From e92264c280cfe7da225b1b2e7e1b10c658293450 Mon Sep 17 00:00:00 2001
From: Alejandro Moreo <alejandro.moreo@isti.cnr.it>
Date: Wed, 24 Apr 2024 17:03:57 +0200
Subject: [PATCH] adding wiki documents to the sphinx documentation in order to
allow for collaboration
---
docs/Makefile | 15 +-
docs/build/html/_sources/index.rst.txt | 17 +
docs/build/html/_sources/quapy.method.rst.txt | 8 +
docs/build/html/genindex.html | 114 +++-
docs/build/html/index.html | 85 ++-
docs/build/html/modules.html | 2 +
docs/build/html/objects.inv | Bin 3532 -> 4066 bytes
docs/build/html/py-modindex.html | 14 +
docs/build/html/quapy.data.html | 14 +-
docs/build/html/quapy.html | 82 ++-
docs/build/html/quapy.method.html | 609 +++++++++++++++++-
docs/build/html/search.html | 9 +
docs/build/html/searchindex.js | 2 +-
docs/source/conf.py | 2 +
docs/source/index.rst | 17 +
docs/source/wiki_editable/Datasets.md | 440 +++++++++++++
docs/source/wiki_editable/Evaluation.md | 159 +++++
.../wiki_editable/ExplicitLossMinimization.md | 26 +
docs/source/wiki_editable/Methods.md | 526 +++++++++++++++
docs/source/wiki_editable/Model-Selection.md | 145 +++++
docs/source/wiki_editable/Plotting.md | 250 +++++++
docs/source/wiki_editable/Protocols.md | 177 +++++
.../wiki_examples/selected_plots/bin_bias.png | Bin 0 -> 63858 bytes
.../selected_plots/bin_bias_bin_cc.png | Bin 0 -> 110404 bytes
.../selected_plots/bin_bias_cc.png | Bin 0 -> 72541 bytes
.../wiki_examples/selected_plots/bin_diag.png | Bin 0 -> 189871 bytes
.../selected_plots/bin_diag_cc.png | Bin 0 -> 344915 bytes
.../selected_plots/err_drift.png | Bin 0 -> 248874 bytes
28 files changed, 2685 insertions(+), 28 deletions(-)
create mode 100644 docs/source/wiki_editable/Datasets.md
create mode 100644 docs/source/wiki_editable/Evaluation.md
create mode 100644 docs/source/wiki_editable/ExplicitLossMinimization.md
create mode 100644 docs/source/wiki_editable/Methods.md
create mode 100644 docs/source/wiki_editable/Model-Selection.md
create mode 100644 docs/source/wiki_editable/Plotting.md
create mode 100644 docs/source/wiki_editable/Protocols.md
create mode 100644 docs/source/wiki_editable/wiki_examples/selected_plots/bin_bias.png
create mode 100644 docs/source/wiki_editable/wiki_examples/selected_plots/bin_bias_bin_cc.png
create mode 100644 docs/source/wiki_editable/wiki_examples/selected_plots/bin_bias_cc.png
create mode 100644 docs/source/wiki_editable/wiki_examples/selected_plots/bin_diag.png
create mode 100644 docs/source/wiki_editable/wiki_examples/selected_plots/bin_diag_cc.png
create mode 100644 docs/source/wiki_editable/wiki_examples/selected_plots/err_drift.png
diff --git a/docs/Makefile b/docs/Makefile
index d0c3cbf..bb42adc 100644
--- a/docs/Makefile
+++ b/docs/Makefile
@@ -14,7 +14,20 @@ help:
.PHONY: help Makefile
+# Convert Markdown files to reStructuredText before building HTML
+markdown_to_rst:
+ @echo "Converting Markdown files to reStructuredText"
+ @mkdir -p $(SOURCEDIR)/wiki/wiki_examples/selected_plots
+ @cp $(SOURCEDIR)/wiki_editable/wiki_examples/selected_plots/* $(SOURCEDIR)/wiki/wiki_examples/selected_plots/
+ @find $(SOURCEDIR)/wiki_editable -name '*.md' -exec sh -c 'pandoc -f markdown -t rst "$$1" -o "$(SOURCEDIR)/wiki/$$(basename "$$1" .md).rst"' _ {} \;
+ @echo "Conversion complete."
+
# Catch-all target: route all unknown targets to Sphinx using the new
# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
-%: Makefile
+html: markdown_to_rst
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+# # Catch-all target: route all unknown targets to Sphinx using the new
+# # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
+# %: Makefile
+# @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
diff --git a/docs/build/html/_sources/index.rst.txt b/docs/build/html/_sources/index.rst.txt
index cc5b4dc..a4150cd 100644
--- a/docs/build/html/_sources/index.rst.txt
+++ b/docs/build/html/_sources/index.rst.txt
@@ -21,6 +21,23 @@ GitHub
QuaPy is hosted in GitHub at `https://github.com/HLT-ISTI/QuaPy <https://github.com/HLT-ISTI/QuaPy>`_
+Wiki Documents
+------------
+
+In this section you can find useful information concerning different aspects of QuaPy, with examples:
+
+.. toctree::
+ :maxdepth: 1
+
+ wiki/Datasets
+ wiki/Evaluation
+ wiki/ExplicitLossMinimization
+ wiki/Methods
+ wiki/Model-Selection
+ wiki/Plotting
+ wiki/Protocols
+
+
.. toctree::
:maxdepth: 2
:caption: Contents:
diff --git a/docs/build/html/_sources/quapy.method.rst.txt b/docs/build/html/_sources/quapy.method.rst.txt
index 8026e0a..31a357a 100644
--- a/docs/build/html/_sources/quapy.method.rst.txt
+++ b/docs/build/html/_sources/quapy.method.rst.txt
@@ -52,6 +52,14 @@ quapy.method.non\_aggregative module
:undoc-members:
:show-inheritance:
+quapy.method.composable module
+------------------------
+
+.. automodule:: quapy.method.composable
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
Module contents
---------------
diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html
index 0099e44..7dcd630 100644
--- a/docs/build/html/genindex.html
+++ b/docs/build/html/genindex.html
@@ -43,6 +43,15 @@
</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="wiki/Datasets.html">Datasets</a></li>
+<li class="toctree-l1"><a class="reference internal" href="wiki/Evaluation.html">Evaluation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="wiki/ExplicitLossMinimization.html">Explicit Loss Minimization</a></li>
+<li class="toctree-l1"><a class="reference internal" href="wiki/Methods.html">Quantification Methods</a></li>
+<li class="toctree-l1"><a class="reference internal" href="wiki/Model-Selection.html">Model Selection</a></li>
+<li class="toctree-l1"><a class="reference internal" href="wiki/Plotting.html">Plotting</a></li>
+<li class="toctree-l1"><a class="reference internal" href="wiki/Protocols.html">Protocols</a></li>
+</ul>
+<ul>
<li class="toctree-l1"><a class="reference internal" href="modules.html">quapy</a></li>
</ul>
@@ -250,6 +259,8 @@
<li><a href="quapy.method.html#quapy.method.aggregative.BinaryAggregativeQuantifier">BinaryAggregativeQuantifier (class in quapy.method.aggregative)</a>
</li>
<li><a href="quapy.method.html#quapy.method.base.BinaryQuantifier">BinaryQuantifier (class in quapy.method.base)</a>
+</li>
+ <li><a href="quapy.method.html#quapy.method.composable.BlobelLoss">BlobelLoss (class in quapy.method.composable)</a>
</li>
<li><a href="quapy.html#quapy.plot.brokenbar_supremacy_by_drift">brokenbar_supremacy_by_drift() (in module quapy.plot)</a>
</li>
@@ -288,18 +299,24 @@
</li>
</ul></li>
<li><a href="quapy.method.html#quapy.method.aggregative.ClassifyAndCount">ClassifyAndCount (in module quapy.method.aggregative)</a>
+</li>
+ <li><a href="quapy.method.html#quapy.method.composable.ClassTransformer">ClassTransformer (class in quapy.method.composable)</a>
</li>
<li><a href="quapy.method.html#quapy.method._neural.QuaNetTrainer.clean_checkpoint">clean_checkpoint() (quapy.method._neural.QuaNetTrainer method)</a>
</li>
<li><a href="quapy.method.html#quapy.method._neural.QuaNetTrainer.clean_checkpoint_dir">clean_checkpoint_dir() (quapy.method._neural.QuaNetTrainer method)</a>
</li>
- </ul></td>
- <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.html#quapy.functional.clip">clip() (in module quapy.functional)</a>
</li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.classification.html#quapy.classification.neural.CNNnet">CNNnet (class in quapy.classification.neural)</a>
</li>
<li><a href="quapy.html#quapy.protocol.AbstractStochasticSeededProtocol.collator">collator() (quapy.protocol.AbstractStochasticSeededProtocol method)</a>
+</li>
+ <li><a href="quapy.method.html#quapy.method.composable.CombinedLoss">CombinedLoss (class in quapy.method.composable)</a>
+</li>
+ <li><a href="quapy.method.html#quapy.method.composable.ComposableQuantifier">ComposableQuantifier() (in module quapy.method.composable)</a>
</li>
<li><a href="quapy.method.html#quapy.method._threshold_optim.MAX.condition">condition() (quapy.method._threshold_optim.MAX method)</a>
@@ -326,6 +343,8 @@
<li><a href="quapy.html#quapy.util.create_parent_dir">create_parent_dir() (in module quapy.util)</a>
</li>
<li><a href="quapy.html#quapy.model_selection.cross_val_predict">cross_val_predict() (in module quapy.model_selection)</a>
+</li>
+ <li><a href="quapy.method.html#quapy.method.composable.CVClassifier">CVClassifier (class in quapy.method.composable)</a>
</li>
</ul></td>
</tr></table>
@@ -351,12 +370,14 @@
<li><a href="quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.discard">(quapy.method._threshold_optim.ThresholdOptimization method)</a>
</li>
</ul></li>
- <li><a href="quapy.method.html#quapy.method.non_aggregative.DistributionMatchingX">DistributionMatchingX (in module quapy.method.non_aggregative)</a>
+ <li><a href="quapy.method.html#quapy.method.composable.DistanceTransformer">DistanceTransformer (class in quapy.method.composable)</a>
</li>
- <li><a href="quapy.method.html#quapy.method.aggregative.DistributionMatchingY">DistributionMatchingY (in module quapy.method.aggregative)</a>
+ <li><a href="quapy.method.html#quapy.method.non_aggregative.DistributionMatchingX">DistributionMatchingX (in module quapy.method.non_aggregative)</a>
</li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="quapy.method.html#quapy.method.aggregative.DistributionMatchingY">DistributionMatchingY (in module quapy.method.aggregative)</a>
+</li>
<li><a href="quapy.method.html#quapy.method.non_aggregative.DMx">DMx (class in quapy.method.non_aggregative)</a>
</li>
<li><a href="quapy.method.html#quapy.method.aggregative.DMy">DMy (class in quapy.method.aggregative)</a>
@@ -399,10 +420,14 @@
</li>
<li><a href="quapy.method.html#quapy.method.aggregative.EMQ.EMQ_BCTS">EMQ_BCTS() (quapy.method.aggregative.EMQ class method)</a>
</li>
- <li><a href="quapy.method.html#quapy.method.meta.Ensemble">Ensemble (class in quapy.method.meta)</a>
+ <li><a href="quapy.method.html#quapy.method.composable.EnergyKernelTransformer">EnergyKernelTransformer (class in quapy.method.composable)</a>
+</li>
+ <li><a href="quapy.method.html#quapy.method.composable.EnergyLoss">EnergyLoss (class in quapy.method.composable)</a>
</li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="quapy.method.html#quapy.method.meta.Ensemble">Ensemble (class in quapy.method.meta)</a>
+</li>
<li><a href="quapy.method.html#quapy.method.meta.ensembleFactory">ensembleFactory() (in module quapy.method.meta)</a>
</li>
<li><a href="quapy.method.html#quapy.method.meta.EPACC">EPACC() (in module quapy.method.meta)</a>
@@ -473,6 +498,8 @@
<li><a href="quapy.method.html#quapy.method.base.BaseQuantifier.fit">(quapy.method.base.BaseQuantifier method)</a>
</li>
<li><a href="quapy.method.html#quapy.method.base.OneVsAllGeneric.fit">(quapy.method.base.OneVsAllGeneric method)</a>
+</li>
+ <li><a href="quapy.method.html#quapy.method.composable.CVClassifier.fit">(quapy.method.composable.CVClassifier method)</a>
</li>
<li><a href="quapy.method.html#quapy.method.meta.Ensemble.fit">(quapy.method.meta.Ensemble method)</a>
</li>
@@ -496,7 +523,23 @@
<li><a href="quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_tr_val">fit_tr_val() (quapy.classification.calibration.RecalibratedProbabilisticClassifierBase method)</a>
</li>
<li><a href="quapy.data.html#quapy.data.preprocessing.IndexTransformer.fit_transform">fit_transform() (quapy.data.preprocessing.IndexTransformer method)</a>
+
+ <ul>
+ <li><a href="quapy.method.html#quapy.method.composable.ClassTransformer.fit_transform">(quapy.method.composable.ClassTransformer method)</a>
</li>
+ <li><a href="quapy.method.html#quapy.method.composable.DistanceTransformer.fit_transform">(quapy.method.composable.DistanceTransformer method)</a>
+</li>
+ <li><a href="quapy.method.html#quapy.method.composable.EnergyKernelTransformer.fit_transform">(quapy.method.composable.EnergyKernelTransformer method)</a>
+</li>
+ <li><a href="quapy.method.html#quapy.method.composable.GaussianKernelTransformer.fit_transform">(quapy.method.composable.GaussianKernelTransformer method)</a>
+</li>
+ <li><a href="quapy.method.html#quapy.method.composable.GaussianRFFKernelTransformer.fit_transform">(quapy.method.composable.GaussianRFFKernelTransformer method)</a>
+</li>
+ <li><a href="quapy.method.html#quapy.method.composable.HistogramTransformer.fit_transform">(quapy.method.composable.HistogramTransformer method)</a>
+</li>
+ <li><a href="quapy.method.html#quapy.method.composable.KernelTransformer.fit_transform">(quapy.method.composable.KernelTransformer method)</a>
+</li>
+ </ul></li>
<li><a href="quapy.classification.html#quapy.classification.neural.TextClassifierNet.forward">forward() (quapy.classification.neural.TextClassifierNet method)</a>
<ul>
@@ -517,6 +560,10 @@
<h2 id="G">G</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="quapy.method.html#quapy.method.composable.GaussianKernelTransformer">GaussianKernelTransformer (class in quapy.method.composable)</a>
+</li>
+ <li><a href="quapy.method.html#quapy.method.composable.GaussianRFFKernelTransformer">GaussianRFFKernelTransformer (class in quapy.method.composable)</a>
+</li>
<li><a href="quapy.html#quapy.protocol.OnLabelledCollectionProtocol.get_collator">get_collator() (quapy.protocol.OnLabelledCollectionProtocol class method)</a>
</li>
<li><a href="quapy.method.html#quapy.method.aggregative.BayesianCC.get_conditional_probability_samples">get_conditional_probability_samples() (quapy.method.aggregative.BayesianCC method)</a>
@@ -585,11 +632,15 @@
</li>
<li><a href="quapy.method.html#quapy.method.aggregative.HDy">HDy (class in quapy.method.aggregative)</a>
</li>
- </ul></td>
- <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.html#quapy.functional.HellingerDistance">HellingerDistance() (in module quapy.functional)</a>
</li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.method.html#quapy.method.aggregative.HellingerDistanceY">HellingerDistanceY (in module quapy.method.aggregative)</a>
+</li>
+ <li><a href="quapy.method.html#quapy.method.composable.HellingerSurrogateLoss">HellingerSurrogateLoss (class in quapy.method.composable)</a>
+</li>
+ <li><a href="quapy.method.html#quapy.method.composable.HistogramTransformer">HistogramTransformer (class in quapy.method.composable)</a>
</li>
</ul></td>
</tr></table>
@@ -626,10 +677,14 @@
<li><a href="quapy.method.html#quapy.method._kdey.KDEyCS">KDEyCS (class in quapy.method._kdey)</a>
</li>
<li><a href="quapy.method.html#quapy.method._kdey.KDEyHD">KDEyHD (class in quapy.method._kdey)</a>
+</li>
+ <li><a href="quapy.method.html#quapy.method._kdey.KDEyML">KDEyML (class in quapy.method._kdey)</a>
</li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="quapy.method.html#quapy.method._kdey.KDEyML">KDEyML (class in quapy.method._kdey)</a>
+ <li><a href="quapy.method.html#quapy.method.composable.LaplacianKernelTransformer.kernel">kernel (quapy.method.composable.LaplacianKernelTransformer property)</a>
+</li>
+ <li><a href="quapy.method.html#quapy.method.composable.KernelTransformer">KernelTransformer (class in quapy.method.composable)</a>
</li>
<li><a href="quapy.data.html#quapy.data.base.Dataset.kFCV">kFCV() (quapy.data.base.Dataset class method)</a>
@@ -649,10 +704,14 @@
</li>
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection">LabelledCollection (class in quapy.data.base)</a>
</li>
- <li><a href="quapy.html#quapy.functional.linear_search">linear_search() (in module quapy.functional)</a>
+ <li><a href="quapy.method.html#quapy.method.composable.LaplacianKernelTransformer">LaplacianKernelTransformer (class in quapy.method.composable)</a>
+</li>
+ <li><a href="quapy.method.html#quapy.method.composable.LeastSquaresLoss">LeastSquaresLoss (class in quapy.method.composable)</a>
</li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="quapy.html#quapy.functional.linear_search">linear_search() (in module quapy.functional)</a>
+</li>
<li><a href="quapy.data.html#quapy.data.base.Dataset.load">load() (quapy.data.base.Dataset class method)</a>
<ul>
@@ -746,6 +805,8 @@
<li><a href="quapy.method.html#module-quapy.method.aggregative">quapy.method.aggregative</a>
</li>
<li><a href="quapy.method.html#module-quapy.method.base">quapy.method.base</a>
+</li>
+ <li><a href="quapy.method.html#module-quapy.method.composable">quapy.method.composable</a>
</li>
<li><a href="quapy.method.html#module-quapy.method.meta">quapy.method.meta</a>
</li>
@@ -874,6 +935,8 @@
<li><a href="quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.predict">(quapy.classification.neural.NeuralClassifierTrainer method)</a>
</li>
<li><a href="quapy.classification.html#quapy.classification.svmperf.SVMperf.predict">(quapy.classification.svmperf.SVMperf method)</a>
+</li>
+ <li><a href="quapy.method.html#quapy.method.composable.CVClassifier.predict">(quapy.method.composable.CVClassifier method)</a>
</li>
</ul></li>
<li><a href="quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.predict_proba">predict_proba() (quapy.classification.calibration.RecalibratedProbabilisticClassifierBase method)</a>
@@ -886,6 +949,8 @@
<li><a href="quapy.classification.html#quapy.classification.neural.TextClassifierNet.predict_proba">(quapy.classification.neural.TextClassifierNet method)</a>
</li>
<li><a href="quapy.method.html#quapy.method.aggregative.EMQ.predict_proba">(quapy.method.aggregative.EMQ method)</a>
+</li>
+ <li><a href="quapy.method.html#quapy.method.composable.CVClassifier.predict_proba">(quapy.method.composable.CVClassifier method)</a>
</li>
</ul></li>
</ul></td>
@@ -1086,6 +1151,13 @@
<ul>
<li><a href="quapy.method.html#module-quapy.method.base">module</a>
+</li>
+ </ul></li>
+ <li>
+ quapy.method.composable
+
+ <ul>
+ <li><a href="quapy.method.html#module-quapy.method.composable">module</a>
</li>
</ul></li>
<li>
@@ -1279,6 +1351,10 @@
<li><a href="quapy.classification.html#quapy.classification.neural.TextClassifierNet">TextClassifierNet (class in quapy.classification.neural)</a>
</li>
<li><a href="quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization">ThresholdOptimization (class in quapy.method._threshold_optim)</a>
+</li>
+ <li><a href="quapy.method.html#quapy.method.composable.TikhonovRegularization">TikhonovRegularization (class in quapy.method.composable)</a>
+</li>
+ <li><a href="quapy.method.html#quapy.method.composable.TikhonovRegularized">TikhonovRegularized() (in module quapy.method.composable)</a>
</li>
<li><a href="quapy.html#quapy.model_selection.Status.TIMEOUT">TIMEOUT (quapy.model_selection.Status attribute)</a>
</li>
@@ -1322,6 +1398,20 @@
<li><a href="quapy.classification.html#quapy.classification.neural.NeuralClassifierTrainer.transform">(quapy.classification.neural.NeuralClassifierTrainer method)</a>
</li>
<li><a href="quapy.data.html#quapy.data.preprocessing.IndexTransformer.transform">(quapy.data.preprocessing.IndexTransformer method)</a>
+</li>
+ <li><a href="quapy.method.html#quapy.method.composable.ClassTransformer.transform">(quapy.method.composable.ClassTransformer method)</a>
+</li>
