|
||||
aggregative.PCC,
|
||||
aggregative.PACC,
|
||||
aggregative.RLLS,
|
||||
aggregative.EMQ,
|
||||
aggregative.KDEyML,
|
||||
aggregative.KDEyCS,
|
||||
|
|
@ -75,4 +77,3 @@ QUANTIFICATION_METHODS = AGGREGATIVE_METHODS | NON_AGGREGATIVE_METHODS | META_ME
|
|||
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -4,6 +4,7 @@ from sklearn.base import BaseEstimator
|
|||
from sklearn.neighbors import KernelDensity
|
||||
|
||||
import quapy as qp
|
||||
from quapy.method._helper import _labels_to_indices
|
||||
from quapy.method.aggregative import AggregativeSoftQuantifier
|
||||
import quapy.functional as F
|
||||
from scipy.special import logsumexp
|
||||
|
|
@ -374,13 +375,11 @@ class KDEyCS(AggregativeSoftQuantifier):
|
|||
|
||||
P, y = classif_predictions, labels
|
||||
n = len(self.classes_)
|
||||
|
||||
assert all(sorted(np.unique(y)) == np.arange(n)), \
|
||||
'label name gaps not allowed in current implementation'
|
||||
y = _labels_to_indices(y, self.classes_)
|
||||
|
||||
# counts_inv keeps track of the relative weight of each datapoint within its class
|
||||
# (i.e., the weight in its KDE model)
|
||||
counts_inv = 1 / (F.counts_from_labels(y, classes=self.classes_))
|
||||
counts_inv = 1 / (F.counts_from_labels(y, classes=np.arange(n)))
|
||||
|
||||
# tr_tr_sums corresponds to symbol \overline{B} in the paper
|
||||
tr_tr_sums = np.zeros(shape=(n,n), dtype=float)
|
||||
|
|
|
|||
|
|
@ -3,7 +3,6 @@ from argparse import ArgumentError
|
|||
from copy import deepcopy
|
||||
from typing import Callable, Literal, Union
|
||||
import numpy as np
|
||||
from numpy.f2py.crackfortran import true_intent_list
|
||||
from sklearn.base import BaseEstimator
|
||||
from sklearn.calibration import CalibratedClassifierCV
|
||||
from sklearn.exceptions import NotFittedError
|
||||
|
|
@ -17,25 +16,17 @@ from quapy.functional import get_divergence
|
|||
from quapy.classification.svmperf import SVMperf
|
||||
from quapy.data import LabelledCollection
|
||||
from quapy.method.base import BaseQuantifier, BinaryQuantifier, OneVsAllGeneric
|
||||
from quapy.method._helper import (
|
||||
_get_abstention_calibrators,
|
||||
_get_cvxpy,
|
||||
_rlls_check_mode,
|
||||
_rlls_joint_distribution,
|
||||
_rlls_predicted_marginal,
|
||||
_rlls_compute_3deltaC,
|
||||
_rlls_compute_weights,
|
||||
_labels_to_indices,
|
||||
)
|
||||
|
||||
# import warnings
|
||||
# from sklearn.exceptions import ConvergenceWarning
|
||||
# warnings.filterwarnings("ignore", category=ConvergenceWarning)
|
||||
|
||||
|
||||
def _get_abstention_calibrators():
|
||||
try:
|
||||
from abstention.calibration import NoBiasVectorScaling, TempScaling, VectorScaling
|
||||
except ImportError as exc:
|
||||
raise ImportError(
|
||||
"Posterior calibration for EMQ requires the optional 'abstention' package."
|
||||
) from exc
|
||||
return {
|
||||
'nbvs': NoBiasVectorScaling(),
|
||||
'bcts': TempScaling(bias_positions='all'),
|
||||
'ts': TempScaling(),
|
||||
'vs': VectorScaling(),
|
||||
}
|
||||
|
||||
|
||||
# Abstract classes
|
||||
|
|
@ -683,6 +674,108 @@ class PACC(AggregativeSoftQuantifier):
|
|||
return confusion.T
|
||||
|
||||
|
||||
class RLLS(AggregativeSoftQuantifier):
|
||||
"""
|
||||
`Regularized Learning for Domain Adaptation under Label Shifts
|
||||
<https://arxiv.org/abs/1903.09734>`_, used here as an aggregative
|
||||
quantifier.
|
||||
|
||||
This implementation ports the regularized weight-estimation component of
|
||||
RLLS to QuaPy's aggregative interface. It estimates label-shift ratios from
|
||||
validation posteriors and source labels, then rescales the source
|
||||
prevalence to obtain target prevalence estimates.