+ <li><a href="quapy.method.html#quapy.method.composable.DistanceTransformer.transform">(quapy.method.composable.DistanceTransformer method)</a>
+</li>
+ <li><a href="quapy.method.html#quapy.method.composable.EnergyKernelTransformer.transform">(quapy.method.composable.EnergyKernelTransformer method)</a>
+</li>
+ <li><a href="quapy.method.html#quapy.method.composable.GaussianKernelTransformer.transform">(quapy.method.composable.GaussianKernelTransformer method)</a>
+</li>
+ <li><a href="quapy.method.html#quapy.method.composable.GaussianRFFKernelTransformer.transform">(quapy.method.composable.GaussianRFFKernelTransformer method)</a>
+</li>
+ <li><a href="quapy.method.html#quapy.method.composable.HistogramTransformer.transform">(quapy.method.composable.HistogramTransformer method)</a>
+</li>
+ <li><a href="quapy.method.html#quapy.method.composable.KernelTransformer.transform">(quapy.method.composable.KernelTransformer method)</a>
</li>
</ul></li>
<li><a href="quapy.classification.html#quapy.classification.calibration.TSCalibration">TSCalibration (class in quapy.classification.calibration)</a>
@@ -1332,14 +1422,16 @@
<h2 id="U">U</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="quapy.html#quapy.functional.uniform_prevalence">uniform_prevalence() (in module quapy.functional)</a>
+</li>
<li><a href="quapy.html#quapy.functional.uniform_prevalence_sampling">uniform_prevalence_sampling() (in module quapy.functional)</a>
</li>
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection.uniform_sampling">uniform_sampling() (quapy.data.base.LabelledCollection method)</a>
-</li>
- <li><a href="quapy.data.html#quapy.data.base.LabelledCollection.uniform_sampling_index">uniform_sampling_index() (quapy.data.base.LabelledCollection method)</a>
</li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="quapy.data.html#quapy.data.base.LabelledCollection.uniform_sampling_index">uniform_sampling_index() (quapy.data.base.LabelledCollection method)</a>
+</li>
<li><a href="quapy.html#quapy.functional.uniform_simplex_sampling">uniform_simplex_sampling() (in module quapy.functional)</a>
</li>
<li><a href="quapy.html#quapy.protocol.UniformPrevalenceProtocol">UniformPrevalenceProtocol (in module quapy.protocol)</a>
diff --git a/docs/build/html/index.html b/docs/build/html/index.html
index e3156f3..f475199 100644
--- a/docs/build/html/index.html
+++ b/docs/build/html/index.html
@@ -22,7 +22,7 @@
<script src="_static/js/theme.js"></script>
<link rel="index" title="Index" href="genindex.html" />
<link rel="search" title="Search" href="search.html" />
- <link rel="next" title="quapy" href="modules.html" />
+ <link rel="next" title="Datasets" href="wiki/Datasets.html" />
</head>
<body class="wy-body-for-nav">
@@ -45,6 +45,15 @@
</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="wiki/Datasets.html">Datasets</a></li>
+<li class="toctree-l1"><a class="reference internal" href="wiki/Evaluation.html">Evaluation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="wiki/ExplicitLossMinimization.html">Explicit Loss Minimization</a></li>
+<li class="toctree-l1"><a class="reference internal" href="wiki/Methods.html">Quantification Methods</a></li>
+<li class="toctree-l1"><a class="reference internal" href="wiki/Model-Selection.html">Model Selection</a></li>
+<li class="toctree-l1"><a class="reference internal" href="wiki/Plotting.html">Plotting</a></li>
+<li class="toctree-l1"><a class="reference internal" href="wiki/Protocols.html">Protocols</a></li>
+</ul>
+<ul>
<li class="toctree-l1"><a class="reference internal" href="modules.html">quapy</a></li>
</ul>
@@ -83,6 +92,21 @@
<section id="github">
<h2>GitHub<a class="headerlink" href="#github" title="Permalink to this heading"></a></h2>
<p>QuaPy is hosted in GitHub at <a class="reference external" href="https://github.com/HLT-ISTI/QuaPy">https://github.com/HLT-ISTI/QuaPy</a></p>
+</section>
+<section id="wiki-documents">
+<h2>Wiki Documents<a class="headerlink" href="#wiki-documents" title="Permalink to this heading"></a></h2>
+<p>In this section you can find useful information concerning different aspects of QuaPy, with examples:</p>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="wiki/Datasets.html">Datasets</a></li>
+<li class="toctree-l1"><a class="reference internal" href="wiki/Evaluation.html">Evaluation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="wiki/ExplicitLossMinimization.html">Explicit Loss Minimization</a></li>
+<li class="toctree-l1"><a class="reference internal" href="wiki/Methods.html">Quantification Methods</a></li>
+<li class="toctree-l1"><a class="reference internal" href="wiki/Model-Selection.html">Model Selection</a></li>
+<li class="toctree-l1"><a class="reference internal" href="wiki/Plotting.html">Plotting</a></li>
+<li class="toctree-l1"><a class="reference internal" href="wiki/Protocols.html">Protocols</a></li>
+</ul>
+</div>
<div class="toctree-wrapper compound">
</div>
</section>
@@ -517,6 +541,62 @@
</li>
</ul>
</li>
+<li class="toctree-l5"><a class="reference internal" href="quapy.method.html#quapy-method-composable-module">quapy.method.composable module</a><ul>
+<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.BlobelLoss"><code class="docutils literal notranslate"><span class="pre">BlobelLoss</span></code></a></li>
+<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.CVClassifier"><code class="docutils literal notranslate"><span class="pre">CVClassifier</span></code></a><ul>
+<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.CVClassifier.fit"><code class="docutils literal notranslate"><span class="pre">CVClassifier.fit()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.CVClassifier.predict"><code class="docutils literal notranslate"><span class="pre">CVClassifier.predict()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.CVClassifier.predict_proba"><code class="docutils literal notranslate"><span class="pre">CVClassifier.predict_proba()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.ClassTransformer"><code class="docutils literal notranslate"><span class="pre">ClassTransformer</span></code></a><ul>
+<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.ClassTransformer.fit_transform"><code class="docutils literal notranslate"><span class="pre">ClassTransformer.fit_transform()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.ClassTransformer.transform"><code class="docutils literal notranslate"><span class="pre">ClassTransformer.transform()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.CombinedLoss"><code class="docutils literal notranslate"><span class="pre">CombinedLoss</span></code></a></li>
+<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.ComposableQuantifier"><code class="docutils literal notranslate"><span class="pre">ComposableQuantifier()</span></code></a></li>
+<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.DistanceTransformer"><code class="docutils literal notranslate"><span class="pre">DistanceTransformer</span></code></a><ul>
+<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.DistanceTransformer.fit_transform"><code class="docutils literal notranslate"><span class="pre">DistanceTransformer.fit_transform()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.DistanceTransformer.transform"><code class="docutils literal notranslate"><span class="pre">DistanceTransformer.transform()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.EnergyKernelTransformer"><code class="docutils literal notranslate"><span class="pre">EnergyKernelTransformer</span></code></a><ul>
+<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.EnergyKernelTransformer.fit_transform"><code class="docutils literal notranslate"><span class="pre">EnergyKernelTransformer.fit_transform()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.EnergyKernelTransformer.transform"><code class="docutils literal notranslate"><span class="pre">EnergyKernelTransformer.transform()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.EnergyLoss"><code class="docutils literal notranslate"><span class="pre">EnergyLoss</span></code></a></li>
+<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.GaussianKernelTransformer"><code class="docutils literal notranslate"><span class="pre">GaussianKernelTransformer</span></code></a><ul>
+<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.GaussianKernelTransformer.fit_transform"><code class="docutils literal notranslate"><span class="pre">GaussianKernelTransformer.fit_transform()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.GaussianKernelTransformer.transform"><code class="docutils literal notranslate"><span class="pre">GaussianKernelTransformer.transform()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.GaussianRFFKernelTransformer"><code class="docutils literal notranslate"><span class="pre">GaussianRFFKernelTransformer</span></code></a><ul>
+<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.GaussianRFFKernelTransformer.fit_transform"><code class="docutils literal notranslate"><span class="pre">GaussianRFFKernelTransformer.fit_transform()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.GaussianRFFKernelTransformer.transform"><code class="docutils literal notranslate"><span class="pre">GaussianRFFKernelTransformer.transform()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.HellingerSurrogateLoss"><code class="docutils literal notranslate"><span class="pre">HellingerSurrogateLoss</span></code></a></li>
+<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.HistogramTransformer"><code class="docutils literal notranslate"><span class="pre">HistogramTransformer</span></code></a><ul>
+<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.HistogramTransformer.fit_transform"><code class="docutils literal notranslate"><span class="pre">HistogramTransformer.fit_transform()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.HistogramTransformer.transform"><code class="docutils literal notranslate"><span class="pre">HistogramTransformer.transform()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.KernelTransformer"><code class="docutils literal notranslate"><span class="pre">KernelTransformer</span></code></a><ul>
+<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.KernelTransformer.fit_transform"><code class="docutils literal notranslate"><span class="pre">KernelTransformer.fit_transform()</span></code></a></li>
+<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.KernelTransformer.transform"><code class="docutils literal notranslate"><span class="pre">KernelTransformer.transform()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.LaplacianKernelTransformer"><code class="docutils literal notranslate"><span class="pre">LaplacianKernelTransformer</span></code></a><ul>
+<li class="toctree-l7"><a class="reference internal" href="quapy.method.html#quapy.method.composable.LaplacianKernelTransformer.kernel"><code class="docutils literal notranslate"><span class="pre">LaplacianKernelTransformer.kernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.LeastSquaresLoss"><code class="docutils literal notranslate"><span class="pre">LeastSquaresLoss</span></code></a></li>
+<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.TikhonovRegularization"><code class="docutils literal notranslate"><span class="pre">TikhonovRegularization</span></code></a></li>
+<li class="toctree-l6"><a class="reference internal" href="quapy.method.html#quapy.method.composable.TikhonovRegularized"><code class="docutils literal notranslate"><span class="pre">TikhonovRegularized()</span></code></a></li>
+</ul>
+</li>
<li class="toctree-l5"><a class="reference internal" href="quapy.method.html#module-quapy.method">Module contents</a></li>
</ul>
</li>
@@ -586,6 +666,7 @@
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.solve_adjustment_binary"><code class="docutils literal notranslate"><span class="pre">solve_adjustment_binary()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.strprev"><code class="docutils literal notranslate"><span class="pre">strprev()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.ternary_search"><code class="docutils literal notranslate"><span class="pre">ternary_search()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.uniform_prevalence"><code class="docutils literal notranslate"><span class="pre">uniform_prevalence()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.uniform_prevalence_sampling"><code class="docutils literal notranslate"><span class="pre">uniform_prevalence_sampling()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.functional.uniform_simplex_sampling"><code class="docutils literal notranslate"><span class="pre">uniform_simplex_sampling()</span></code></a></li>
</ul>
@@ -715,7 +796,7 @@
</div>
</div>
<footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
- <a href="modules.html" class="btn btn-neutral float-right" title="quapy" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+ <a href="wiki/Datasets.html" class="btn btn-neutral float-right" title="Datasets" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
</div>
<hr/>
diff --git a/docs/build/html/modules.html b/docs/build/html/modules.html
index be528e0..2232595 100644
--- a/docs/build/html/modules.html
+++ b/docs/build/html/modules.html
@@ -106,6 +106,7 @@
<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#module-quapy.method.base">quapy.method.base module</a></li>
<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#module-quapy.method.meta">quapy.method.meta module</a></li>
<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#module-quapy.method.non_aggregative">quapy.method.non_aggregative module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#quapy-method-composable-module">quapy.method.composable module</a></li>
<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#module-quapy.method">Module contents</a></li>
</ul>
</li>
@@ -175,6 +176,7 @@
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.solve_adjustment_binary"><code class="docutils literal notranslate"><span class="pre">solve_adjustment_binary()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.strprev"><code class="docutils literal notranslate"><span class="pre">strprev()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.ternary_search"><code class="docutils literal notranslate"><span class="pre">ternary_search()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.uniform_prevalence"><code class="docutils literal notranslate"><span class="pre">uniform_prevalence()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.uniform_prevalence_sampling"><code class="docutils literal notranslate"><span class="pre">uniform_prevalence_sampling()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.functional.uniform_simplex_sampling"><code class="docutils literal notranslate"><span class="pre">uniform_simplex_sampling()</span></code></a></li>
</ul>
diff --git a/docs/build/html/objects.inv b/docs/build/html/objects.inv
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diff --git a/docs/build/html/py-modindex.html b/docs/build/html/py-modindex.html
index de81f69..0dc841b 100644
--- a/docs/build/html/py-modindex.html
+++ b/docs/build/html/py-modindex.html
@@ -46,6 +46,15 @@
</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="wiki/Datasets.html">Datasets</a></li>
+<li class="toctree-l1"><a class="reference internal" href="wiki/Evaluation.html">Evaluation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="wiki/ExplicitLossMinimization.html">Explicit Loss Minimization</a></li>
+<li class="toctree-l1"><a class="reference internal" href="wiki/Methods.html">Quantification Methods</a></li>
+<li class="toctree-l1"><a class="reference internal" href="wiki/Model-Selection.html">Model Selection</a></li>
+<li class="toctree-l1"><a class="reference internal" href="wiki/Plotting.html">Plotting</a></li>
+<li class="toctree-l1"><a class="reference internal" href="wiki/Protocols.html">Protocols</a></li>
+</ul>
+<ul>
<li class="toctree-l1"><a class="reference internal" href="modules.html">quapy</a></li>
</ul>
@@ -184,6 +193,11 @@
<td>   
<a href="quapy.method.html#module-quapy.method.base"><code class="xref">quapy.method.base</code></a></td><td>
<em></em></td></tr>
+ <tr class="cg-1">
+ <td></td>
+ <td>   
+ <a href="quapy.method.html#module-quapy.method.composable"><code class="xref">quapy.method.composable</code></a></td><td>
+ <em></em></td></tr>
<tr class="cg-1">
<td></td>
<td>   
diff --git a/docs/build/html/quapy.data.html b/docs/build/html/quapy.data.html
index 0f0f06f..24f8aea 100644
--- a/docs/build/html/quapy.data.html
+++ b/docs/build/html/quapy.data.html
@@ -213,7 +213,7 @@ See <a class="reference internal" href="#quapy.data.base.LabelledCollection.load
<dl class="py method">
<dt class="sig sig-object py" id="quapy.data.base.Dataset.reduce">
-<span class="sig-name descname"><span class="pre">reduce</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_train</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_test</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#Dataset.reduce"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.Dataset.reduce" title="Permalink to this definition"></a></dt>
+<span class="sig-name descname"><span class="pre">reduce</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_train</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_test</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#Dataset.reduce"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.Dataset.reduce" title="Permalink to this definition"></a></dt>
<dd><p>Reduce the number of instances in place for quick experiments. Preserves the prevalence of each set.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -448,8 +448,7 @@ as listed by <cite>self.classes_</cite></p>
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.sampling">
<span class="sig-name descname"><span class="pre">sampling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.sampling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.sampling" title="Permalink to this definition"></a></dt>
<dd><p>Return a random sample (an instance of <a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">LabelledCollection</span></code></a>) of desired size and desired prevalence
-values. For each class, the sampling is drawn without replacement if the requested prevalence is larger than
-the actual prevalence of the class, or with replacement otherwise.</p>
+values. For each class, the sampling is drawn with replacement.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
@@ -488,8 +487,7 @@ index.</p>
<span class="sig-name descname"><span class="pre">sampling_index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">prevs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.sampling_index"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.sampling_index" title="Permalink to this definition"></a></dt>
<dd><p>Returns an index to be used to extract a random sample of desired size and desired prevalence values. If the
prevalence values are not specified, then returns the index of a uniform sampling.
-For each class, the sampling is drawn with replacement if the requested prevalence is larger than
-the actual prevalence of the class, or without replacement otherwise.</p>
+For each class, the sampling is drawn with replacement.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
@@ -575,8 +573,7 @@ values for each class)</p>
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.uniform_sampling">
<span class="sig-name descname"><span class="pre">uniform_sampling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.uniform_sampling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.uniform_sampling" title="Permalink to this definition"></a></dt>
<dd><p>Returns a uniform sample (an instance of <a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">LabelledCollection</span></code></a>) of desired size. The sampling is drawn
-with replacement if the requested size is greater than the number of instances, or without replacement
-otherwise.</p>
+with replacement.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
@@ -594,8 +591,7 @@ otherwise.</p>
<dt class="sig sig-object py" id="quapy.data.base.LabelledCollection.uniform_sampling_index">
<span class="sig-name descname"><span class="pre">uniform_sampling_index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/data/base.html#LabelledCollection.uniform_sampling_index"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.base.LabelledCollection.uniform_sampling_index" title="Permalink to this definition"></a></dt>
<dd><p>Returns an index to be used to extract a uniform sample of desired size. The sampling is drawn
-with replacement if the requested size is greater than the number of instances, or without replacement
-otherwise.</p>
+with replacement.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
diff --git a/docs/build/html/quapy.html b/docs/build/html/quapy.html
index 0803b74..5c7970a 100644
--- a/docs/build/html/quapy.html
+++ b/docs/build/html/quapy.html
@@ -119,6 +119,7 @@
<li class="toctree-l4"><a class="reference internal" href="#quapy.functional.solve_adjustment_binary"><code class="docutils literal notranslate"><span class="pre">solve_adjustment_binary()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#quapy.functional.strprev"><code class="docutils literal notranslate"><span class="pre">strprev()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#quapy.functional.ternary_search"><code class="docutils literal notranslate"><span class="pre">ternary_search()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#quapy.functional.uniform_prevalence"><code class="docutils literal notranslate"><span class="pre">uniform_prevalence()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#quapy.functional.uniform_prevalence_sampling"><code class="docutils literal notranslate"><span class="pre">uniform_prevalence_sampling()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#quapy.functional.uniform_simplex_sampling"><code class="docutils literal notranslate"><span class="pre">uniform_simplex_sampling()</span></code></a></li>
</ul>
@@ -632,6 +633,62 @@
</li>
</ul>
</li>
+<li class="toctree-l2"><a class="reference internal" href="quapy.method.html#quapy-method-composable-module">quapy.method.composable module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="quapy.method.html#quapy.method.composable.BlobelLoss"><code class="docutils literal notranslate"><span class="pre">BlobelLoss</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="quapy.method.html#quapy.method.composable.CVClassifier"><code class="docutils literal notranslate"><span class="pre">CVClassifier</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#quapy.method.composable.CVClassifier.fit"><code class="docutils literal notranslate"><span class="pre">CVClassifier.fit()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#quapy.method.composable.CVClassifier.predict"><code class="docutils literal notranslate"><span class="pre">CVClassifier.predict()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#quapy.method.composable.CVClassifier.predict_proba"><code class="docutils literal notranslate"><span class="pre">CVClassifier.predict_proba()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="quapy.method.html#quapy.method.composable.ClassTransformer"><code class="docutils literal notranslate"><span class="pre">ClassTransformer</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#quapy.method.composable.ClassTransformer.fit_transform"><code class="docutils literal notranslate"><span class="pre">ClassTransformer.fit_transform()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#quapy.method.composable.ClassTransformer.transform"><code class="docutils literal notranslate"><span class="pre">ClassTransformer.transform()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="quapy.method.html#quapy.method.composable.CombinedLoss"><code class="docutils literal notranslate"><span class="pre">CombinedLoss</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="quapy.method.html#quapy.method.composable.ComposableQuantifier"><code class="docutils literal notranslate"><span class="pre">ComposableQuantifier()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="quapy.method.html#quapy.method.composable.DistanceTransformer"><code class="docutils literal notranslate"><span class="pre">DistanceTransformer</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#quapy.method.composable.DistanceTransformer.fit_transform"><code class="docutils literal notranslate"><span class="pre">DistanceTransformer.fit_transform()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#quapy.method.composable.DistanceTransformer.transform"><code class="docutils literal notranslate"><span class="pre">DistanceTransformer.transform()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="quapy.method.html#quapy.method.composable.EnergyKernelTransformer"><code class="docutils literal notranslate"><span class="pre">EnergyKernelTransformer</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#quapy.method.composable.EnergyKernelTransformer.fit_transform"><code class="docutils literal notranslate"><span class="pre">EnergyKernelTransformer.fit_transform()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#quapy.method.composable.EnergyKernelTransformer.transform"><code class="docutils literal notranslate"><span class="pre">EnergyKernelTransformer.transform()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="quapy.method.html#quapy.method.composable.EnergyLoss"><code class="docutils literal notranslate"><span class="pre">EnergyLoss</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="quapy.method.html#quapy.method.composable.GaussianKernelTransformer"><code class="docutils literal notranslate"><span class="pre">GaussianKernelTransformer</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#quapy.method.composable.GaussianKernelTransformer.fit_transform"><code class="docutils literal notranslate"><span class="pre">GaussianKernelTransformer.fit_transform()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#quapy.method.composable.GaussianKernelTransformer.transform"><code class="docutils literal notranslate"><span class="pre">GaussianKernelTransformer.transform()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="quapy.method.html#quapy.method.composable.GaussianRFFKernelTransformer"><code class="docutils literal notranslate"><span class="pre">GaussianRFFKernelTransformer</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#quapy.method.composable.GaussianRFFKernelTransformer.fit_transform"><code class="docutils literal notranslate"><span class="pre">GaussianRFFKernelTransformer.fit_transform()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#quapy.method.composable.GaussianRFFKernelTransformer.transform"><code class="docutils literal notranslate"><span class="pre">GaussianRFFKernelTransformer.transform()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="quapy.method.html#quapy.method.composable.HellingerSurrogateLoss"><code class="docutils literal notranslate"><span class="pre">HellingerSurrogateLoss</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="quapy.method.html#quapy.method.composable.HistogramTransformer"><code class="docutils literal notranslate"><span class="pre">HistogramTransformer</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#quapy.method.composable.HistogramTransformer.fit_transform"><code class="docutils literal notranslate"><span class="pre">HistogramTransformer.fit_transform()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#quapy.method.composable.HistogramTransformer.transform"><code class="docutils literal notranslate"><span class="pre">HistogramTransformer.transform()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="quapy.method.html#quapy.method.composable.KernelTransformer"><code class="docutils literal notranslate"><span class="pre">KernelTransformer</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#quapy.method.composable.KernelTransformer.fit_transform"><code class="docutils literal notranslate"><span class="pre">KernelTransformer.fit_transform()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#quapy.method.composable.KernelTransformer.transform"><code class="docutils literal notranslate"><span class="pre">KernelTransformer.transform()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="quapy.method.html#quapy.method.composable.LaplacianKernelTransformer"><code class="docutils literal notranslate"><span class="pre">LaplacianKernelTransformer</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="quapy.method.html#quapy.method.composable.LaplacianKernelTransformer.kernel"><code class="docutils literal notranslate"><span class="pre">LaplacianKernelTransformer.kernel</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="quapy.method.html#quapy.method.composable.LeastSquaresLoss"><code class="docutils literal notranslate"><span class="pre">LeastSquaresLoss</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="quapy.method.html#quapy.method.composable.TikhonovRegularization"><code class="docutils literal notranslate"><span class="pre">TikhonovRegularization</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="quapy.method.html#quapy.method.composable.TikhonovRegularized"><code class="docutils literal notranslate"><span class="pre">TikhonovRegularized()</span></code></a></li>
+</ul>
+</li>
<li class="toctree-l2"><a class="reference internal" href="quapy.method.html#module-quapy.method">Module contents</a></li>
</ul>
</li>
@@ -1534,7 +1591,16 @@ then it returns one such bool for each prevalence vector</p>
<dl class="py function">
<dt class="sig sig-object py" id="quapy.functional.condsoftmax">
<span class="sig-prename descclassname"><span class="pre">quapy.functional.</span></span><span class="sig-name descname"><span class="pre">condsoftmax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prevalences</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">_SupportsArray</span><span class="p"><span class="pre">[</span></span><span class="pre">dtype</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">_NestedSequence</span><span class="p"><span class="pre">[</span></span><span class="pre">_SupportsArray</span><span class="p"><span class="pre">[</span></span><span class="pre">dtype</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">bool</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">int</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">complex</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">bytes</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">_NestedSequence</span><span class="p"><span class="pre">[</span></span><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">bool</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">int</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">float</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">complex</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">bytes</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">ndarray</span></span></span><a class="reference internal" href="_modules/quapy/functional.html#condsoftmax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.functional.condsoftmax" title="Permalink to this definition"></a></dt>
-<dd></dd></dl>
+<dd><p>Applies the softmax function only to vectors that do not represent valid distributions.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>prevalences</strong> – array-like of shape <cite>(n_classes,)</cite> or of shape <cite>(n_samples, n_classes,)</cite> with prevalence values</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>np.ndarray representing a valid distribution</p>
+</dd>
+</dl>
+</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="quapy.functional.counts_from_labels">
@@ -1882,6 +1948,20 @@ positive class <cite>p</cite> comes down to computing:</p>
<span class="sig-prename descclassname"><span class="pre">quapy.functional.</span></span><span class="sig-name descname"><span class="pre">ternary_search</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Callable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/functional.html#ternary_search"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.functional.ternary_search" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
+<dl class="py function">
+<dt class="sig sig-object py" id="quapy.functional.uniform_prevalence">
+<span class="sig-prename descclassname"><span class="pre">quapy.functional.</span></span><span class="sig-name descname"><span class="pre">uniform_prevalence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/functional.html#uniform_prevalence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.functional.uniform_prevalence" title="Permalink to this definition"></a></dt>
+<dd><p>Returns a vector representing the uniform distribution for <cite>n_classes</cite></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>n_classes</strong> – number of classes</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>np.ndarray with all values 1/n_classes</p>
+</dd>
+</dl>
+</dd></dl>
+
<dl class="py function">
<dt class="sig sig-object py" id="quapy.functional.uniform_prevalence_sampling">
<span class="sig-prename descclassname"><span class="pre">quapy.functional.</span></span><span class="sig-name descname"><span class="pre">uniform_prevalence_sampling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">ndarray</span></span></span><a class="reference internal" href="_modules/quapy/functional.html#uniform_prevalence_sampling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.functional.uniform_prevalence_sampling" title="Permalink to this definition"></a></dt>
diff --git a/docs/build/html/quapy.method.html b/docs/build/html/quapy.method.html
index c71d492..5f82403 100644
--- a/docs/build/html/quapy.method.html
+++ b/docs/build/html/quapy.method.html
@@ -458,6 +458,13 @@ non-probabilistic quantifiers. The default one is “decision_function”.</p>
<li><p><strong>data</strong> – a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
<li><p><strong>fit_classifier</strong> – whether to train the learner (default is True). Set to False if the
learner has been trained outside the quantifier.</p></li>
+<li><p><strong>val_split</strong> – 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 <cite>k</cite>-fold cross-validation manner (with this integer indicating the value
+for <cite>k</cite>); or as a collection defining the specific set of data to use for validation.
+Alternatively, this set can be specified at fit time by indicating the exact set of data
+on which the predictions are to be generated.</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -587,6 +594,13 @@ as instances, the label predictions issued by the classifier and, as labels, the
<li><p><strong>data</strong> – a <a class="reference internal" href="quapy.data.html#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.LabelledCollection</span></code></a> consisting of the training data</p></li>
<li><p><strong>fit_classifier</strong> – whether to train the learner (default is True). Set to False if the
learner has been trained outside the quantifier.</p></li>
+<li><p><strong>val_split</strong> – 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 <cite>k</cite>-fold cross-validation manner (with this integer indicating the value
+for <cite>k</cite>); or as a collection defining the specific set of data to use for validation.
+Alternatively, this set can be specified at fit time by indicating the exact set of data
+on which the predictions are to be generated.</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -2085,7 +2099,7 @@ This function should return the (float) score to be minimized.</p>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method._threshold_optim.ThresholdOptimization">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method._threshold_optim.</span></span><span class="sig-name descname"><span class="pre">ThresholdOptimization</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BaseEstimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_threshold_optim.html#ThresholdOptimization"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._threshold_optim.ThresholdOptimization" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method._threshold_optim.</span></span><span class="sig-name descname"><span class="pre">ThresholdOptimization</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">BaseEstimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_threshold_optim.html#ThresholdOptimization"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._threshold_optim.ThresholdOptimization" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.BinaryAggregativeQuantifier" title="quapy.method.aggregative.BinaryAggregativeQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">BinaryAggregativeQuantifier</span></code></a></p>
<p>Abstract class of Threshold Optimization variants for <code class="xref py py-class docutils literal notranslate"><span class="pre">ACC</span></code> as proposed by
<a class="reference external" href="https://dl.acm.org/doi/abs/10.1145/1150402.1150423">Forman 2006</a> and
@@ -2276,7 +2290,7 @@ This function should return the (float) score to be minimized.</p>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.base.OneVsAllGeneric">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.base.</span></span><span class="sig-name descname"><span class="pre">OneVsAllGeneric</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">binary_quantifier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/base.html#OneVsAllGeneric"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.base.OneVsAllGeneric" title="Permalink to this definition"></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.base.</span></span><span class="sig-name descname"><span class="pre">OneVsAllGeneric</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">binary_quantifier</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><span class="pre">BaseQuantifier</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/base.html#OneVsAllGeneric"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.base.OneVsAllGeneric" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.base.OneVsAll" title="quapy.method.base.OneVsAll"><code class="xref py py-class docutils literal notranslate"><span class="pre">OneVsAll</span></code></a>, <a class="reference internal" href="#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">BaseQuantifier</span></code></a></p>
<p>Allows any binary quantifier to perform quantification on single-label datasets. The method maintains one binary
quantifier for each class, and then l1-normalizes the outputs so that the class prevelence values sum up to 1.</p>
@@ -2317,7 +2331,7 @@ quantifier for each class, and then l1-normalizes the outputs so that the class
<dl class="py function">
<dt class="sig sig-object py" id="quapy.method.base.newOneVsAll">
-<span class="sig-prename descclassname"><span class="pre">quapy.method.base.</span></span><span class="sig-name descname"><span class="pre">newOneVsAll</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">binary_quantifier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/base.html#newOneVsAll"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.base.newOneVsAll" title="Permalink to this definition"></a></dt>
+<span class="sig-prename descclassname"><span class="pre">quapy.method.base.</span></span><span class="sig-name descname"><span class="pre">newOneVsAll</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">binary_quantifier</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#quapy.method.base.BaseQuantifier" title="quapy.method.base.BaseQuantifier"><span class="pre">BaseQuantifier</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/base.html#newOneVsAll"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.base.newOneVsAll" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</section>
@@ -2996,6 +3010,595 @@ any quantification method should beat.</p>
</dd></dl>
+</section>
+<section id="quapy-method-composable-module">
+<h2>quapy.method.composable module<a class="headerlink" href="#quapy-method-composable-module" title="Permalink to this heading"></a></h2>
+<span class="target" id="module-quapy.method.composable"></span><p>This module allows the composition of quantification methods from loss functions and feature transformations. This functionality is realized through an integration of the qunfold package: <a class="reference external" href="https://github.com/mirkobunse/qunfold">https://github.com/mirkobunse/qunfold</a>.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="quapy.method.composable.BlobelLoss">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.composable.</span></span><span class="sig-name descname"><span class="pre">BlobelLoss</span></span><a class="reference internal" href="_modules/qunfold/losses.html#BlobelLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.BlobelLoss" title="Permalink to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">FunctionLoss</span></code></p>
+<p>The loss function of RUN (Blobel, 1985).</p>
+<p>This loss function models a likelihood function under the assumption of independent Poisson-distributed elements of <cite>q</cite> with Poisson rates <cite>M*p</cite>.</p>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="quapy.method.composable.CVClassifier">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.composable.</span></span><span class="sig-name descname"><span class="pre">CVClassifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_estimators</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/qunfold/sklearn.html#CVClassifier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.CVClassifier" title="Permalink to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">BaseEstimator</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">ClassifierMixin</span></code></p>
+<p>An ensemble of classifiers that are trained from cross-validation folds.</p>
+<p>All objects of this type have a fixed attribute <cite>oob_score = True</cite> and, when trained, a fitted attribute <cite>self.oob_decision_function_</cite>, just like scikit-learn bagging classifiers.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>estimator</strong> – A classifier that implements the API of scikit-learn.</p></li>
+<li><p><strong>n_estimators</strong> (<em>optional</em>) – The number of stratified cross-validation folds. Defaults to <cite>5</cite>.</p></li>
+<li><p><strong>random_state</strong> (<em>optional</em>) – The random state for stratification. Defaults to <cite>None</cite>.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<p>Here, we create an instance of ACC that trains a logistic regression classifier with 10 cross-validation folds.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">ACC</span><span class="p">(</span><span class="n">CVClassifier</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">(),</span> <span class="mi">10</span><span class="p">))</span>
+</pre></div>
+</div>
+<dl class="py method">
+<dt class="sig sig-object py" id="quapy.method.composable.CVClassifier.fit">
+<span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/qunfold/sklearn.html#CVClassifier.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.CVClassifier.fit" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="quapy.method.composable.CVClassifier.predict">
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/qunfold/sklearn.html#CVClassifier.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.CVClassifier.predict" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="quapy.method.composable.CVClassifier.predict_proba">
+<span class="sig-name descname"><span class="pre">predict_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/qunfold/sklearn.html#CVClassifier.predict_proba"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.CVClassifier.predict_proba" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="quapy.method.composable.ClassTransformer">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.composable.</span></span><span class="sig-name descname"><span class="pre">ClassTransformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">classifier</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_probabilistic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fit_classifier</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/qunfold/transformers.html#ClassTransformer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.ClassTransformer" title="Permalink to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">AbstractTransformer</span></code></p>
+<p>A classification-based feature transformation.</p>
+<p>This transformation can either be probabilistic (using the posterior predictions of a classifier) or crisp (using the class predictions of a classifier). It is used in ACC, PACC, CC, PCC, and SLD.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>classifier</strong> – A classifier that implements the API of scikit-learn.</p></li>
+<li><p><strong>is_probabilistic</strong> (<em>optional</em>) – Whether probabilistic or crisp predictions of the <cite>classifier</cite> are used to transform the data. Defaults to <cite>False</cite>.</p></li>
+<li><p><strong>fit_classifier</strong> (<em>optional</em>) – Whether to fit the <cite>classifier</cite> when this quantifier is fitted. Defaults to <cite>True</cite>.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="quapy.method.composable.ClassTransformer.fit_transform">
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/qunfold/transformers.html#ClassTransformer.fit_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.ClassTransformer.fit_transform" title="Permalink to this definition"></a></dt>
+<dd><p>This abstract method has to fit the transformer and to return the transformation of the input data.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Implementations of this abstract method should check the sanity of labels by calling <cite>_check_y(y, n_classes)</cite> and they must set the property <cite>self.p_trn = class_prevalences(y, n_classes)</cite>.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> – The feature matrix to which this transformer will be fitted.</p></li>
+<li><p><strong>y</strong> – The labels to which this transformer will be fitted.</p></li>
+<li><p><strong>average</strong> (<em>optional</em>) – Whether to return a transfer matrix <cite>M</cite> or a transformation <cite>(f(X), y)</cite>. Defaults to <cite>True</cite>.</p></li>
+<li><p><strong>n_classes</strong> (<em>optional</em>) – The number of expected classes. Defaults to <cite>None</cite>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>A transfer matrix <cite>M</cite> if <cite>average==True</cite> or a transformation <cite>(f(X), y)</cite> if <cite>average==False</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="quapy.method.composable.ClassTransformer.transform">
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/qunfold/transformers.html#ClassTransformer.transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.ClassTransformer.transform" title="Permalink to this definition"></a></dt>
+<dd><p>This abstract method has to transform the data <cite>X</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> – The feature matrix that will be transformed.</p></li>
+<li><p><strong>average</strong> (<em>optional</em>) – Whether to return a vector <cite>q</cite> or a transformation <cite>f(X)</cite>. Defaults to <cite>True</cite>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>A vector <cite>q = f(X).mean(axis=0)</cite> if <cite>average==True</cite> or a transformation <cite>f(X)</cite> if <cite>average==False</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="quapy.method.composable.CombinedLoss">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.composable.</span></span><span class="sig-name descname"><span class="pre">CombinedLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">losses</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/qunfold/losses.html#CombinedLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.CombinedLoss" title="Permalink to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">AbstractLoss</span></code></p>
+<p>The weighted sum of multiple losses.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>*losses</strong> – An arbitrary number of losses to be added together.</p></li>
+<li><p><strong>weights</strong> (<em>optional</em>) – An array of weights which the losses are scaled.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="quapy.method.composable.ComposableQuantifier">
+<span class="sig-prename descclassname"><span class="pre">quapy.method.composable.</span></span><span class="sig-name descname"><span class="pre">ComposableQuantifier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transformer</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/composable.html#ComposableQuantifier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.ComposableQuantifier" title="Permalink to this definition"></a></dt>
+<dd><p>A generic quantification / unfolding method that solves a linear system of equations.</p>
+<p>This class represents any quantifier that can be described in terms of a loss function, a feature transformation, and a regularization term. In this implementation, the loss is minimized through unconstrained second-order minimization. Valid probability estimates are ensured through a soft-max trick by Bunse (2022).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>loss</strong> – An instance of a loss class from <cite>quapy.methods.composable</cite>.</p></li>
+<li><p><strong>transformer</strong> – An instance of a transformer class from <cite>quapy.methods.composable</cite>.</p></li>
+<li><p><strong>solver</strong> (<em>optional</em>) – The <cite>method</cite> argument in <cite>scipy.optimize.minimize</cite>. Defaults to <cite>“trust-ncg”</cite>.</p></li>
+<li><p><strong>solver_options</strong> (<em>optional</em>) – The <cite>options</cite> argument in <cite>scipy.optimize.minimize</cite>. Defaults to <cite>{“gtol”: 1e-8, “maxiter”: 1000}</cite>.</p></li>
+<li><p><strong>seed</strong> (<em>optional</em>) – A random number generator seed from which a numpy RandomState is created. Defaults to <cite>None</cite>.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<p>Here, we create the ordinal variant of ACC (Bunse et al., 2023). This variant consists of the original feature transformation of ACC and of the original loss of ACC, the latter of which is regularized towards smooth solutions.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">qunfold.method.composable</span> <span class="kn">import</span> <span class="p">(</span>
+<span class="gp">>>> </span> <span class="n">ComposableQuantifier</span><span class="p">,</span>
+<span class="gp">>>> </span> <span class="n">TikhonovRegularized</span><span class="p">,</span>
+<span class="gp">>>> </span> <span class="n">LeastSquaresLoss</span><span class="p">,</span>
+<span class="gp">>>> </span> <span class="n">ClassTransformer</span><span class="p">,</span>
+<span class="gp">>>> </span><span class="p">)</span>
+<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span>
+<span class="gp">>>> </span><span class="n">o_acc</span> <span class="o">=</span> <span class="n">ComposableQuantifier</span><span class="p">(</span>
+<span class="gp">>>> </span> <span class="n">TikhonovRegularized</span><span class="p">(</span><span class="n">LeastSquaresLoss</span><span class="p">(),</span> <span class="mf">0.01</span><span class="p">),</span>
+<span class="gp">>>> </span> <span class="n">ClassTransformer</span><span class="p">(</span><span class="n">RandomForestClassifier</span><span class="p">(</span><span class="n">oob_score</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+<span class="gp">>>> </span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Here, we perform hyper-parameter optimization with the ordinal ACC.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">quapy</span><span class="o">.</span><span class="n">model_selection</span><span class="o">.</span><span class="n">GridSearchQ</span><span class="p">(</span>
+<span class="gp">>>> </span> <span class="n">model</span> <span class="o">=</span> <span class="n">o_acc</span><span class="p">,</span>