|
||||
|
||||
This method relies on the optional `cvxpy` dependency.
|
||||
|
||||
:param classifier: a scikit-learn's BaseEstimator, or None, in which case
|
||||
the classifier is taken to be the one indicated in
|
||||
`qp.environ['DEFAULT_CLS']`
|
||||
:param fit_classifier: whether to train the learner (default is True). Set
|
||||
to False if the learner has been trained outside the quantifier.
|
||||
:param val_split: specifies the data used for generating classifier
|
||||
predictions. This specification can be made as float in (0, 1)
|
||||
indicating the proportion of stratified held-out validation set to be
|
||||
extracted from the training set; or as an integer (default 5),
|
||||
indicating that the predictions are to be generated in a `k`-fold
|
||||
cross-validation manner; or as a tuple `(X, y)` defining the specific
|
||||
set of data to use for validation. This method requires source
|
||||
predictions and therefore needs `val_split` whenever
|
||||
`fit_classifier=True`.
|
||||
:param mode: whether source- and target-domain quantities are estimated
|
||||
from posterior probabilities (`soft`, default) or from argmax
|
||||
predictions (`hard`)
|
||||
:param alpha: multiplicative factor for the regularization level (default
|
||||
0.01)
|
||||
:param delta: confidence parameter used in the finite-sample regularizer
|
||||
(default 0.05)
|
||||
:param clip_weights: if True, clips negative importance weights to zero
|
||||
before converting them into prevalence estimates
|
||||
:param norm: the normalization method passed to
|
||||
:func:`quapy.functional.normalize_prevalence`
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
classifier: BaseEstimator = None,
|
||||
fit_classifier=True,
|
||||
val_split=5,
|
||||
mode: Literal['soft', 'hard'] = 'soft',
|
||||
alpha: float = 0.01,
|
||||
delta: float = 0.05,
|
||||
clip_weights: bool = True,
|
||||
norm: Literal['clip', 'mapsimplex', 'condsoftmax'] = 'clip',
|
||||
):
|
||||
super().__init__(classifier, fit_classifier, val_split)
|
||||
self.mode = mode
|
||||
self.alpha = alpha
|
||||
self.delta = delta
|
||||
self.clip_weights = clip_weights
|
||||
self.norm = norm
|
||||
self.last_w_ = None
|
||||
|
||||
def _check_init_parameters(self):
|
||||
_get_cvxpy()
|
||||
_rlls_check_mode(self.mode)
|
||||
if not isinstance(self.alpha, (int, float)) or self.alpha < 0:
|
||||
raise ValueError(f'expected a non-negative real value for alpha; found {self.alpha!r}')
|
||||
if not isinstance(self.delta, (int, float)) or not (0 < self.delta < 1):
|
||||
raise ValueError(f'expected delta to be in (0,1); found {self.delta!r}')
|
||||
if self.norm not in ACC.NORMALIZATIONS:
|
||||
raise ValueError(f"unknown normalization; valid ones are {ACC.NORMALIZATIONS}")
|
||||
if self.fit_classifier and self.val_split is None:
|
||||
raise ValueError(
|
||||
'RLLS requires validation predictions for aggregation_fit; '
|
||||
'please set val_split to an integer, float, or validation tuple'
|
||||
)
|
||||
|
||||
def aggregation_fit(self, classif_predictions, labels):
|
||||
if classif_predictions is None or labels is None:
|
||||
raise ValueError('RLLS requires source posterior predictions and source labels')
|
||||
|
||||
self.train_prevalence_ = F.prevalence_from_labels(labels, classes=self.classes_)
|
||||
self.C_zy_ = _rlls_joint_distribution(
|
||||
classif_predictions,
|
||||
labels,
|
||||
self.classes_,
|
||||
mode=self.mode,
|
||||
)
|
||||
self.pz_ = _rlls_predicted_marginal(classif_predictions, mode=self.mode)
|
||||
self.rho_ = _rlls_compute_3deltaC(len(self.classes_), len(labels), self.delta)
|
||||
|
||||
def aggregate(self, classif_posteriors):
|
||||
qz = _rlls_predicted_marginal(classif_posteriors, mode=self.mode)
|
||||
w = _rlls_compute_weights(
|
||||
self.C_zy_,
|
||||
qz,
|
||||
self.pz_,
|
||||
rho=self.alpha * self.rho_,
|
||||