+<span class="gp">>>> </span> <span class="n">param_grid</span> <span class="o">=</span> <span class="p">{</span> <span class="c1"># try both splitting criteria</span>
+<span class="gp">>>> </span> <span class="s2">"transformer__classifier__estimator__criterion"</span><span class="p">:</span> <span class="p">[</span><span class="s2">"gini"</span><span class="p">,</span> <span class="s2">"entropy"</span><span class="p">],</span>
+<span class="gp">>>> </span> <span class="p">},</span>
+<span class="gp">>>> </span> <span class="c1"># ...</span>
+<span class="gp">>>> </span><span class="p">)</span>
+</pre></div>
+</div>
+<p>To use a classifier that does not provide the <cite>oob_score</cite> argument, such as logistic regression, you have to configure a cross validation of this classifier. Here, we employ 10 cross validation folds. 5 folds are the default.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">qunfold.method.composable</span> <span class="kn">import</span> <span class="n">CVClassifier</span>
+<span class="gp">>>> </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="gp">>>> </span><span class="n">acc_lr</span> <span class="o">=</span> <span class="n">ComposableQuantifier</span><span class="p">(</span>
+<span class="gp">>>> </span> <span class="n">LeastSquaresLoss</span><span class="p">(),</span>
+<span class="gp">>>> </span> <span class="n">ClassTransformer</span><span class="p">(</span><span class="n">CVClassifier</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">(),</span> <span class="mi">10</span><span class="p">))</span>
+<span class="gp">>>> </span><span class="p">)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="quapy.method.composable.DistanceTransformer">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.composable.</span></span><span class="sig-name descname"><span class="pre">DistanceTransformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'euclidean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preprocessor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/qunfold/transformers.html#DistanceTransformer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.DistanceTransformer" title="Permalink to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">AbstractTransformer</span></code></p>
+<p>A distance-based feature transformation, as it is used in <cite>EDx</cite> and <cite>EDy</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>metric</strong> (<em>optional</em>) – The metric with which the distance between data items is measured. Can take any value that is accepted by <cite>scipy.spatial.distance.cdist</cite>. Defaults to <cite>“euclidean”</cite>.</p></li>
+<li><p><strong>preprocessor</strong> (<em>optional</em>) – Another <cite>AbstractTransformer</cite> that is called before this transformer. Defaults to <cite>None</cite>.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="quapy.method.composable.DistanceTransformer.fit_transform">
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/qunfold/transformers.html#DistanceTransformer.fit_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.DistanceTransformer.fit_transform" title="Permalink to this definition"></a></dt>
+<dd><p>This abstract method has to fit the transformer and to return the transformation of the input data.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Implementations of this abstract method should check the sanity of labels by calling <cite>_check_y(y, n_classes)</cite> and they must set the property <cite>self.p_trn = class_prevalences(y, n_classes)</cite>.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> – The feature matrix to which this transformer will be fitted.</p></li>
+<li><p><strong>y</strong> – The labels to which this transformer will be fitted.</p></li>
+<li><p><strong>average</strong> (<em>optional</em>) – Whether to return a transfer matrix <cite>M</cite> or a transformation <cite>(f(X), y)</cite>. Defaults to <cite>True</cite>.</p></li>
+<li><p><strong>n_classes</strong> (<em>optional</em>) – The number of expected classes. Defaults to <cite>None</cite>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>A transfer matrix <cite>M</cite> if <cite>average==True</cite> or a transformation <cite>(f(X), y)</cite> if <cite>average==False</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="quapy.method.composable.DistanceTransformer.transform">
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/qunfold/transformers.html#DistanceTransformer.transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.DistanceTransformer.transform" title="Permalink to this definition"></a></dt>
+<dd><p>This abstract method has to transform the data <cite>X</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> – The feature matrix that will be transformed.</p></li>
+<li><p><strong>average</strong> (<em>optional</em>) – Whether to return a vector <cite>q</cite> or a transformation <cite>f(X)</cite>. Defaults to <cite>True</cite>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>A vector <cite>q = f(X).mean(axis=0)</cite> if <cite>average==True</cite> or a transformation <cite>f(X)</cite> if <cite>average==False</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="quapy.method.composable.EnergyKernelTransformer">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.composable.</span></span><span class="sig-name descname"><span class="pre">EnergyKernelTransformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">preprocessor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/qunfold/transformers.html#EnergyKernelTransformer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.EnergyKernelTransformer" title="Permalink to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">AbstractTransformer</span></code></p>
+<p>A kernel-based feature transformation, as it is used in <cite>KMM</cite>, that uses the <cite>energy</cite> kernel:</p>
+<blockquote>
+<div><p>k(x_1, x_2) = ||x_1|| + ||x_2|| - ||x_1 - x_2||</p>
+</div></blockquote>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The methods of this transformer do not support setting <cite>average=False</cite>.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>preprocessor</strong> (<em>optional</em>) – Another <cite>AbstractTransformer</cite> that is called before this transformer. Defaults to <cite>None</cite>.</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="quapy.method.composable.EnergyKernelTransformer.fit_transform">
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/qunfold/transformers.html#EnergyKernelTransformer.fit_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.EnergyKernelTransformer.fit_transform" title="Permalink to this definition"></a></dt>
+<dd><p>This abstract method has to fit the transformer and to return the transformation of the input data.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Implementations of this abstract method should check the sanity of labels by calling <cite>_check_y(y, n_classes)</cite> and they must set the property <cite>self.p_trn = class_prevalences(y, n_classes)</cite>.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> – The feature matrix to which this transformer will be fitted.</p></li>
+<li><p><strong>y</strong> – The labels to which this transformer will be fitted.</p></li>
+<li><p><strong>average</strong> (<em>optional</em>) – Whether to return a transfer matrix <cite>M</cite> or a transformation <cite>(f(X), y)</cite>. Defaults to <cite>True</cite>.</p></li>
+<li><p><strong>n_classes</strong> (<em>optional</em>) – The number of expected classes. Defaults to <cite>None</cite>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>A transfer matrix <cite>M</cite> if <cite>average==True</cite> or a transformation <cite>(f(X), y)</cite> if <cite>average==False</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="quapy.method.composable.EnergyKernelTransformer.transform">
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/qunfold/transformers.html#EnergyKernelTransformer.transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.EnergyKernelTransformer.transform" title="Permalink to this definition"></a></dt>
+<dd><p>This abstract method has to transform the data <cite>X</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> – The feature matrix that will be transformed.</p></li>
+<li><p><strong>average</strong> (<em>optional</em>) – Whether to return a vector <cite>q</cite> or a transformation <cite>f(X)</cite>. Defaults to <cite>True</cite>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>A vector <cite>q = f(X).mean(axis=0)</cite> if <cite>average==True</cite> or a transformation <cite>f(X)</cite> if <cite>average==False</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="quapy.method.composable.EnergyLoss">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.composable.</span></span><span class="sig-name descname"><span class="pre">EnergyLoss</span></span><a class="reference internal" href="_modules/qunfold/losses.html#EnergyLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.EnergyLoss" title="Permalink to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">FunctionLoss</span></code></p>
+<p>The loss function of EDx (Kawakubo et al., 2016) and EDy (Castaño et al., 2022).</p>
+<p>This loss function represents the Energy Distance between two samples.</p>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="quapy.method.composable.GaussianKernelTransformer">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.composable.</span></span><span class="sig-name descname"><span class="pre">GaussianKernelTransformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preprocessor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/qunfold/transformers.html#GaussianKernelTransformer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.GaussianKernelTransformer" title="Permalink to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">AbstractTransformer</span></code></p>
+<p>A kernel-based feature transformation, as it is used in <cite>KMM</cite>, that uses the <cite>gaussian</cite> kernel:</p>
+<blockquote>
+<div><p>k(x, y) = exp(-||x - y||^2 / (2σ^2))</p>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>sigma</strong> (<em>optional</em>) – A smoothing parameter of the kernel function. Defaults to <cite>1</cite>.</p></li>
+<li><p><strong>preprocessor</strong> (<em>optional</em>) – Another <cite>AbstractTransformer</cite> that is called before this transformer. Defaults to <cite>None</cite>.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="quapy.method.composable.GaussianKernelTransformer.fit_transform">
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/qunfold/transformers.html#GaussianKernelTransformer.fit_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.GaussianKernelTransformer.fit_transform" title="Permalink to this definition"></a></dt>
+<dd><p>This abstract method has to fit the transformer and to return the transformation of the input data.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Implementations of this abstract method should check the sanity of labels by calling <cite>_check_y(y, n_classes)</cite> and they must set the property <cite>self.p_trn = class_prevalences(y, n_classes)</cite>.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> – The feature matrix to which this transformer will be fitted.</p></li>
+<li><p><strong>y</strong> – The labels to which this transformer will be fitted.</p></li>
+<li><p><strong>average</strong> (<em>optional</em>) – Whether to return a transfer matrix <cite>M</cite> or a transformation <cite>(f(X), y)</cite>. Defaults to <cite>True</cite>.</p></li>
+<li><p><strong>n_classes</strong> (<em>optional</em>) – The number of expected classes. Defaults to <cite>None</cite>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>A transfer matrix <cite>M</cite> if <cite>average==True</cite> or a transformation <cite>(f(X), y)</cite> if <cite>average==False</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="quapy.method.composable.GaussianKernelTransformer.transform">
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/qunfold/transformers.html#GaussianKernelTransformer.transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.GaussianKernelTransformer.transform" title="Permalink to this definition"></a></dt>
+<dd><p>This abstract method has to transform the data <cite>X</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> – The feature matrix that will be transformed.</p></li>
+<li><p><strong>average</strong> (<em>optional</em>) – Whether to return a vector <cite>q</cite> or a transformation <cite>f(X)</cite>. Defaults to <cite>True</cite>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>A vector <cite>q = f(X).mean(axis=0)</cite> if <cite>average==True</cite> or a transformation <cite>f(X)</cite> if <cite>average==False</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="quapy.method.composable.GaussianRFFKernelTransformer">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.composable.</span></span><span class="sig-name descname"><span class="pre">GaussianRFFKernelTransformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_rff</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preprocessor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/qunfold/transformers.html#GaussianRFFKernelTransformer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.GaussianRFFKernelTransformer" title="Permalink to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">AbstractTransformer</span></code></p>
+<p>An efficient approximation of the <cite>GaussianKernelTransformer</cite>, as it is used in <cite>KMM</cite>, using random Fourier features.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>sigma</strong> (<em>optional</em>) – A smoothing parameter of the kernel function. Defaults to <cite>1</cite>.</p></li>
+<li><p><strong>n_rff</strong> (<em>optional</em>) – The number of random Fourier features. Defaults to <cite>1000</cite>.</p></li>
+<li><p><strong>preprocessor</strong> (<em>optional</em>) – Another <cite>AbstractTransformer</cite> that is called before this transformer. Defaults to <cite>None</cite>.</p></li>
+<li><p><strong>seed</strong> (<em>optional</em>) – Controls the randomness of the random Fourier features. Defaults to <cite>None</cite>.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="quapy.method.composable.GaussianRFFKernelTransformer.fit_transform">
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/qunfold/transformers.html#GaussianRFFKernelTransformer.fit_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.GaussianRFFKernelTransformer.fit_transform" title="Permalink to this definition"></a></dt>
+<dd><p>This abstract method has to fit the transformer and to return the transformation of the input data.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Implementations of this abstract method should check the sanity of labels by calling <cite>_check_y(y, n_classes)</cite> and they must set the property <cite>self.p_trn = class_prevalences(y, n_classes)</cite>.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> – The feature matrix to which this transformer will be fitted.</p></li>
+<li><p><strong>y</strong> – The labels to which this transformer will be fitted.</p></li>
+<li><p><strong>average</strong> (<em>optional</em>) – Whether to return a transfer matrix <cite>M</cite> or a transformation <cite>(f(X), y)</cite>. Defaults to <cite>True</cite>.</p></li>
+<li><p><strong>n_classes</strong> (<em>optional</em>) – The number of expected classes. Defaults to <cite>None</cite>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>A transfer matrix <cite>M</cite> if <cite>average==True</cite> or a transformation <cite>(f(X), y)</cite> if <cite>average==False</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="quapy.method.composable.GaussianRFFKernelTransformer.transform">
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/qunfold/transformers.html#GaussianRFFKernelTransformer.transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.GaussianRFFKernelTransformer.transform" title="Permalink to this definition"></a></dt>
+<dd><p>This abstract method has to transform the data <cite>X</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> – The feature matrix that will be transformed.</p></li>
+<li><p><strong>average</strong> (<em>optional</em>) – Whether to return a vector <cite>q</cite> or a transformation <cite>f(X)</cite>. Defaults to <cite>True</cite>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>A vector <cite>q = f(X).mean(axis=0)</cite> if <cite>average==True</cite> or a transformation <cite>f(X)</cite> if <cite>average==False</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="quapy.method.composable.HellingerSurrogateLoss">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.composable.</span></span><span class="sig-name descname"><span class="pre">HellingerSurrogateLoss</span></span><a class="reference internal" href="_modules/qunfold/losses.html#HellingerSurrogateLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.HellingerSurrogateLoss" title="Permalink to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">FunctionLoss</span></code></p>
+<p>The loss function of HDx and HDy (González-Castro et al., 2013).</p>
+<p>This loss function computes the average of the squared Hellinger distances between feature-wise (or class-wise) histograms. Note that the original HDx and HDy by González-Castro et al (2013) do not use the squared but the regular Hellinger distance. Their approach is problematic because the regular distance is not always twice differentiable and, hence, complicates numerical optimizations.</p>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="quapy.method.composable.HistogramTransformer">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.composable.</span></span><span class="sig-name descname"><span class="pre">HistogramTransformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_bins</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preprocessor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unit_scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/qunfold/transformers.html#HistogramTransformer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.HistogramTransformer" title="Permalink to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">AbstractTransformer</span></code></p>
+<p>A histogram-based feature transformation, as it is used in <cite>HDx</cite> and <cite>HDy</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>n_bins</strong> – The number of bins in each feature.</p></li>
+<li><p><strong>preprocessor</strong> (<em>optional</em>) – Another <cite>AbstractTransformer</cite> that is called before this transformer. Defaults to <cite>None</cite>.</p></li>
+<li><p><strong>unit_scale</strong> (<em>optional</em>) – Whether or not to scale each output to a sum of one. A value of <cite>False</cite> indicates that the sum of each output is the number of features. Defaults to <cite>True</cite>.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="quapy.method.composable.HistogramTransformer.fit_transform">
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/qunfold/transformers.html#HistogramTransformer.fit_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.HistogramTransformer.fit_transform" title="Permalink to this definition"></a></dt>
+<dd><p>This abstract method has to fit the transformer and to return the transformation of the input data.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Implementations of this abstract method should check the sanity of labels by calling <cite>_check_y(y, n_classes)</cite> and they must set the property <cite>self.p_trn = class_prevalences(y, n_classes)</cite>.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> – The feature matrix to which this transformer will be fitted.</p></li>
+<li><p><strong>y</strong> – The labels to which this transformer will be fitted.</p></li>
+<li><p><strong>average</strong> (<em>optional</em>) – Whether to return a transfer matrix <cite>M</cite> or a transformation <cite>(f(X), y)</cite>. Defaults to <cite>True</cite>.</p></li>
+<li><p><strong>n_classes</strong> (<em>optional</em>) – The number of expected classes. Defaults to <cite>None</cite>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>A transfer matrix <cite>M</cite> if <cite>average==True</cite> or a transformation <cite>(f(X), y)</cite> if <cite>average==False</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="quapy.method.composable.HistogramTransformer.transform">
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/qunfold/transformers.html#HistogramTransformer.transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.HistogramTransformer.transform" title="Permalink to this definition"></a></dt>
+<dd><p>This abstract method has to transform the data <cite>X</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> – The feature matrix that will be transformed.</p></li>
+<li><p><strong>average</strong> (<em>optional</em>) – Whether to return a vector <cite>q</cite> or a transformation <cite>f(X)</cite>. Defaults to <cite>True</cite>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>A vector <cite>q = f(X).mean(axis=0)</cite> if <cite>average==True</cite> or a transformation <cite>f(X)</cite> if <cite>average==False</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="quapy.method.composable.KernelTransformer">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.composable.</span></span><span class="sig-name descname"><span class="pre">KernelTransformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/qunfold/transformers.html#KernelTransformer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.KernelTransformer" title="Permalink to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">AbstractTransformer</span></code></p>
+<p>A general kernel-based feature transformation, as it is used in <cite>KMM</cite>. If you intend to use a Gaussian kernel or energy kernel, prefer their dedicated and more efficient implementations over this class.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The methods of this transformer do not support setting <cite>average=False</cite>.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>kernel</strong> – A callable that will be used as the kernel. Must follow the signature <cite>(X[y==i], X[y==j]) -> scalar</cite>.</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="quapy.method.composable.KernelTransformer.fit_transform">
+<span class="sig-name descname"><span class="pre">fit_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/qunfold/transformers.html#KernelTransformer.fit_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.KernelTransformer.fit_transform" title="Permalink to this definition"></a></dt>
+<dd><p>This abstract method has to fit the transformer and to return the transformation of the input data.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Implementations of this abstract method should check the sanity of labels by calling <cite>_check_y(y, n_classes)</cite> and they must set the property <cite>self.p_trn = class_prevalences(y, n_classes)</cite>.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> – The feature matrix to which this transformer will be fitted.</p></li>
+<li><p><strong>y</strong> – The labels to which this transformer will be fitted.</p></li>
+<li><p><strong>average</strong> (<em>optional</em>) – Whether to return a transfer matrix <cite>M</cite> or a transformation <cite>(f(X), y)</cite>. Defaults to <cite>True</cite>.</p></li>
+<li><p><strong>n_classes</strong> (<em>optional</em>) – The number of expected classes. Defaults to <cite>None</cite>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>A transfer matrix <cite>M</cite> if <cite>average==True</cite> or a transformation <cite>(f(X), y)</cite> if <cite>average==False</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="quapy.method.composable.KernelTransformer.transform">
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/qunfold/transformers.html#KernelTransformer.transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.KernelTransformer.transform" title="Permalink to this definition"></a></dt>
+<dd><p>This abstract method has to transform the data <cite>X</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>X</strong> – The feature matrix that will be transformed.</p></li>
+<li><p><strong>average</strong> (<em>optional</em>) – Whether to return a vector <cite>q</cite> or a transformation <cite>f(X)</cite>. Defaults to <cite>True</cite>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>A vector <cite>q = f(X).mean(axis=0)</cite> if <cite>average==True</cite> or a transformation <cite>f(X)</cite> if <cite>average==False</cite>.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="quapy.method.composable.LaplacianKernelTransformer">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.composable.</span></span><span class="sig-name descname"><span class="pre">LaplacianKernelTransformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/qunfold/transformers.html#LaplacianKernelTransformer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.LaplacianKernelTransformer" title="Permalink to this definition"></a></dt>
+<dd><p>Bases: <a class="reference internal" href="#quapy.method.composable.KernelTransformer" title="qunfold.transformers.KernelTransformer"><code class="xref py py-class docutils literal notranslate"><span class="pre">KernelTransformer</span></code></a></p>
+<p>A kernel-based feature transformation, as it is used in <cite>KMM</cite>, that uses the <cite>laplacian</cite> kernel.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>sigma</strong> (<em>optional</em>) – A smoothing parameter of the kernel function. Defaults to <cite>1</cite>.</p>
+</dd>
+</dl>
+<dl class="py property">
+<dt class="sig sig-object py" id="quapy.method.composable.LaplacianKernelTransformer.kernel">
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">kernel</span></span><a class="headerlink" href="#quapy.method.composable.LaplacianKernelTransformer.kernel" title="Permalink to this definition"></a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="quapy.method.composable.LeastSquaresLoss">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.composable.</span></span><span class="sig-name descname"><span class="pre">LeastSquaresLoss</span></span><a class="reference internal" href="_modules/qunfold/losses.html#LeastSquaresLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.LeastSquaresLoss" title="Permalink to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">FunctionLoss</span></code></p>
+<p>The loss function of ACC (Forman, 2008), PACC (Bella et al., 2019), and ReadMe (Hopkins & King, 2010).</p>
+<p>This loss function computes the sum of squares of element-wise errors between <cite>q</cite> and <cite>M*p</cite>.</p>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="quapy.method.composable.TikhonovRegularization">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.composable.</span></span><span class="sig-name descname"><span class="pre">TikhonovRegularization</span></span><a class="reference internal" href="_modules/qunfold/losses.html#TikhonovRegularization"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.TikhonovRegularization" title="Permalink to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">AbstractLoss</span></code></p>
+<p>Tikhonov regularization, as proposed by Blobel (1985).</p>
+<p>This regularization promotes smooth solutions. This behavior is often required in ordinal quantification and in unfolding problems.</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="quapy.method.composable.TikhonovRegularized">
+<span class="sig-prename descclassname"><span class="pre">quapy.method.composable.</span></span><span class="sig-name descname"><span class="pre">TikhonovRegularized</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/qunfold/losses.html#TikhonovRegularized"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.composable.TikhonovRegularized" title="Permalink to this definition"></a></dt>
+<dd><p>Add TikhonovRegularization (Blobel, 1985) to any loss.</p>
+<p>Calling this function is equivalent to calling</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">CombinedLoss</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">TikhonovRegularization</span><span class="p">(),</span> <span class="n">weights</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="n">tau</span><span class="p">])</span>
+</pre></div>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>loss</strong> – An instance from <cite>qunfold.losses</cite>.</p></li>
+<li><p><strong>tau</strong> (<em>optional</em>) – The regularization strength. Defaults to 0.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>An instance of <cite>CombinedLoss</cite>.</p>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<p>The regularized loss of RUN (Blobel, 1985) is:</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">TikhonovRegularization</span><span class="p">(</span><span class="n">BlobelLoss</span><span class="p">(),</span> <span class="n">tau</span><span class="p">)</span>
+</pre></div>
+</div>
+</dd></dl>
+
</section>
<section id="module-quapy.method">
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-quapy.method" title="Permalink to this heading"></a></h2>
diff --git a/docs/build/html/search.html b/docs/build/html/search.html
index bf88dae..519edd4 100644
--- a/docs/build/html/search.html
+++ b/docs/build/html/search.html
@@ -46,6 +46,15 @@
</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="wiki/Datasets.html">Datasets</a></li>
+<li class="toctree-l1"><a class="reference internal" href="wiki/Evaluation.html">Evaluation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="wiki/ExplicitLossMinimization.html">Explicit Loss Minimization</a></li>
+<li class="toctree-l1"><a class="reference internal" href="wiki/Methods.html">Quantification Methods</a></li>
+<li class="toctree-l1"><a class="reference internal" href="wiki/Model-Selection.html">Model Selection</a></li>
+<li class="toctree-l1"><a class="reference internal" href="wiki/Plotting.html">Plotting</a></li>
+<li class="toctree-l1"><a class="reference internal" href="wiki/Protocols.html">Protocols</a></li>
+</ul>
+<ul>
<li class="toctree-l1"><a class="reference internal" href="modules.html">quapy</a></li>
</ul>
diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
index 57c32b1..e411003 100644
--- a/docs/build/html/searchindex.js
+++ b/docs/build/html/searchindex.js
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"datasets"]], "Reviews Datasets": [[6, "reviews-datasets"]], "Twitter Sentiment Datasets": [[6, "twitter-sentiment-datasets"]], "UCI Machine Learning": [[6, "uci-machine-learning"]], "Binary datasets": [[6, "binary-datasets"]], "Issues:": [[6, "issues"]], "Multiclass datasets": [[6, "multiclass-datasets"]], "LeQua 2022 Datasets": [[6, "lequa-2022-datasets"]], "IFCB Plankton dataset": [[6, "ifcb-plankton-dataset"]], "Adding Custom Datasets": [[6, "adding-custom-datasets"]], "Data Processing": [[6, "data-processing"]], "Evaluation": [[7, "evaluation"]], "Error Measures": [[7, "error-measures"]], "Evaluation Protocols": [[7, "evaluation-protocols"]], "Explicit Loss Minimization": [[8, "explicit-loss-minimization"], [10, "explicit-loss-minimization"]], "Quantification Methods": [[10, "quantification-methods"]], "Aggregative Methods": [[10, "aggregative-methods"]], "The Classify & Count variants": [[10, "the-classify-count-variants"]], "BayesianCC (New in v0.1.9!)": [[10, 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"Sampling from the unit-simplex, the Uniform-Prevalence Protocol (UPP)": [[13, "sampling-from-the-unit-simplex-the-uniform-prevalence-protocol-upp"]], "Natural-Prevalence Protocol": [[13, "natural-prevalence-protocol"]], "Other protocols": [[13, "other-protocols"]]}, "indexentries": {}})
\ No newline at end of file
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 734acf2..01c305d 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -10,8 +10,10 @@ import pathlib
import sys
from os.path import join
quapy_path = join(pathlib.Path(__file__).parents[2].resolve().as_posix(), 'quapy')
+wiki_path = join(pathlib.Path(__file__).parents[0].resolve().as_posix(), 'wiki')
print(f'quapy path={quapy_path}')
sys.path.insert(0, quapy_path)
+sys.path.insert(0, wiki_path)
project = 'QuaPy: A Python-based open-source framework for quantification'
diff --git a/docs/source/index.rst b/docs/source/index.rst
index cc5b4dc..a4150cd 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -21,6 +21,23 @@ GitHub
QuaPy is hosted in GitHub at `https://github.com/HLT-ISTI/QuaPy <https://github.com/HLT-ISTI/QuaPy>`_
+Wiki Documents
+------------
+
+In this section you can find useful information concerning different aspects of QuaPy, with examples:
+
+.. toctree::
+ :maxdepth: 1
+
+ wiki/Datasets
+ wiki/Evaluation
+ wiki/ExplicitLossMinimization
+ wiki/Methods
+ wiki/Model-Selection
+ wiki/Plotting
+ wiki/Protocols
+
+
.. toctree::
:maxdepth: 2
:caption: Contents:
diff --git a/docs/source/wiki_editable/Datasets.md b/docs/source/wiki_editable/Datasets.md
new file mode 100644
index 0000000..904fe53
--- /dev/null
+++ b/docs/source/wiki_editable/Datasets.md
@@ -0,0 +1,440 @@
+# Datasets
+
+QuaPy makes available several datasets that have been used in
+quantification literature, as well as an interface to allow
+anyone import their custom datasets.
+
+A _Dataset_ object in QuaPy is roughly a pair of _LabelledCollection_ objects,
+one playing the role of the training set, another the test set.
+_LabelledCollection_ is a data class consisting of the (iterable)
+instances and labels. This class handles most of the sampling functionality in QuaPy.
+Take a look at the following code:
+
+```python
+
+import quapy as qp
+import quapy.functional as F
+
+instances = [
+ '1st positive document', '2nd positive document',
+ 'the only negative document',
+ '1st neutral document', '2nd neutral document', '3rd neutral document'
+]
+labels = [2, 2, 0, 1, 1, 1]
+
+data = qp.data.LabelledCollection(instances, labels)
+print(F.strprev(data.prevalence(), prec=2))
+
+```
+
+Output the class prevalences (showing 2 digit precision):
+```
+[0.17, 0.50, 0.33]
+```
+
+One can easily produce new samples at desired class prevalence values:
+
+```python
+sample_size = 10
+prev = [0.4, 0.1, 0.5]
+sample = data.sampling(sample_size, *prev)
+
+print('instances:', sample.instances)
+print('labels:', sample.labels)
+print('prevalence:', F.strprev(sample.prevalence(), prec=2))
+```
+
+Which outputs:
+```
+instances: ['the only negative document' '2nd positive document'
+ '2nd positive document' '2nd neutral document' '1st positive document'
+ 'the only negative document' 'the only negative document'
+ 'the only negative document' '2nd positive document'
+ '1st positive document']
+labels: [0 2 2 1 2 0 0 0 2 2]
+prevalence: [0.40, 0.10, 0.50]
+```
+
+Samples can be made consistent across different runs (e.g., to test
+different methods on the same exact samples) by sampling and retaining
+the indexes, that can then be used to generate the sample:
+
+```python
+index = data.sampling_index(sample_size, *prev)
+for method in methods:
+ sample = data.sampling_from_index(index)
+ ...
+```
+
+However, generating samples for evaluation purposes is tackled in QuaPy
+by means of the evaluation protocols (see the dedicated entries in the Wiki
+for [evaluation](https://github.com/HLT-ISTI/QuaPy/wiki/Evaluation) and
+[protocols](https://github.com/HLT-ISTI/QuaPy/wiki/Protocols)).
+
+
+## Reviews Datasets
+
+Three datasets of reviews about Kindle devices, Harry Potter's series, and
+the well-known IMDb movie reviews can be fetched using a unified interface.
+For example:
+
+```python
+import quapy as qp
+data = qp.datasets.fetch_reviews('kindle')
+```
+
+These datasets have been used in:
+```
+Esuli, A., Moreo, A., & Sebastiani, F. (2018, October).
+A recurrent neural network for sentiment quantification.
+In Proceedings of the 27th ACM International Conference on
+Information and Knowledge Management (pp. 1775-1778).
+```
+
+The list of reviews ids is available in:
+
+```python
+qp.datasets.REVIEWS_SENTIMENT_DATASETS
+```
+
+Some statistics of the fhe available datasets are summarized below:
+
+| Dataset | classes | train size | test size | train prev | test prev | type |
+|---|:---:|:---:|:---:|:---:|:---:|---|
+| hp | 2 | 9533 | 18399 | \[0.018, 0.982\] | \[0.065, 0.935\] | text |
+| kindle | 2 | 3821 | 21591 | \[0.081, 0.919\] | \[0.063, 0.937\] | text |
+| imdb | 2 | 25000 | 25000 | \[0.500, 0.500\] | \[0.500, 0.500\] | text |
+
+## Twitter Sentiment Datasets
+
+11 Twitter datasets for sentiment analysis.
+Text is not accessible, and the documents were made available
+in tf-idf format. Each dataset presents two splits: a train/val
+split for model selection purposes, and a train+val/test split
+for model evaluation. The following code exemplifies how to load
+a twitter dataset for model selection.
+
+```python
+import quapy as qp
+data = qp.datasets.fetch_twitter('gasp', for_model_selection=True)
+```
+
+The datasets were used in:
+
+```
+Gao, W., & Sebastiani, F. (2015, August).
+Tweet sentiment: From classification to quantification.
+In 2015 IEEE/ACM International Conference on Advances in
+Social Networks Analysis and Mining (ASONAM) (pp. 97-104). IEEE.
+```
+
+Three of the datasets (semeval13, semeval14, and semeval15) share the
+same training set (semeval), meaning that the training split one would get
+when requesting any of them is the same. The dataset "semeval" can only
+be requested with "for_model_selection=True".
+The lists of the Twitter dataset's ids can be consulted in:
+
+```python
+# a list of 11 dataset ids that can be used for model selection or model evaluation
+qp.datasets.TWITTER_SENTIMENT_DATASETS_TEST
+
+# 9 dataset ids in which "semeval13", "semeval14", and "semeval15" are replaced with "semeval"
+qp.datasets.TWITTER_SENTIMENT_DATASETS_TRAIN
+```
+
+Some details can be found below:
+
+| Dataset | classes | train size | test size | features | train prev | test prev | type |
+|---|:---:|:---:|:---:|:---:|:---:|:---:|---|
+| gasp | 3 | 8788 | 3765 | 694582 | [0.421, 0.496, 0.082] | [0.407, 0.507, 0.086] | sparse |
+| hcr | 3 | 1594 | 798 | 222046 | [0.546, 0.211, 0.243] | [0.640, 0.167, 0.193] | sparse |
+| omd | 3 | 1839 | 787 | 199151 | [0.463, 0.271, 0.266] | [0.437, 0.283, 0.280] | sparse |
+| sanders | 3 | 2155 | 923 | 229399 | [0.161, 0.691, 0.148] | [0.164, 0.688, 0.148] | sparse |
+| semeval13 | 3 | 11338 | 3813 | 1215742 | [0.159, 0.470, 0.372] | [0.158, 0.430, 0.412] | sparse |
+| semeval14 | 3 | 11338 | 1853 | 1215742 | [0.159, 0.470, 0.372] | [0.109, 0.361, 0.530] | sparse |
+| semeval15 | 3 | 11338 | 2390 | 1215742 | [0.159, 0.470, 0.372] | [0.153, 0.413, 0.434] | sparse |
+| semeval16 | 3 | 8000 | 2000 | 889504 | [0.157, 0.351, 0.492] | [0.163, 0.341, 0.497] | sparse |
+| sst | 3 | 2971 | 1271 | 376132 | [0.261, 0.452, 0.288] | [0.207, 0.481, 0.312] | sparse |
+| wa | 3 | 2184 | 936 | 248563 | [0.305, 0.414, 0.281] | [0.282, 0.446, 0.272] | sparse |
+| wb | 3 | 4259 | 1823 | 404333 | [0.270, 0.392, 0.337] | [0.274, 0.392, 0.335] | sparse |
+
+
+## UCI Machine Learning
+
+### Binary datasets
+
+A set of 32 datasets from the [UCI Machine Learning repository](https://archive.ics.uci.edu/ml/datasets.php)
+used in:
+
+```
+Pérez-Gállego, P., Quevedo, J. R., & del Coz, J. J. (2017).
+Using ensembles for problems with characterizable changes
+in data distribution: A case study on quantification.
+Information Fusion, 34, 87-100.
+```
+
+The list does not exactly coincide with that used in Pérez-Gállego et al. 2017
+since we were unable to find the datasets with ids "diabetes" and "phoneme".
+
+These dataset can be loaded by calling, e.g.:
+
+```python
+import quapy as qp
+
+data = qp.datasets.fetch_UCIBinaryDataset('yeast', verbose=True)
+```
+
+This call will return a _Dataset_ object in which the training and
+test splits are randomly drawn, in a stratified manner, from the whole
+collection at 70% and 30%, respectively. The _verbose=True_ option indicates
+that the dataset description should be printed in standard output.
+The original data is not split,
+and some papers submit the entire collection to a kFCV validation.
+In order to accommodate with these practices, one could first instantiate
+the entire collection, and then creating a generator that will return one
+training+test dataset at a time, following a kFCV protocol:
+
+```python
+import quapy as qp
+
+collection = qp.datasets.fetch_UCIBinaryLabelledCollection("yeast")
+for data in qp.data.Dataset.kFCV(collection, nfolds=5, nrepeats=2):
+ ...
+```
+
+Above code will allow to conduct a 2x5FCV evaluation on the "yeast" dataset.
+
+All datasets come in numerical form (dense matrices); some statistics
+are summarized below.
+
+| Dataset | classes | instances | features | prev | type |
+|---|:---:|:---:|:---:|:---:|---|
+| acute.a | 2 | 120 | 6 | [0.508, 0.492] | dense |
+| acute.b | 2 | 120 | 6 | [0.583, 0.417] | dense |
+| balance.1 | 2 | 625 | 4 | [0.539, 0.461] | dense |
+| balance.2 | 2 | 625 | 4 | [0.922, 0.078] | dense |
+| balance.3 | 2 | 625 | 4 | [0.539, 0.461] | dense |
+| breast-cancer | 2 | 683 | 9 | [0.350, 0.650] | dense |
+| cmc.1 | 2 | 1473 | 9 | [0.573, 0.427] | dense |
+| cmc.2 | 2 | 1473 | 9 | [0.774, 0.226] | dense |
+| cmc.3 | 2 | 1473 | 9 | [0.653, 0.347] | dense |
+| ctg.1 | 2 | 2126 | 22 | [0.222, 0.778] | dense |
+| ctg.2 | 2 | 2126 | 22 | [0.861, 0.139] | dense |
+| ctg.3 | 2 | 2126 | 22 | [0.917, 0.083] | dense |
+| german | 2 | 1000 | 24 | [0.300, 0.700] | dense |
+| haberman | 2 | 306 | 3 | [0.735, 0.265] | dense |
+| ionosphere | 2 | 351 | 34 | [0.641, 0.359] | dense |
+| iris.1 | 2 | 150 | 4 | [0.667, 0.333] | dense |
+| iris.2 | 2 | 150 | 4 | [0.667, 0.333] | dense |
+| iris.3 | 2 | 150 | 4 | [0.667, 0.333] | dense |
+| mammographic | 2 | 830 | 5 | [0.514, 0.486] | dense |
+| pageblocks.5 | 2 | 5473 | 10 | [0.979, 0.021] | dense |
+| semeion | 2 | 1593 | 256 | [0.901, 0.099] | dense |
+| sonar | 2 | 208 | 60 | [0.534, 0.466] | dense |
+| spambase | 2 | 4601 | 57 | [0.606, 0.394] | dense |
+| spectf | 2 | 267 | 44 | [0.794, 0.206] | dense |
+| tictactoe | 2 | 958 | 9 | [0.653, 0.347] | dense |
+| transfusion | 2 | 748 | 4 | [0.762, 0.238] | dense |
+| wdbc | 2 | 569 | 30 | [0.627, 0.373] | dense |
+| wine.1 | 2 | 178 | 13 | [0.669, 0.331] | dense |
+| wine.2 | 2 | 178 | 13 | [0.601, 0.399] | dense |
+| wine.3 | 2 | 178 | 13 | [0.730, 0.270] | dense |
+| wine-q-red | 2 | 1599 | 11 | [0.465, 0.535] | dense |
+| wine-q-white | 2 | 4898 | 11 | [0.335, 0.665] | dense |
+| yeast | 2 | 1484 | 8 | [0.711, 0.289] | dense |
+
+### Issues:
+All datasets will be downloaded automatically the first time they are requested, and
+stored in the _quapy_data_ folder for faster further reuse.
+However, some datasets require special actions that at the moment are not fully
+automated.
+
+* Datasets with ids "ctg.1", "ctg.2", and "ctg.3" (_Cardiotocography Data Set_) load
+an Excel file, which requires the user to install the _xlrd_ Python module in order
+to open it.
+* The dataset with id "pageblocks.5" (_Page Blocks Classification (5)_) needs to
+open a "unix compressed file" (extension .Z), which is not directly doable with
+standard Pythons packages like gzip or zip. This file would need to be uncompressed using
+OS-dependent software manually. Information on how to do it will be printed the first
+time the dataset is invoked.
+* It is a good idea to ignore datasets _acute.a_, _acute.b_ and _balance.2_, since the former two
+are very easy (many classifiers would score 100% accuracy) while the latter is extremely difficult
+ (probably there is some problem with this dataset, the errors it tends to produce are orders of magnitude
+greater than for other datasets, and this has a disproportionate impact in the average performance).
+
+### Multiclass datasets
+
+A collection of 5 multiclass datasets from the [UCI Machine Learning repository](https://archive.ics.uci.edu/ml/datasets.php). The datasets were first used
+in [this paper](https://arxiv.org/abs/2401.00490) and can be instantiated as follows:
+
+```python
+import quapy as qp
+data = qp.datasets.fetch_UCIMulticlassLabelledCollection('dry-bean', test_split=0.4, verbose=True)
+```
+
+There are no pre-defined train-test partitions for these datasets, but you can easily create your own with the
+`split_stratified` method, e.g., `data.split_stratified()`. This is equivalent
+to calling the following method directly:
+
+```python
+data = qp.datasets.fetch_UCIMulticlassDataset('dry-bean', min_test_split=0.4, verbose=True)
+```
+
+The datasets correspond to all the datasets that can be retrieved from the platform
+using the following filters:
+* datasets for classification
+* more than 2 classes
+* containing at least 1,000 instances
+* can be imported using the Python API.
+
+Some statistics about these datasets are displayed below:
+
+| **Dataset** | **classes** | **train size** | **test size** |
+|------------------|:-----------:|:--------------:|:-------------:|
+| dry-bean | 7 | 9527 | 4084 |
+| wine-quality | 7 | 3428 | 1470 |
+| academic-success | 3 | 3096 | 1328 |
+| digits | 10 | 3933 | 1687 |
+| letter | 26 | 14000 | 6000 |
+
+
+## LeQua 2022 Datasets
+
+QuaPy also provides the datasets used for the LeQua 2022 competition.
+In brief, there are 4 tasks (T1A, T1B, T2A, T2B) having to do with text quantification
+problems. Tasks T1A and T1B provide documents in vector form, while T2A and T2B provide
+raw documents instead.
+Tasks T1A and T2A are binary sentiment quantification problems, while T2A and T2B
+are multiclass quantification problems consisting of estimating the class prevalence
+values of 28 different merchandise products.
+
+Every task consists of a training set, a set of validation samples (for model selection)
+and a set of test samples (for evaluation). QuaPy returns this data as a LabelledCollection
+(training) and two generation protocols (for validation and test samples), as follows:
+
+```python
+training, val_generator, test_generator = fetch_lequa2022(task=task)
+```
+
+See the `lequa2022_experiments.py` in the examples folder for further details on how to
+carry out experiments using these datasets.
+
+The datasets are downloaded only once, and stored for fast reuse.
+
+Some statistics are summarized below:
+
+| Dataset | classes | train size | validation samples | test samples | docs by sample | type |
+|---------|:-------:|:----------:|:------------------:|:------------:|:----------------:|:--------:|
+| T1A | 2 | 5000 | 1000 | 5000 | 250 | vector |
+| T1B | 28 | 20000 | 1000 | 5000 | 1000 | vector |
+| T2A | 2 | 5000 | 1000 | 5000 | 250 | text |
+| T2B | 28 | 20000 | 1000 | 5000 | 1000 | text |
+
+For further details on the datasets, we refer to the original
+[paper](https://ceur-ws.org/Vol-3180/paper-146.pdf):
+
+```
+Esuli, A., Moreo, A., Sebastiani, F., & Sperduti, G. (2022).
+A Detailed Overview of LeQua@ CLEF 2022: Learning to Quantify.
+```
+
+## IFCB Plankton dataset
+
+IFCB is a dataset of plankton species in water samples hosted in `Zenodo <https://zenodo.org/records/10036244>`_.
+This dataset is based on the data available publicly at `WHOI-Plankton repo <https://github.com/hsosik/WHOI-Plankton>`_
+and in the scripts for the processing are available at `P. González's repo <https://github.com/pglez82/IFCB_Zenodo>`_.
+
+This dataset comes with precomputed features for testing quantification algorithms.