clip=self.clip_weights,
|
||||
)
|
||||
self.last_w_ = w
|
||||
estimate = self.train_prevalence_ * w
|
||||
return F.normalize_prevalence(estimate, method=self.norm)
|
||||
|
||||
|
||||
class EMQ(AggregativeSoftQuantifier):
|
||||
"""
|
||||
`Expectation Maximization for Quantification <https://ieeexplore.ieee.org/abstract/document/6789744>`_ (EMQ),
|
||||
|
|
@ -991,9 +1084,6 @@ class HDy(AggregativeSoftQuantifier, BinaryAggregativeQuantifier):
|
|||
Px = classif_posteriors[:, self.pos_label] # takes only the P(y=+1|x)
|
||||
|
||||
prev_estimations = []
|
||||
# for bins in np.linspace(10, 110, 11, dtype=int): #[10, 20, 30, ..., 100, 110]
|
||||
# Pxy0_density, _ = np.histogram(self.Pxy0, bins=bins, range=(0, 1), density=True)
|
||||
# Pxy1_density, _ = np.histogram(self.Pxy1, bins=bins, range=(0, 1), density=True)
|
||||
for bins in self.bins:
|
||||
Pxy0_density = self.Pxy0_density[bins]
|
||||
Pxy1_density = self.Pxy1_density[bins]
|
||||
|
|
@ -1003,13 +1093,12 @@ class HDy(AggregativeSoftQuantifier, BinaryAggregativeQuantifier):
|
|||
# the authors proposed to search for the prevalence yielding the best matching as a linear search
|
||||
# at small steps (modern implementations resort to an optimization procedure,
|
||||
# see class DistributionMatching)
|
||||
prev_selected, min_dist = None, None
|
||||
for prev in F.prevalence_linspace(grid_points=101, repeats=1, smooth_limits_epsilon=0.0):
|
||||
Px_train = prev * Pxy1_density + (1 - prev) * Pxy0_density
|
||||
hdy = F.HellingerDistance(Px_train, Px_test)
|
||||
if prev_selected is None or hdy < min_dist:
|
||||
prev_selected, min_dist = prev, hdy
|
||||
prev_estimations.append(prev_selected)
|
||||
def loss(prev):
|
||||
class1_prev = prev[1]
|
||||
Px_train = class1_prev * Pxy1_density + (1 - class1_prev) * Pxy0_density
|
||||
return F.HellingerDistance(Px_train, Px_test)
|
||||
|
||||
prev_estimations.append(F.linear_search(loss, n_classes=2)[1])
|
||||
|
||||
class1_prev = np.median(prev_estimations)
|
||||
return F.as_binary_prevalence(class1_prev)
|
||||
|
|
@ -1168,6 +1257,10 @@ class DMy(AggregativeSoftQuantifier):
|
|||
|
||||
:param cdf: whether to use CDF instead of PDF (default False)
|
||||
|
||||
:param search: string indicating the search strategy used to estimate the prevalence values.
|
||||
Valid options are `optim_minimize` (default, works for binary and multiclass problems),
|
||||
`linear_search` (binary only), and `ternary_search` (binary only)
|
||||
|
||||
:param n_jobs: number of parallel workers (default None)
|
||||
"""
|
||||
|
||||
|
|
@ -1218,7 +1311,9 @@ class DMy(AggregativeSoftQuantifier):
|
|||
:param labels: array-like with the true labels associated to each posterior
|
||||
"""
|
||||
posteriors, true_labels = classif_predictions, labels
|
||||
n_classes = len(self.classifier.classes_)
|
||||
classes = self.classifier.classes_
|
||||
n_classes = len(classes)
|
||||
true_labels = _labels_to_indices(true_labels, classes)
|
||||
|
||||
self.validation_distribution = qp.util.parallel(
|
||||
func=self._get_distributions,
|
||||
|
|
|
|||
|
|
@ -9,6 +9,7 @@ from sklearn.preprocessing import normalize
|
|||
from quapy.method.confidence import WithConfidenceABC, ConfidenceRegionABC
|
||||
from quapy.functional import get_divergence
|
||||
from quapy.method.base import BaseQuantifier, BinaryQuantifier
|
||||
from quapy.method._helper import _labels_to_indices
|
||||
import quapy.functional as F
|
||||
from scipy.optimize import lsq_linear
|
||||
from scipy import sparse
|
||||
|
|
@ -58,6 +59,9 @@ class DMx(BaseQuantifier):
|
|||
or a callable function taking two ndarrays of the same dimension as input (default "HD", meaning Hellinger
|
||||
Distance)
|
||||
:param cdf: whether to use CDF instead of PDF (default False)
|
||||
:param search: string indicating the search strategy used to estimate the prevalence values.