+
+Some statistics:
+
+| | **Training** | **Validation** | **Test** |
+|-----------------|:------------:|:--------------:|:--------:|
+| samples | 200 | 86 | 678 |
+| total instances | 584474 | 246916 | 2626429 |
+| mean per sample | 2922.3 | 2871.1 | 3873.8 |
+| min per sample | 266 | 59 | 33 |
+| max per sample | 6645 | 7375 | 9112 |
+
+The number of features is 512, while the number of classes is 50.
+In terms of prevalence, the mean is 0.020, the minimum is 0, and the maximum is 0.978.
+
+The dataset can be loaded for model selection (`for_model_selection=True`, thus returning the training and validation)
+or for test (`for_model_selection=False`, thus returning the training+validation and the test).
+
+Additionally, the training can be interpreted as a list (a generator) of samples (`single_sample_train=False`)
+or as a single training set (`single_sample_train=True`).
+
+Example:
+
+```python
+train, val_gen = qp.datasets.fetch_IFCB(for_model_selection=True, single_sample_train=True)
+# ... model selection
+
+train, test_gen = qp.datasets.fetch_IFCB(for_model_selection=False, single_sample_train=True)
+# ... train and evaluation
+```
+
+
+
+## Adding Custom Datasets
+
+QuaPy provides data loaders for simple formats dealing with
+text, following the format:
+
+```
+class-id \t first document's pre-processed text \n
+class-id \t second document's pre-processed text \n
+...
+```
+
+and sparse representations of the form:
+
+```
+{-1, 0, or +1} col(int):val(float) col(int):val(float) ... \n
+...
+```
+
+The code in charge in loading a LabelledCollection is:
+
+```python
+@classmethod
+def load(cls, path:str, loader_func:callable):
+ return LabelledCollection(*loader_func(path))
+```
+
+indicating that any _loader_func_ (e.g., a user-defined one) which
+returns valid arguments for initializing a _LabelledCollection_ object will allow
+to load any collection. In particular, the _LabelledCollection_ receives as
+arguments the instances (as an iterable) and the labels (as an iterable) and,
+additionally, the number of classes can be specified (it would otherwise be
+inferred from the labels, but that requires at least one positive example for
+all classes to be present in the collection).
+
+The same _loader_func_ can be passed to a Dataset, along with two
+paths, in order to create a training and test pair of _LabelledCollection_,
+e.g.:
+
+```python
+import quapy as qp
+
+train_path = '../my_data/train.dat'
+test_path = '../my_data/test.dat'
+
+def my_custom_loader(path):
+ with open(path, 'rb') as fin:
+ ...
+ return instances, labels
+
+data = qp.data.Dataset.load(train_path, test_path, my_custom_loader)
+```
+
+### Data Processing
+
+QuaPy implements a number of preprocessing functions in the package _qp.data.preprocessing_, including:
+
+* _text2tfidf_: tfidf vectorization
+* _reduce_columns_: reducing the number of columns based on term frequency
+* _standardize_: transforms the column values into z-scores (i.e., subtract the mean and normalizes by the standard deviation, so
+that the column values have zero mean and unit variance).
+* _index_: transforms textual tokens into lists of numeric ids)
diff --git a/docs/source/wiki_editable/Evaluation.md b/docs/source/wiki_editable/Evaluation.md
new file mode 100644
index 0000000..d9c1b79
--- /dev/null
+++ b/docs/source/wiki_editable/Evaluation.md
@@ -0,0 +1,159 @@
+# Evaluation
+
+Quantification is an appealing tool in scenarios of dataset shift,
+and particularly in scenarios of prior-probability shift.
+That is, the interest in estimating the class prevalences arises
+under the belief that those class prevalences might have changed
+with respect to the ones observed during training.
+In other words, one could simply return the training prevalence
+as a predictor of the test prevalence if this change is assumed
+to be unlikely (as is the case in general scenarios of
+machine learning governed by the iid assumption).
+In brief, quantification requires dedicated evaluation protocols,
+which are implemented in QuaPy and explained here.
+
+## Error Measures
+
+The module quapy.error implements the most popular error measures for quantification, e.g., mean absolute error (_mae_), mean relative absolute error (_mrae_), among others. For each such measure (e.g., _mrae_) there are corresponding functions (e.g., _rae_) that do not average the results across samples.
+
+Some errors of classification are also available, e.g., accuracy error (_acce_) or F-1 error (_f1e_).
+
+The error functions implement the following interface, e.g.:
+
+```python
+mae(true_prevs, prevs_hat)
+```
+
+in which the first argument is a ndarray containing the true
+prevalences, and the second argument is another ndarray with
+the estimations produced by some method.
+
+Some error functions, e.g., _mrae_, _mkld_, and _mnkld_, are
+smoothed for numerical stability. In those cases, there is a
+third argument, e.g.:
+
+```python
+def mrae(true_prevs, prevs_hat, eps=None): ...
+```
+
+indicating the value for the smoothing parameter epsilon.
+Traditionally, this value is set to 1/(2T) in past literature,
+with T the sampling size. One could either pass this value
+to the function each time, or to set a QuaPy's environment
+variable _SAMPLE_SIZE_ once, and omit this argument
+thereafter (recommended);
+e.g.:
+
+```python
+qp.environ['SAMPLE_SIZE'] = 100 # once for all
+true_prev = np.asarray([0.5, 0.3, 0.2]) # let's assume 3 classes
+estim_prev = np.asarray([0.1, 0.3, 0.6])
+error = qp.error.mrae(true_prev, estim_prev)
+print(f'mrae({true_prev}, {estim_prev}) = {error:.3f}')
+```
+
+will print:
+```
+mrae([0.500, 0.300, 0.200], [0.100, 0.300, 0.600]) = 0.914
+```
+
+Finally, it is possible to instantiate QuaPy's quantification
+error functions from strings using, e.g.:
+
+```python
+error_function = qp.error.from_name('mse')
+error = error_function(true_prev, estim_prev)
+```
+
+## Evaluation Protocols
+
+An _evaluation protocol_ is an evaluation procedure that uses
+one specific _sample generation procotol_ to genereate many
+samples, typically characterized by widely varying amounts of
+_shift_ with respect to the original distribution, that are then
+used to evaluate the performance of a (trained) quantifier.
+These protocols are explained in more detail in a dedicated [entry
+in the wiki](Protocols.md). For the moment being, let us assume we already have
+chosen and instantiated one specific such protocol, that we here
+simply call _prot_. Let also assume our model is called
+_quantifier_ and that our evaluatio measure of choice is
+_mae_. The evaluation comes down to:
+
+```python
+mae = qp.evaluation.evaluate(quantifier, protocol=prot, error_metric='mae')
+print(f'MAE = {mae:.4f}')
+```
+
+It is often desirable to evaluate our system using more than one
+single evaluatio measure. In this case, it is convenient to generate
+a _report_. A report in QuaPy is a dataframe accounting for all the
+true prevalence values with their corresponding prevalence values
+as estimated by the quantifier, along with the error each has given
+rise.
+
+```python
+report = qp.evaluation.evaluation_report(quantifier, protocol=prot, error_metrics=['mae', 'mrae', 'mkld'])
+```
+
+From a pandas' dataframe, it is straightforward to visualize all the results,
+and compute the averaged values, e.g.:
+
+```python
+pd.set_option('display.expand_frame_repr', False)
+report['estim-prev'] = report['estim-prev'].map(F.strprev)
+print(report)
+
+print('Averaged values:')
+print(report.mean())
+```
+
+This will produce an output like:
+
+```
+ true-prev estim-prev mae mrae mkld
+0 [0.308, 0.692] [0.314, 0.686] 0.005649 0.013182 0.000074
+1 [0.896, 0.104] [0.909, 0.091] 0.013145 0.069323 0.000985
+2 [0.848, 0.152] [0.809, 0.191] 0.039063 0.149806 0.005175
+3 [0.016, 0.984] [0.033, 0.967] 0.017236 0.487529 0.005298
+4 [0.728, 0.272] [0.751, 0.249] 0.022769 0.057146 0.001350
+... ... ... ... ... ...
+4995 [0.72, 0.28] [0.698, 0.302] 0.021752 0.053631 0.001133
+4996 [0.868, 0.132] [0.888, 0.112] 0.020490 0.088230 0.001985
+4997 [0.292, 0.708] [0.298, 0.702] 0.006149 0.014788 0.000090
+4998 [0.24, 0.76] [0.220, 0.780] 0.019950 0.054309 0.001127
+4999 [0.948, 0.052] [0.965, 0.035] 0.016941 0.165776 0.003538
+
+[5000 rows x 5 columns]
+Averaged values:
+mae 0.023588
+mrae 0.108779
+mkld 0.003631
+dtype: float64
+
+Process finished with exit code 0
+```
+
+Alternatively, we can simply generate all the predictions by:
+
+```python
+true_prevs, estim_prevs = qp.evaluation.prediction(quantifier, protocol=prot)
+```
+
+All the evaluation functions implement specific optimizations for speeding-up
+the evaluation of aggregative quantifiers (i.e., of instances of _AggregativeQuantifier_).
+The optimization comes down to generating classification predictions (either crisp or soft)
+only once for the entire test set, and then applying the sampling procedure to the
+predictions, instead of generating samples of instances and then computing the
+classification predictions every time. This is only possible when the protocol
+is an instance of _OnLabelledCollectionProtocol_. The optimization is only
+carried out when the number of classification predictions thus generated would be
+smaller than the number of predictions required for the entire protocol; e.g.,
+if the original dataset contains 1M instances, but the protocol is such that it would
+at most generate 20 samples of 100 instances, then it would be preferable to postpone the
+classification for each sample. This behaviour is indicated by setting
+_aggr_speedup="auto"_. Conversely, when indicating _aggr_speedup="force"_ QuaPy will
+precompute all the predictions irrespectively of the number of instances and number of samples.
+Finally, this can be deactivated by setting _aggr_speedup=False_. Note that this optimization
+is not only applied for the final evaluation, but also for the internal evaluations carried
+out during _model selection_. Since these are typically many, the heuristic can help reduce the
+execution time a lot.
\ No newline at end of file
diff --git a/docs/source/wiki_editable/ExplicitLossMinimization.md b/docs/source/wiki_editable/ExplicitLossMinimization.md
new file mode 100644
index 0000000..23a07ea
--- /dev/null
+++ b/docs/source/wiki_editable/ExplicitLossMinimization.md
@@ -0,0 +1,26 @@
+# Explicit Loss Minimization
+
+QuaPy makes available several Explicit Loss Minimization (ELM) methods, including
+SVM(Q), SVM(KLD), SVM(NKLD), SVM(AE), or SVM(RAE).
+These methods require to first download the
+[svmperf](http://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html)
+package, apply the patch
+[svm-perf-quantification-ext.patch](./svm-perf-quantification-ext.patch), and compile the sources.
+The script [prepare_svmperf.sh](prepare_svmperf.sh) does all the job. Simply run:
+
+```
+./prepare_svmperf.sh
+```
+
+The resulting directory [svm_perf_quantification](./svm_perf_quantification) contains the
+patched version of _svmperf_ with quantification-oriented losses.
+
+The [svm-perf-quantification-ext.patch](https://github.com/HLT-ISTI/QuaPy/blob/master/prepare_svmperf.sh) is an extension of the patch made available by
+[Esuli et al. 2015](https://dl.acm.org/doi/abs/10.1145/2700406?casa_token=8D2fHsGCVn0AAAAA:ZfThYOvrzWxMGfZYlQW_y8Cagg-o_l6X_PcF09mdETQ4Tu7jK98mxFbGSXp9ZSO14JkUIYuDGFG0)
+that allows SVMperf to optimize for
+the _Q_ measure as proposed by [Barranquero et al. 2015](https://www.sciencedirect.com/science/article/abs/pii/S003132031400291X)
+and for the _KLD_ and _NKLD_ measures as proposed by [Esuli et al. 2015](https://dl.acm.org/doi/abs/10.1145/2700406?casa_token=8D2fHsGCVn0AAAAA:ZfThYOvrzWxMGfZYlQW_y8Cagg-o_l6X_PcF09mdETQ4Tu7jK98mxFbGSXp9ZSO14JkUIYuDGFG0).
+This patch extends the above one by also allowing SVMperf to optimize for
+_AE_ and _RAE_.
+See [Methods.md](Methods.md) for more details and code examples.
+
diff --git a/docs/source/wiki_editable/Methods.md b/docs/source/wiki_editable/Methods.md
new file mode 100644
index 0000000..760df16
--- /dev/null
+++ b/docs/source/wiki_editable/Methods.md
@@ -0,0 +1,526 @@
+# Quantification Methods
+
+Quantification methods can be categorized as belonging to
+`aggregative` and `non-aggregative` groups.
+Most methods included in QuaPy at the moment are of type `aggregative`
+(though we plan to add many more methods in the near future), i.e.,
+are methods characterized by the fact that
+quantification is performed as an aggregation function of the individual
+products of classification.
+
+Any quantifier in QuaPy shoud extend the class `BaseQuantifier`,
+and implement some abstract methods:
+```python
+ @abstractmethod
+ def fit(self, data: LabelledCollection): ...
+
+ @abstractmethod
+ def quantify(self, instances): ...
+```
+The meaning of those functions should be familiar to those
+used to work with scikit-learn since the class structure of QuaPy
+is directly inspired by scikit-learn's _Estimators_. Functions
+`fit` and `quantify` are used to train the model and to provide
+class estimations (the reason why
+scikit-learn' structure has not been adopted _as is_ in QuaPy responds to
+the fact that scikit-learn's `predict` function is expected to return
+one output for each input element --e.g., a predicted label for each
+instance in a sample-- while in quantification the output for a sample
+is one single array of class prevalences).
+Quantifiers also extend from scikit-learn's `BaseEstimator`, in order
+to simplify the use of `set_params` and `get_params` used in
+[model selector](https://github.com/HLT-ISTI/QuaPy/wiki/Model-Selection).
+
+## Aggregative Methods
+
+All quantification methods are implemented as part of the
+`qp.method` package. In particular, `aggregative` methods are defined in
+`qp.method.aggregative`, and extend `AggregativeQuantifier(BaseQuantifier)`.
+The methods that any `aggregative` quantifier must implement are:
+
+```python
+ @abstractmethod
+ def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
+
+ @abstractmethod
+ def aggregate(self, classif_predictions:np.ndarray): ...
+```
+
+These two functions replace the `fit` and `quantify` methods, since those
+come with default implementations. The `fit` function is provided and amounts to:
+
+```python
+def fit(self, data: LabelledCollection, fit_classifier=True, val_split=None):
+ self._check_init_parameters()
+ classif_predictions = self.classifier_fit_predict(data, fit_classifier, predict_on=val_split)
+ self.aggregation_fit(classif_predictions, data)
+ return self
+```
+
+Note that this function fits the classifier, and generates the predictions. This is assumed
+to be a routine common to all aggregative quantifiers, and is provided by QuaPy. What remains
+ahead is to define the `aggregation_fit` function, that takes as input the classifier predictions
+and the original training data (this latter is typically unused). The classifier predictions
+can be:
+- confidence scores: quantifiers inheriting directly from `AggregativeQuantifier`
+- crisp predictions: quantifiers inheriting from `AggregativeCrispQuantifier`
+- posterior probabilities: quantifiers inheriting from `AggregativeSoftQuantifier`
+- _anything_: custom quantifiers overriding the `classify` method
+
+Note also that the `fit` method also calls `_check_init_parameters`; this function is meant to be
+overriden (if needed) and allows the method to quickly raise any exception based on any inconsistency
+found in the `__init__` arguments, thus avoiding to break after training the classifier and generating
+predictions.
+
+Similarly, the function `quantify` is provided, and amounts to:
+
+```python
+def quantify(self, instances):
+ classif_predictions = self.classify(instances)
+ return self.aggregate(classif_predictions)
+```
+
+in which only the function `aggregate` is required to be overriden in most cases.
+
+Aggregative quantifiers are expected to maintain a classifier (which is
+accessed through the `@property` `classifier`). This classifier is
+given as input to the quantifier, and can be already fit
+on external data (in which case, the `fit_learner` argument should
+be set to False), or be fit by the quantifier's fit (default).
+
+The above patterns (in training: fit the classifier, then fit the aggregation;
+in test: classify, then aggregate) allows QuaPy to optimize many internal procedures.
+In particular, the model selection routing takes advantage of this two-step process
+and generates classifiers only for the valid combinations of hyperparameters of the
+classifier, and then _clones_ these classifiers and explores the combinations
+of hyperparameters that are specific to the quantifier (this can result in huge
+time savings).
+Concerning the inference phase, this two-step process allow the evaluation of many
+standard protocols (e.g., the [artificial sampling protocol](https://github.com/HLT-ISTI/QuaPy/wiki/Evaluation)) to be
+carried out very efficiently. The reason is that the entire set can be pre-classified
+once, and the quantification estimations for different samples can directly
+reuse these predictions, without requiring to classify each element every time.
+QuaPy leverages this property to speed-up any procedure having to do with
+quantification over samples, as is customarily done in model selection or
+in evaluation.
+
+### The Classify & Count variants
+
+QuaPy implements the four CC variants, i.e.:
+
+* _CC_ (Classify & Count), the simplest aggregative quantifier; one that
+ simply relies on the label predictions of a classifier to deliver class estimates.
+* _ACC_ (Adjusted Classify & Count), the adjusted variant of CC.
+* _PCC_ (Probabilistic Classify & Count), the probabilistic variant of CC that
+relies on the soft estimations (or posterior probabilities) returned by a (probabilistic) classifier.
+* _PACC_ (Probabilistic Adjusted Classify & Count), the adjusted variant of PCC.
+
+The following code serves as a complete example using CC equipped
+with a SVM as the classifier:
+
+```python
+import quapy as qp
+import quapy.functional as F
+from sklearn.svm import LinearSVC
+
+training, test = qp.datasets.fetch_twitter('hcr', pickle=True).train_test
+
+# instantiate a classifier learner, in this case a SVM
+svm = LinearSVC()
+
+# instantiate a Classify & Count with the SVM
+# (an alias is available in qp.method.aggregative.ClassifyAndCount)
+model = qp.method.aggregative.CC(svm)
+model.fit(training)
+estim_prevalence = model.quantify(test.instances)
+```
+
+The same code could be used to instantiate an ACC, by simply replacing
+the instantiation of the model with:
+```python
+model = qp.method.aggregative.ACC(svm)
+```
+Note that the adjusted variants (ACC and PACC) need to estimate
+some parameters for performing the adjustment (e.g., the
+_true positive rate_ and the _false positive rate_ in case of
+binary classification) that are estimated on a validation split
+of the labelled set. In this case, the `__init__` method of
+ACC defines an additional parameter, `val_split`. If this parameter
+is set to a float in [0,1] representing a fraction (e.g., 0.4)
+then that fraction of labelled data (e.g., 40%)
+will be used for estimating the parameters for adjusting the
+predictions. This parameters can also be set with an integer,
+indicating that the parameters should be estimated by means of
+_k_-fold cross-validation, for which the integer indicates the
+number _k_ of folds (the default value is 5). Finally, `val_split` can be set to a
+specific held-out validation set (i.e., an instance of `LabelledCollection`).
+
+The specification of `val_split` can be
+postponed to the invokation of the fit method (if `val_split` was also
+set in the constructor, the one specified at fit time would prevail),
+e.g.:
+
+```python
+model = qp.method.aggregative.ACC(svm)
+# perform 5-fold cross validation for estimating ACC's parameters
+# (overrides the default val_split=0.4 in the constructor)
+model.fit(training, val_split=5)
+```
+
+The following code illustrates the case in which PCC is used:
+
+```python
+model = qp.method.aggregative.PCC(svm)
+model.fit(training)
+estim_prevalence = model.quantify(test.instances)
+print('classifier:', model.classifier)
+```
+In this case, QuaPy will print:
+```
+The learner LinearSVC does not seem to be probabilistic. The learner will be calibrated.
+classifier: CalibratedClassifierCV(base_estimator=LinearSVC(), cv=5)
+```
+The first output indicates that the learner (`LinearSVC` in this case)
+is not a probabilistic classifier (i.e., it does not implement the
+`predict_proba` method) and so, the classifier will be converted to
+a probabilistic one through [calibration](https://scikit-learn.org/stable/modules/calibration.html).
+As a result, the classifier that is printed in the second line points
+to a `CalibratedClassifier` instance. Note that calibration can only
+be applied to hard classifiers when `fit_learner=True`; an exception
+will be raised otherwise.
+
+Lastly, everything we said aboud ACC and PCC
+applies to PACC as well.
+
+_New in v0.1.9_: quantifiers ACC and PACC now have three additional arguments: `method`, `solver` and `norm`:
+
+* Argument `method` specifies how to solve, for `p`, the linear system `q = Mp` (where `q` is the unadjusted counts for the
+test sample, `M` contains the class-conditional unadjusted counts --i.e., the missclassification rates-- and `p` is the
+sought prevalence vector):
+ * option `"inversion"`: attempts to invert matrix `M`, thus solving `Minv q = p`. In degenerated cases, this
+ inversion may not exist. In such cases, the method defaults to returning `q` (the unadjusted counts)
+ * option `"invariant-ratio""` uses the invariant ratio estimator system proposed in Remark 5 of
+[Vaz, A.F., Izbicki F. and Stern, R.B. "Quantification Under Prior Probability Shift: the Ratio Estimator
+and its Extensions", in Journal of Machine Learning Research 20 (2019)](https://jmlr.csail.mit.edu/papers/volume20/18-456/18-456.pdf).
+
+* Argument `solver` specifies how to solve the linear system.
+ * `"exact-raise"` solves the system of linear equations and raises an exception if the system is not solvable
+ * `"exact-cc"` returns the original unadjusted count if the system is not solvable
+ * `"minimize"` minimizes the L2 norm of :math:`|Mp-q|`. This one generally works better, and is the
+ default parameter. More details about this can be consulted in
+ [Bunse, M. "On Multi-Class Extensions of Adjusted Classify and Count",
+ on proceedings of the 2nd International Workshop on Learning to Quantify: Methods and Applications (LQ 2022),
+ ECML/PKDD 2022, Grenoble (France)](https://lq-2022.github.io/proceedings/CompleteVolume.pdf)).
+
+* Argument `norm` specifies how to normalize the estimate `p` when the vector lies outside of the probability simplex.
+Options are:
+ * `"clip"` which clips the values to range `[0, 1]` and then L1-normalizes the vector
+ * `"mapsimplex"` which projects the results on the probability simplex, as proposed by Vaz et al. in
+ [Remark 5 of Vaz, et. (2019)](https://jmlr.csail.mit.edu/papers/volume20/18-456/18-456.pdf). This implementation
+ relies on [Mathieu Blondel's `projection_simplex_sort`](https://gist.github.com/mblondel/6f3b7aaad90606b98f71))
+ * `"condsoftmax"` applies softmax normalization only if the prevalence vector lies outside of the probability simplex.
+
+
+#### BayesianCC (_New in v0.1.9_!)
+
+The `BayesianCC` is a variant of ACC introduced in
+[Ziegler, A. and Czyż, P. "Bayesian quantification with black-box estimators", arXiv (2023)](https://arxiv.org/abs/2302.09159),
+which models the probabilities `q = Mp` using latent random variables with weak Bayesian priors, rather than
+plug-in probability estimates. In particular, it uses Markov Chain Monte Carlo sampling to find the values of
+`p` compatible with the observed quantities.
+The `aggregate` method returns the posterior mean and the `get_prevalence_samples` method can be used to find
+uncertainty around `p` estimates (conditional on the observed data and the trained classifier)
+and is suitable for problems in which the `q = Mp` matrix is nearly non-invertible.
+
+Note that this quantification method requires `val_split` to be a `float` and installation of additional dependencies (`$ pip install quapy[bayes]`) needed to run Markov chain Monte Carlo sampling. Markov Chain Monte Carlo is is slower than matrix inversion methods, but is guaranteed to sample proper probability vectors, so no clipping strategies are required.
+An example presenting how to run the method and use posterior samples is available in `examples/bayesian_quantification.py`.
+
+### Expectation Maximization (EMQ)
+
+The Expectation Maximization Quantifier (EMQ), also known as
+the SLD, is available at `qp.method.aggregative.EMQ` or via the
+alias `qp.method.aggregative.ExpectationMaximizationQuantifier`.
+The method is described in:
+
+_Saerens, M., Latinne, P., and Decaestecker, C. (2002). Adjusting the outputs of a classifier
+to new a priori probabilities: A simple procedure. Neural Computation, 14(1):21–41._
+
+EMQ works with a probabilistic classifier (if the classifier
+given as input is a hard one, a calibration will be attempted).
+Although this method was originally proposed for improving the
+posterior probabilities of a probabilistic classifier, and not
+for improving the estimation of prior probabilities, EMQ ranks
+almost always among the most effective quantifiers in the
+experiments we have carried out.
+
+An example of use can be found below:
+
+```python
+import quapy as qp
+from sklearn.linear_model import LogisticRegression
+
+dataset = qp.datasets.fetch_twitter('hcr', pickle=True)
+
+model = qp.method.aggregative.EMQ(LogisticRegression())
+model.fit(dataset.training)
+estim_prevalence = model.quantify(dataset.test.instances)
+```
+
+_New in v0.1.7_: EMQ now accepts two new parameters in the construction method, namely
+`exact_train_prev` which allows to use the true training prevalence as the departing
+prevalence estimation (default behaviour), or instead an approximation of it as
+suggested by [Alexandari et al. (2020)](http://proceedings.mlr.press/v119/alexandari20a.html)
+(by setting `exact_train_prev=False`).
+The other parameter is `recalib` which allows to indicate a calibration method, among those
+proposed by [Alexandari et al. (2020)](http://proceedings.mlr.press/v119/alexandari20a.html),
+including the Bias-Corrected Temperature Scaling, Vector Scaling, etc.
+See the API documentation for further details.
+
+
+### Hellinger Distance y (HDy)
+
+Implementation of the method based on the Hellinger Distance y (HDy) proposed by
+[González-Castro, V., Alaiz-Rodrı́guez, R., and Alegre, E. (2013). Class distribution
+estimation based on the Hellinger distance. Information Sciences, 218:146–164.](https://www.sciencedirect.com/science/article/pii/S0020025512004069)
+
+It is implemented in `qp.method.aggregative.HDy` (also accessible
+through the allias `qp.method.aggregative.HellingerDistanceY`).
+This method works with a probabilistic classifier (hard classifiers
+can be used as well and will be calibrated) and requires a validation
+set to estimate parameter for the mixture model. Just like
+ACC and PACC, this quantifier receives a `val_split` argument
+in the constructor (or in the fit method, in which case the previous
+value is overridden) that can either be a float indicating the proportion
+of training data to be taken as the validation set (in a random
+stratified split), or a validation set (i.e., an instance of
+`LabelledCollection`) itself.
+
+HDy was proposed as a binary classifier and the implementation
+provided in QuaPy accepts only binary datasets.
+
+The following code shows an example of use:
+```python
+import quapy as qp
+from sklearn.linear_model import LogisticRegression
+
+# load a binary dataset
+dataset = qp.datasets.fetch_reviews('hp', pickle=True)
+qp.data.preprocessing.text2tfidf(dataset, min_df=5, inplace=True)
+
+model = qp.method.aggregative.HDy(LogisticRegression())
+model.fit(dataset.training)
+estim_prevalence = model.quantify(dataset.test.instances)
+```
+
+_New in v0.1.7:_ QuaPy now provides an implementation of the generalized
+"Distribution Matching" approaches for multiclass, inspired by the framework
+of [Firat (2016)](https://arxiv.org/abs/1606.00868). One can instantiate
+a variant of HDy for multiclass quantification as follows:
+
+```python
+mutliclassHDy = qp.method.aggregative.DMy(classifier=LogisticRegression(), divergence='HD', cdf=False)
+```
+
+_New in v0.1.7:_ QuaPy now provides an implementation of the "DyS"
+framework proposed by [Maletzke et al (2020)](https://ojs.aaai.org/index.php/AAAI/article/view/4376)
+and the "SMM" method proposed by [Hassan et al (2019)](https://ieeexplore.ieee.org/document/9260028)
+(thanks to _Pablo González_ for the contributions!)
+
+### Threshold Optimization methods
+
+_New in v0.1.7:_ QuaPy now implements Forman's threshold optimization methods;
+see, e.g., [(Forman 2006)](https://dl.acm.org/doi/abs/10.1145/1150402.1150423)
+and [(Forman 2008)](https://link.springer.com/article/10.1007/s10618-008-0097-y).
+These include: T50, MAX, X, Median Sweep (MS), and its variant MS2.
+
+### Explicit Loss Minimization
+
+The Explicit Loss Minimization (ELM) represent a family of methods
+based on structured output learning, i.e., quantifiers relying on
+classifiers that have been optimized targeting a
+quantification-oriented evaluation measure.
+The original methods are implemented in QuaPy as classify & count (CC)
+quantifiers that use Joachim's [SVMperf](https://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html)
+as the underlying classifier, properly set to optimize for the desired loss.
+
+In QuaPy, this can be more achieved by calling the functions:
+
+* `newSVMQ`: returns the quantification method called SVM(Q) that optimizes for the metric _Q_ defined
+in [_Barranquero, J., Díez, J., and del Coz, J. J. (2015). Quantification-oriented learning based
+on reliable classifiers. Pattern Recognition, 48(2):591–604._](https://www.sciencedirect.com/science/article/pii/S003132031400291X)
+* `newSVMKLD` and `newSVMNKLD`: returns the quantification method called SVM(KLD) and SVM(nKLD), standing for
+ Kullback-Leibler Divergence and Normalized Kullback-Leibler Divergence, as proposed in [_Esuli, A. and Sebastiani, F. (2015).
+ Optimizing text quantifiers for multivariate loss functions.
+ ACM Transactions on Knowledge Discovery and Data, 9(4):Article 27._](https://dl.acm.org/doi/abs/10.1145/2700406)
+* `newSVMAE` and `newSVMRAE`: returns a quantification method called SVM(AE) and SVM(RAE) that optimizes for the (Mean) Absolute Error and for the
+ (Mean) Relative Absolute Error, as first used by
+ [_Moreo, A. and Sebastiani, F. (2021). Tweet sentiment quantification: An experimental re-evaluation. PLOS ONE 17 (9), 1-23._](https://arxiv.org/abs/2011.02552)
+
+the last two methods (SVM(AE) and SVM(RAE)) have been implemented in
+QuaPy in order to make available ELM variants for what nowadays
+are considered the most well-behaved evaluation metrics in quantification.
+
+In order to make these models work, you would need to run the script
+`prepare_svmperf.sh` (distributed along with QuaPy) that
+downloads `SVMperf`' source code, applies a patch that
+implements the quantification oriented losses, and compiles the
+sources.
+
+If you want to add any custom loss, you would need to modify
+the source code of `SVMperf` in order to implement it, and
+assign a valid loss code to it. Then you must re-compile
+the whole thing and instantiate the quantifier in QuaPy
+as follows:
+
+```python
+# you can either set the path to your custom svm_perf_quantification implementation
+# in the environment variable, or as an argument to the constructor of ELM
+qp.environ['SVMPERF_HOME'] = './path/to/svm_perf_quantification'
+
+# assign an alias to your custom loss and the id you have assigned to it
+svmperf = qp.classification.svmperf.SVMperf
+svmperf.valid_losses['mycustomloss'] = 28
+
+# instantiate the ELM method indicating the loss
+model = qp.method.aggregative.ELM(loss='mycustomloss')
+```
+
+All ELM are binary quantifiers since they rely on `SVMperf`, that
+currently supports only binary classification.
+ELM variants (any binary quantifier in general) can be extended
+to operate in single-label scenarios trivially by adopting a
+"one-vs-all" strategy (as, e.g., in
+[_Gao, W. and Sebastiani, F. (2016). From classification to quantification in tweet sentiment
+analysis. Social Network Analysis and Mining, 6(19):1–22_](https://link.springer.com/article/10.1007/s13278-016-0327-z)).
+In QuaPy this is possible by using the `OneVsAll` class.
+
+There are two ways for instantiating this class, `OneVsAllGeneric` that works for
+any quantifier, and `OneVsAllAggregative` that is optimized for aggregative quantifiers.
+In general, you can simply use the `newOneVsAll` function and QuaPy will choose
+the more convenient of the two.
+
+```python
+import quapy as qp
+from quapy.method.aggregative import SVMQ
+
+# load a single-label dataset (this one contains 3 classes)
+dataset = qp.datasets.fetch_twitter('hcr', pickle=True)
+
+# let qp know where svmperf is
+qp.environ['SVMPERF_HOME'] = '../svm_perf_quantification'
+
+model = newOneVsAll(SVMQ(), n_jobs=-1) # run them on parallel
+model.fit(dataset.training)
+estim_prevalence = model.quantify(dataset.test.instances)
+```
+
+Check the examples _[explicit_loss_minimization.py](..%2Fexamples%2Fexplicit_loss_minimization.py)_
+and [one_vs_all.py](..%2Fexamples%2Fone_vs_all.py) for more details.
+
+### Kernel Density Estimation methods (KDEy)
+
+_New in v0.1.8_: QuaPy now provides implementations for the three variants
+of KDE-based methods proposed in
+_[Moreo, A., González, P. and del Coz, J.J., 2023.
+Kernel Density Estimation for Multiclass Quantification.
+arXiv preprint arXiv:2401.00490.](https://arxiv.org/abs/2401.00490)_.
+The variants differ in the divergence metric to be minimized:
+
+- KDEy-HD: minimizes the (squared) Hellinger Distance and solves the problem via a Monte Carlo approach
+- KDEy-CS: minimizes the Cauchy-Schwarz divergence and solves the problem via a closed-form solution
+- KDEy-ML: minimizes the Kullback-Leibler divergence and solves the problem via maximum-likelihood
+
+These methods are specifically devised for multiclass problems (although they can tackle
+binary problems too).
+
+All KDE-based methods depend on the hyperparameter `bandwidth` of the kernel. Typical values
+that can be explored in model selection range in [0.01, 0.25]. The methods' performance
+vary smoothing with smooth variations of this hyperparameter.
+
+
+## Meta Models
+
+By _meta_ models we mean quantification methods that are defined on top of other
+quantification methods, and that thus do not squarely belong to the aggregative nor
+the non-aggregative group (indeed, _meta_ models could use quantifiers from any of those
+groups).
+_Meta_ models are implemented in the `qp.method.meta` module.
+
+### Ensembles
+
+QuaPy implements (some of) the variants proposed in:
+
+* [_Pérez-Gállego, P., Quevedo, J. R., & del Coz, J. J. (2017).
+Using ensembles for problems with characterizable changes in data distribution: A case study on quantification.
+Information Fusion, 34, 87-100._](https://www.sciencedirect.com/science/article/pii/S1566253516300628)
+* [_Pérez-Gállego, P., Castano, A., Quevedo, J. R., & del Coz, J. J. (2019).
+ Dynamic ensemble selection for quantification tasks.
+ Information Fusion, 45, 1-15._](https://www.sciencedirect.com/science/article/pii/S1566253517303652)
+
+The following code shows how to instantiate an Ensemble of 30 _Adjusted Classify & Count_ (ACC)
+quantifiers operating with a _Logistic Regressor_ (LR) as the base classifier, and using the
+_average_ as the aggregation policy (see the original article for further details).
+The last parameter indicates to use all processors for parallelization.
+
+```python
+import quapy as qp
+from quapy.method.aggregative import ACC
+from quapy.method.meta import Ensemble
+from sklearn.linear_model import LogisticRegression
+
+dataset = qp.datasets.fetch_UCIBinaryDataset('haberman')
+
+model = Ensemble(quantifier=ACC(LogisticRegression()), size=30, policy='ave', n_jobs=-1)
+model.fit(dataset.training)
+estim_prevalence = model.quantify(dataset.test.instances)
+```
+
+Other aggregation policies implemented in QuaPy include:
+* 'ptr' for applying a dynamic selection based on the training prevalence of the ensemble's members
+* 'ds' for applying a dynamic selection based on the Hellinger Distance
+* _any valid quantification measure_ (e.g., 'mse') for performing a static selection based on
+the performance estimated for each member of the ensemble in terms of that evaluation metric.
+
+When using any of the above options, it is important to set the `red_size` parameter, which
+informs of the number of members to retain.
+
+Please, check the [model selection](https://github.com/HLT-ISTI/QuaPy/wiki/Model-Selection)
+wiki if you want to optimize the hyperparameters of ensemble for classification or quantification.
+
+### The QuaNet neural network
+
+QuaPy offers an implementation of QuaNet, a deep learning model presented in:
+
+[_Esuli, A., Moreo, A., & Sebastiani, F. (2018, October).
+A recurrent neural network for sentiment quantification.
+In Proceedings of the 27th ACM International Conference on
+Information and Knowledge Management (pp. 1775-1778)._](https://dl.acm.org/doi/abs/10.1145/3269206.3269287)
+
+This model requires `torch` to be installed.
+QuaNet also requires a classifier that can provide embedded representations
+of the inputs.
+In the original paper, QuaNet was tested using an LSTM as the base classifier.
+In the following example, we show an instantiation of QuaNet that instead uses CNN as a probabilistic classifier, taking its last layer representation as the document embedding:
+
+```python
+import quapy as qp
+from quapy.method.meta import QuaNet
+from quapy.classification.neural import NeuralClassifierTrainer, CNNnet
+
+# use samples of 100 elements
+qp.environ['SAMPLE_SIZE'] = 100
+
+# load the kindle dataset as text, and convert words to numerical indexes
+dataset = qp.datasets.fetch_reviews('kindle', pickle=True)
+qp.data.preprocessing.index(dataset, min_df=5, inplace=True)
+
+# the text classifier is a CNN trained by NeuralClassifierTrainer
+cnn = CNNnet(dataset.vocabulary_size, dataset.n_classes)
+learner = NeuralClassifierTrainer(cnn, device='cuda')
+
+# train QuaNet
+model = QuaNet(learner, device='cuda')
+model.fit(dataset.training)
+estim_prevalence = model.quantify(dataset.test.instances)
+```
+
diff --git a/docs/source/wiki_editable/Model-Selection.md b/docs/source/wiki_editable/Model-Selection.md
new file mode 100644
index 0000000..9dd5bab
--- /dev/null
+++ b/docs/source/wiki_editable/Model-Selection.md
@@ -0,0 +1,145 @@
+# Model Selection
+
+As a supervised machine learning task, quantification methods
+can strongly depend on a good choice of model hyper-parameters.
+The process whereby those hyper-parameters are chosen is
+typically known as _Model Selection_, and typically consists of
+testing different settings and picking the one that performed
+best in a held-out validation set in terms of any given
+evaluation measure.
+
+## Targeting a Quantification-oriented loss
+
+The task being optimized determines the evaluation protocol,
+i.e., the criteria according to which the performance of
+any given method for solving is to be assessed.
+As a task on its own right, quantification should impose
+its own model selection strategies, i.e., strategies
+aimed at finding appropriate configurations
+specifically designed for the task of quantification.
+
+Quantification has long been regarded as an add-on of
+classification, and thus the model selection strategies
+customarily adopted in classification have simply been
+applied to quantification (see the next section).
+It has been argued in [Moreo, Alejandro, and Fabrizio Sebastiani.
+Re-Assessing the "Classify and Count" Quantification Method.
+ECIR 2021: Advances in Information Retrieval pp 75–91.](https://link.springer.com/chapter/10.1007/978-3-030-72240-1_6)
+that specific model selection strategies should
+be adopted for quantification. That is, model selection
+strategies for quantification should target
+quantification-oriented losses and be tested in a variety
+of scenarios exhibiting different degrees of prior
+probability shift.
+
+The class _qp.model_selection.GridSearchQ_ implements a grid-search exploration over the space of
+hyper-parameter combinations that [evaluates](https://github.com/HLT-ISTI/QuaPy/wiki/Evaluation)
+each combination of hyper-parameters by means of a given quantification-oriented
+error metric (e.g., any of the error functions implemented
+in _qp.error_) and according to a
+[sampling generation protocol](https://github.com/HLT-ISTI/QuaPy/wiki/Protocols).
+
+The following is an example (also included in the examples folder) of model selection for quantification:
+
+```python
+import quapy as qp
+from quapy.protocol import APP
+from quapy.method.aggregative import DMy
+from sklearn.linear_model import LogisticRegression
+import numpy as np
+
+"""
+In this example, we show how to perform model selection on a DistributionMatching quantifier.
+"""
+
+model = DMy(LogisticRegression())
+
+qp.environ['SAMPLE_SIZE'] = 100
+qp.environ['N_JOBS'] = -1 # explore hyper-parameters in parallel
+
+training, test = qp.datasets.fetch_reviews('imdb', tfidf=True, min_df=5).train_test
+
+# The model will be returned by the fit method of GridSearchQ.
+# Every combination of hyper-parameters will be evaluated by confronting the
+# quantifier thus configured against a series of samples generated by means
+# of a sample generation protocol. For this example, we will use the
+# artificial-prevalence protocol (APP), that generates samples with prevalence
+# values in the entire range of values from a grid (e.g., [0, 0.1, 0.2, ..., 1]).
+# We devote 30% of the dataset for this exploration.
+training, validation = training.split_stratified(train_prop=0.7)
+protocol = APP(validation)
+
+# We will explore a classification-dependent hyper-parameter (e.g., the 'C'
+# hyper-parameter of LogisticRegression) and a quantification-dependent hyper-parameter
+# (e.g., the number of bins in a DistributionMatching quantifier.