|
||||
Valid options are `optim_minimize` (default, works for binary and multiclass problems),
|
||||
`linear_search` (binary only), and `ternary_search` (binary only)
|
||||
:param n_jobs: number of parallel workers (default None)
|
||||
"""
|
||||
|
||||
|
|
@ -122,7 +126,9 @@ class DMx(BaseQuantifier):
|
|||
"""
|
||||
self.nfeats = X.shape[1]
|
||||
self.feat_ranges = _get_features_range(X)
|
||||
n_classes = len(np.unique(y))
|
||||
classes = np.unique(y)
|
||||
y = _labels_to_indices(y, classes)
|
||||
n_classes = len(classes)
|
||||
|
||||
self.validation_distribution = np.asarray(
|
||||
[self.__get_distributions(X[y==cat]) for cat in range(n_classes)]
|
||||
|
|
|
|||
|
|
@ -2,10 +2,13 @@ import itertools
|
|||
import inspect
|
||||
import unittest
|
||||
|
||||
import numpy as np
|
||||
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
|
||||
from quapy.method import AGGREGATIVE_METHODS, BINARY_METHODS, NON_AGGREGATIVE_METHODS
|
||||
from quapy.method.aggregative import ACC
|
||||
from quapy.method.non_aggregative import DMx
|
||||
from quapy.method.aggregative import ACC, DMy, KDEyCS, RLLS
|
||||
from quapy.method.meta import Ensemble
|
||||
from quapy.functional import check_prevalence_vector
|
||||
from quapy.tests._synthetic import make_dataset
|
||||
|
|
@ -16,6 +19,7 @@ OPTIONAL_AGGREGATIVE_METHODS = {
|
|||
'BayesianKDEy',
|
||||
'BayesianMAPLS',
|
||||
'PQ',
|
||||
'RLLS',
|
||||
}
|
||||
|
||||
|
||||
|
|
@ -122,6 +126,54 @@ class TestMethods(unittest.TestCase):
|
|||
from quapy.method.composable import __old_version_message
|
||||
print(__old_version_message)
|
||||
|
||||
def test_rlls(self):
|
||||
try:
|
||||
import cvxpy # noqa: F401
|
||||
except ImportError:
|
||||
return
|
||||
|
||||
dataset = TestMethods.tiny_dataset_multiclass
|
||||
q = RLLS(LogisticRegression(max_iter=2000), val_split=3)
|
||||
q.fit(*dataset.training.Xy)
|
||||
estim_prevalences = q.predict(dataset.test.X)
|
||||
self.assertTrue(check_prevalence_vector(estim_prevalences))
|
||||
|
||||
|
||||
def test_dmy_noncanonical_labels(self):
|
||||
dataset = TestMethods.tiny_dataset_multiclass
|
||||
label_names = np.asarray(['class-a', 'class-c', 'class-z'])
|
||||
y_train = label_names[dataset.training.y]
|
||||
y_test = label_names[dataset.test.y]
|
||||
|
||||
q = DMy(LogisticRegression(max_iter=2000), val_split=3)
|
||||
q.fit(dataset.training.X, y_train)
|
||||
estim_prevalences = q.predict(dataset.test.X)
|
||||
self.assertTrue(check_prevalence_vector(estim_prevalences))
|
||||
self.assertEqual(len(estim_prevalences), len(np.unique(y_test)))
|
||||
|
||||
|
||||
def test_dmx_noncanonical_labels(self):
|
||||
dataset = TestMethods.tiny_dataset_multiclass
|
||||
label_names = np.asarray(['class-a', 'class-c', 'class-z'])
|
||||
y_train = label_names[dataset.training.y]
|
||||
|
||||
q = DMx()
|
||||
q.fit(dataset.training.X, y_train)
|
||||
estim_prevalences = q.predict(dataset.test.X)
|
||||
self.assertTrue(check_prevalence_vector(estim_prevalences))
|
||||
self.assertEqual(len(estim_prevalences), len(np.unique(y_train)))
|
||||
|
||||
def test_kdeycs_noncanonical_labels(self):
|
||||
dataset = TestMethods.tiny_dataset_multiclass
|
||||
label_names = np.asarray(['class-a', 'class-c', 'class-z'])
|
||||
y_train = label_names[dataset.training.y]
|
||||
|
||||
q = KDEyCS(LogisticRegression(max_iter=2000), val_split=3)
|
||||
q.fit(dataset.training.X, y_train)
|
||||
estim_prevalences = q.predict(dataset.test.X)
|
||||
self.assertTrue(check_prevalence_vector(estim_prevalences))
|
||||
self.assertEqual(len(estim_prevalences), len(np.unique(y_train)))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
|
|
|
|||
Loading…
Reference in New Issue