+# Classifier-dependent hyper-parameters have to be marked with a prefix "classifier__"
+# in order to let the quantifier know this hyper-parameter belongs to its underlying
+# classifier.
+param_grid = {
+ 'classifier__C': np.logspace(-3, 3, 7),
+ 'nbins': [8, 16, 32, 64],
+}
+
+model = qp.model_selection.GridSearchQ(
+ model=model,
+ param_grid=param_grid,
+ protocol=protocol,
+ error='mae', # the error to optimize is the MAE (a quantification-oriented loss)
+ refit=True, # retrain on the whole labelled set once done
+ verbose=True # show information as the process goes on
+).fit(training)
+
+print(f'model selection ended: best hyper-parameters={model.best_params_}')
+model = model.best_model_
+
+# evaluation in terms of MAE
+# we use the same evaluation protocol (APP) on the test set
+mae_score = qp.evaluation.evaluate(model, protocol=APP(test), error_metric='mae')
+
+print(f'MAE={mae_score:.5f}')
+```
+
+In this example, the system outputs:
+```
+[GridSearchQ]: starting model selection with self.n_jobs =-1
+[GridSearchQ]: hyperparams={'classifier__C': 0.01, 'nbins': 64} got mae score 0.04021 [took 1.1356s]
+[GridSearchQ]: hyperparams={'classifier__C': 0.01, 'nbins': 32} got mae score 0.04286 [took 1.2139s]
+[GridSearchQ]: hyperparams={'classifier__C': 0.01, 'nbins': 16} got mae score 0.04888 [took 1.2491s]
+[GridSearchQ]: hyperparams={'classifier__C': 0.001, 'nbins': 8} got mae score 0.05163 [took 1.5372s]
+[...]
+[GridSearchQ]: hyperparams={'classifier__C': 1000.0, 'nbins': 32} got mae score 0.02445 [took 2.9056s]
+[GridSearchQ]: optimization finished: best params {'classifier__C': 100.0, 'nbins': 32} (score=0.02234) [took 7.3114s]
+[GridSearchQ]: refitting on the whole development set
+model selection ended: best hyper-parameters={'classifier__C': 100.0, 'nbins': 32}
+MAE=0.03102
+```
+
+
+## Targeting a Classification-oriented loss
+
+Optimizing a model for quantification could rather be
+computationally costly.
+In aggregative methods, one could alternatively try to optimize
+the classifier's hyper-parameters for classification.
+Although this is theoretically suboptimal, many articles in
+quantification literature have opted for this strategy.
+
+In QuaPy, this is achieved by simply instantiating the
+classifier learner as a GridSearchCV from scikit-learn.
+The following code illustrates how to do that:
+
+```python
+learner = GridSearchCV(
+ LogisticRegression(),
+ param_grid={'C': np.logspace(-4, 5, 10), 'class_weight': ['balanced', None]},
+ cv=5)
+model = DistributionMatching(learner).fit(dataset.train)
+```
+
+However, this is conceptually flawed, since the model should be
+optimized for the task at hand (quantification), and not for a surrogate task (classification),
+i.e., the model should be requested to deliver low quantification errors, rather
+than low classification errors.
+
+
+
diff --git a/docs/source/wiki_editable/Plotting.md b/docs/source/wiki_editable/Plotting.md
new file mode 100644
index 0000000..99f3f7e
--- /dev/null
+++ b/docs/source/wiki_editable/Plotting.md
@@ -0,0 +1,250 @@
+# Plotting
+
+The module _qp.plot_ implements some basic plotting functions
+that can help analyse the performance of a quantification method.
+
+All plotting functions receive as inputs the outcomes of
+some experiments and include, for each experiment,
+the following three main arguments:
+
+* _method_names_ a list containing the names of the quantification methods
+* _true_prevs_ a list containing matrices of true prevalences
+* _estim_prevs_ a list containing matrices of estimated prevalences
+(should be of the same shape as the corresponding matrix in _true_prevs_)
+
+Note that a method (as indicated by a name in _method_names_) can
+appear more than once. This could occur when various datasets are
+involved in the experiments. In this case, all experiments for the
+method will be merged and the plot will represent the method's
+performance across various datasets.
+
+This is a very simple example of a valid input for the plotting functions:
+```python
+method_names = ['classify & count', 'EMQ', 'classify & count']
+true_prevs = [
+ np.array([[0.5, 0.5], [0.25, 0.75]]),
+ np.array([[0.0, 1.0], [0.25, 0.75], [0.0, 0.1]]),
+ np.array([[0.0, 1.0], [0.25, 0.75], [0.0, 0.1]]),
+]
+estim_prevs = [
+ np.array([[0.45, 0.55], [0.6, 0.4]]),
+ np.array([[0.0, 1.0], [0.5, 0.5], [0.2, 0.8]]),
+ np.array([[0.1, 0.9], [0.3, 0.7], [0.0, 0.1]]),
+]
+```
+in which the _classify & count_ has been tested in two datasets and
+the _EMQ_ method has been tested only in one dataset. For the first
+experiment, only two (binary) quantifications have been tested,
+while for the second and third experiments three instances have
+been tested.
+
+In general, we would like to test the performance of the
+quantification methods across different scenarios showcasing
+the accuracy of the quantifier in predicting class prevalences
+for a wide range of prior distributions. This can easily be
+achieved by means of the
+[artificial sampling protocol](https://github.com/HLT-ISTI/QuaPy/wiki/Protocols)
+that is implemented in QuaPy.
+
+The following code shows how to perform one simple experiment
+in which the 4 _CC-variants_, all equipped with a linear SVM, are
+applied to one binary dataset of reviews about _Kindle_ devices and
+tested across the entire spectrum of class priors (taking 21 splits
+of the interval [0,1], i.e., using prevalence steps of 0.05, and
+generating 100 random samples at each prevalence).
+
+```python
+import quapy as qp
+from protocol import APP
+from quapy.method.aggregative import CC, ACC, PCC, PACC
+from sklearn.svm import LinearSVC
+
+qp.environ['SAMPLE_SIZE'] = 500
+
+def gen_data():
+
+ def base_classifier():
+ return LinearSVC(class_weight='balanced')
+
+ def models():
+ yield 'CC', CC(base_classifier())
+ yield 'ACC', ACC(base_classifier())
+ yield 'PCC', PCC(base_classifier())
+ yield 'PACC', PACC(base_classifier())
+
+ train, test = qp.datasets.fetch_reviews('kindle', tfidf=True, min_df=5).train_test
+
+ method_names, true_prevs, estim_prevs, tr_prevs = [], [], [], []
+
+ for method_name, model in models():
+ model.fit(train)
+ true_prev, estim_prev = qp.evaluation.prediction(model, APP(test, repeats=100, random_state=0))
+
+ method_names.append(method_name)
+ true_prevs.append(true_prev)
+ estim_prevs.append(estim_prev)
+ tr_prevs.append(train.prevalence())
+
+ return method_names, true_prevs, estim_prevs, tr_prevs
+
+method_names, true_prevs, estim_prevs, tr_prevs = gen_data()
+````
+the plots that can be generated are explained below.
+
+## Diagonal Plot
+
+The _diagonal_ plot shows a very insightful view of the
+quantifier's performance. It plots the predicted class
+prevalence (in the y-axis) against the true class prevalence
+(in the x-axis). Unfortunately, it is limited to binary quantification,
+although one can simply generate as many _diagonal_ plots as
+classes there are by indicating which class should be considered
+the target of the plot.
+
+The following call will produce the plot:
+
+```python
+qp.plot.binary_diagonal(method_names, true_prevs, estim_prevs, train_prev=tr_prevs[0], savepath='./plots/bin_diag.png')
+```
+
+the last argument is optional, and indicates the path where to save
+the plot (the file extension will determine the format -- typical extensions
+are '.png' or '.pdf'). If this path is not provided, then the plot
+will be shown but not saved.
+The resulting plot should look like:
+
+
+
+Note that in this case, we are also indicating the training
+prevalence, which is plotted in the diagonal a as cyan dot.
+The color bands indicate the standard deviations of the predictions,
+and can be hidden by setting the argument _show_std=False_ (see
+the complete list of arguments in the documentation).
+
+Finally, note how most quantifiers, and specially the "unadjusted"
+variants CC and PCC, are strongly biased towards the
+prevalence seen during training.
+
+## Quantification bias
+
+This plot aims at evincing the bias that any quantifier
+displays with respect to the training prevalences by
+means of [box plots](https://en.wikipedia.org/wiki/Box_plot).
+This plot can be generated by:
+
+```python
+qp.plot.binary_bias_global(method_names, true_prevs, estim_prevs, savepath='./plots/bin_bias.png')
+```
+
+and should look like:
+
+
+
+The box plots show some interesting facts:
+* all methods are biased towards the training prevalence but specially
+so CC and PCC (an unbiased quantifier would have a box centered at 0)
+* the bias is always positive, indicating that all methods tend to
+overestimate the positive class prevalence
+* CC and PCC have high variability while ACC and specially PACC exhibit
+lower variability.
+
+Again, these plots could be generated for experiments ranging across
+different datasets, and the plot will merge all data accordingly.
+
+Another illustrative example can be shown that consists of
+training different CC quantifiers trained at different
+(artificially sampled) training prevalences.
+For this example, we generate training samples of 5000
+documents containing 10%, 20%, ..., 90% of positives from the
+IMDb dataset, and generate the bias plot again.
+This example can be run by rewritting the _gen_data()_ function
+like this:
+
+```python
+def gen_data():
+
+ train, test = qp.datasets.fetch_reviews('imdb', tfidf=True, min_df=5).train_test
+ model = CC(LinearSVC())
+
+ method_data = []
+ for training_prevalence in np.linspace(0.1, 0.9, 9):
+ training_size = 5000
+ # since the problem is binary, it suffices to specify the negative prevalence, since the positive is constrained
+ train_sample = train.sampling(training_size, 1-training_prevalence)
+ model.fit(train_sample)
+ true_prev, estim_prev = qp.evaluation.prediction(model, APP(test, repeats=100, random_state=0))
+ method_name = 'CC$_{'+f'{int(100*training_prevalence)}' + '\%}$'
+ method_data.append((method_name, true_prev, estim_prev, train_sample.prevalence()))
+
+ return zip(*method_data)
+```
+
+and the plot should now look like:
+
+
+
+which clearly shows a negative bias for CC variants trained on
+data containing more negatives (i.e., < 50%) and positive biases
+in cases containing more positives (i.e., >50%). The CC trained
+at 50% behaves as an unbiased estimator of the positive class
+prevalence.
+
+The function _qp.plot.binary_bias_bins_ allows the user to
+generate box plots broken down by bins of true test prevalence.
+To this aim, an argument _nbins_ is passed which indicates
+how many isometric subintervals to take. For example
+the following plot is produced for _nbins=3_:
+
+
+
+Interestingly enough, the seemingly unbiased estimator (CC at 50%) happens to display
+a positive bias (or a tendency to overestimate) in cases of low prevalence
+(i.e., when the true prevalence of the positive class is below 33%),
+and a negative bias (or a tendency to underestimate) in cases of high prevalence
+(i.e., when the true prevalence is beyond 67%).
+
+Out of curiosity, the diagonal plot for this experiment looks like:
+
+
+
+showing pretty clearly the dependency of CC on the prior probabilities
+of the labeled set it was trained on.
+
+
+## Error by Drift
+
+Above discussed plots are useful for analyzing and comparing
+the performance of different quantification methods, but are
+limited to the binary case. The "error by drift" is a plot
+that shows the error in predictions as a function of the
+(prior probability) drift between each test sample and the
+training set. Interestingly, the error and drift can both be measured
+in terms of any evaluation measure for quantification (like the
+ones available in _qp.error_) and can thus be computed
+irrespectively of the number of classes.
+
+The following shows how to generate the plot for the 4 CC variants,
+using 10 bins for the drift
+and _absolute error_ as the measure of the error (the
+drift in the x-axis is always computed in terms of _absolute error_ since
+other errors are harder to interpret):
+
+```python
+qp.plot.error_by_drift(method_names, true_prevs, estim_prevs, tr_prevs,
+ error_name='ae', n_bins=10, savepath='./plots/err_drift.png')
+```
+
+
+
+Note that all methods work reasonably well in cases of low prevalence
+drift (i.e., any CC-variant is a good quantifier whenever the IID
+assumption is approximately preserved). The higher the drift, the worse
+those quantifiers tend to perform, although it is clear that PACC
+yields the lowest error for the most difficult cases.
+
+Remember that any plot can be generated _across many datasets_, and
+that this would probably result in a more solid comparison.
+In those cases, however, it is likely that the variances of each
+method get higher, to the detriment of the visualization.
+We recommend to set _show_std=False_ in those cases
+in order to hide the color bands.
diff --git a/docs/source/wiki_editable/Protocols.md b/docs/source/wiki_editable/Protocols.md
new file mode 100644
index 0000000..1d6193e
--- /dev/null
+++ b/docs/source/wiki_editable/Protocols.md
@@ -0,0 +1,177 @@
+# Protocols
+
+_New in v0.1.7!_
+
+Quantification methods are expected to behave robustly in the presence of
+shift. For this reason, quantification methods need to be confronted with
+samples exhibiting widely varying amounts of shift.
+_Protocols_ implement specific ways for generating such samples.
+
+In QuaPy, a protocol is an instance of _AbstractProtocol_ implementing a
+_call_ method that returns a generator yielding a tuple _(sample, prev)_
+every time. The protocol can also implement the function _total()_ informing
+of the number of total samples that the protocol generates.
+
+Protocols can inherit from _AbstractStochasticSeededProtocol_, the class of
+protocols that generate samples stochastically, but that can be set with
+a seed in order to allow for replicating the exact same samples. This is important
+for evaluation purposes, since we typically require all our methods be evaluated
+on the exact same test samples in order to allow for a fair comparison.
+Indeed, the seed is set by default to 0, since this is the most commonly
+desired behaviour. Indicate _radom_state=None_ for allowing different sequences of samples to be
+generated every time the protocol is invoked.
+
+Protocols that also inherit from _OnLabelledCollectionProtocol_ are such that
+samples are generated from a _LabelledCollection_ object (e.g., a test collection,
+or a validation collection). These protocols also allow for generating sequences of
+_LabelledCollection_ instead of _(sample, prev)_ by indicating
+_return_type='labelled_collection'_ instead of the default value _return_type='sample_prev'_.
+
+For a more technical explanation on _AbstractStochasticSeededProtocol_ and
+_OnLabelledCollectionProtocol_, see the "custom_protocol.py" provided in the
+example folder.
+
+QuaPy provides implementations of most popular sample generation protocols
+used in literature. This is the subject of the following sections.
+
+
+## Artificial-Prevalence Protocol
+
+The "artificial-sampling protocol" (APP) proposed by
+[Forman (2005)](https://link.springer.com/chapter/10.1007/11564096_55)
+is likely the most popular protocol used for quantification evaluation.
+In APP, a test set is used to generate samples at
+desired prevalence values covering the full spectrum.
+
+In APP, the user specifies the number
+of (equally distant) points to be generated from the interval [0,1];
+in QuaPy this is achieved by setting _n_prevpoints_.
+For example, if _n_prevpoints=11_ then, for each class, the prevalence values
+[0., 0.1, 0.2, ..., 1.] will be used. This means that, for two classes,
+the number of different prevalence values will be 11 (since, once the prevalence
+of one class is determined, the other one is constrained). For 3 classes,
+the number of valid combinations can be obtained as 11 + 10 + ... + 1 = 66.
+In general, the number of valid combinations that will be produced for a given
+value of n_prevpoints can be consulted by invoking
+_num_prevalence_combinations_, e.g.:
+
+```python
+import quapy.functional as F
+n_prevpoints = 21
+n_classes = 4
+n = F.num_prevalence_combinations(n_prevpoints, n_classes, n_repeats=1)
+```
+
+in this example, _n=1771_. Note the last argument, _n_repeats_, that
+informs of the number of examples that will be generated for any
+valid combination (typical values are, e.g., 1 for a single sample,
+or 10 or higher for computing standard deviations of performing statistical
+significance tests).
+
+One can instead work the other way around, i.e., one could decide for a
+maximum budged of evaluations and get the number of prevalence points that
+will give rise to a number of evaluations close, but not higher, than
+this budget. This can be achieved with the function
+_get_nprevpoints_approximation_, e.g.:
+
+```python
+budget = 5000
+n_prevpoints = F.get_nprevpoints_approximation(budget, n_classes, n_repeats=1)
+n = F.num_prevalence_combinations(n_prevpoints, n_classes, n_repeats=1)
+print(f'by setting n_prevpoints={n_prevpoints} the number of evaluations for {n_classes} classes will be {n}')
+```
+this will produce the following output:
+```
+by setting n_prevpoints=30 the number of evaluations for 4 classes will be 4960
+```
+
+The following code shows an example of usage of APP for model selection
+and evaluation:
+
+```python
+import quapy as qp
+from quapy.method.aggregative import ACC
+from quapy.protocol import APP
+import numpy as np
+from sklearn.linear_model import LogisticRegression
+
+qp.environ['SAMPLE_SIZE'] = 100
+qp.environ['N_JOBS'] = -1
+
+# define an instance of our custom quantifier
+quantifier = ACC(LogisticRegression())
+
+# load the IMDb dataset
+train, test = qp.datasets.fetch_reviews('imdb', tfidf=True, min_df=5).train_test
+
+# model selection
+train, val = train.split_stratified(train_prop=0.75)
+quantifier = qp.model_selection.GridSearchQ(
+ quantifier,
+ param_grid={'classifier__C': np.logspace(-2, 2, 5)},
+ protocol=APP(val) # <- this is the protocol we use for generating validation samples
+).fit(train)
+
+# default values are n_prevalences=21, repeats=10, random_state=0; this is equialent to:
+# val_app = APP(val, n_prevalences=21, repeats=10, random_state=0)
+# quantifier = GridSearchQ(quantifier, param_grid, protocol=val_app).fit(train)
+
+# evaluation with APP
+mae = qp.evaluation.evaluate(quantifier, protocol=APP(test), error_metric='mae')
+print(f'MAE = {mae:.4f}')
+```
+
+Note that APP is an instance of _AbstractStochasticSeededProtocol_ and that the
+_random_state_ is by default set to 0, meaning that all the generated validation
+samples will be consistent for all the combinations of hyperparameters being tested.
+Note also that the _sample_size_ is not indicated when instantiating the protocol;
+in such cases QuaPy takes the value of _qp.environ['SAMPLE_SIZE']_.
+
+This protocol is useful for testing a quantifier under conditions of
+_prior probability shift_.
+
+## Sampling from the unit-simplex, the Uniform-Prevalence Protocol (UPP)
+
+Generating all possible combinations from a grid of prevalence values (APP) in
+multiclass is cumbersome, and when the number of classes increases it rapidly
+becomes impractical. In some cases, it is preferable to generate a fixed number
+of samples displaying prevalence values that are uniformly drawn from the unit-simplex,
+that is, so that every legitimate distribution is equally likely. The main drawback
+of this approach is that we are not guaranteed that all classes have been tested
+in the entire range of prevalence values. The main advantage is that every possible
+prevalence value is electable (this was not possible with standard APP, since values
+not included in the grid are never tested). Yet another advantage is that we can
+control the computational burden every evaluation incurs, by deciding in advance
+the number of samples to generate.
+
+The UPP protocol implements this idea by relying on the Kraemer algorithm
+for sampling from the unit-simplex as many vectors of prevalence values as indicated
+in the _repeats_ parameter. UPP can be instantiated as:
+
+```python
+protocol = qp.in_protocol.UPP(test, repeats=100)
+```
+
+This is the most convenient protocol for datasets
+containing many classes; see, e.g.,
+[LeQua (2022)](https://ceur-ws.org/Vol-3180/paper-146.pdf),
+and is useful for testing a quantifier under conditions of
+_prior probability shift_.
+
+
+## Natural-Prevalence Protocol
+
+The "natural-prevalence protocol" (NPP) comes down to generating samples drawn
+uniformly at random from the original labelled collection. This protocol has
+sometimes been used in literature, although it is now considered to be deprecated,
+due to its limited capability to generate interesting amounts of shift.
+All other things being equal, this protocol can be used just like APP or UPP,
+and is instantiated via:
+
+```python
+protocol = qp.in_protocol.NPP(test, repeats=100)
+```
+
+## Other protocols
+
+Other protocols exist in QuaPy and will be added to the `qp.protocol.py` module.
\ No newline at end of file
diff --git a/docs/source/wiki_editable/wiki_examples/selected_plots/bin_bias.png b/docs/source/wiki_editable/wiki_examples/selected_plots/bin_bias.png
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