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cleaning and updating changelog

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Alejandro Moreo Fernandez 2024-05-30 11:41:23 +02:00
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3
CHANGE_LOG.txt
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@ -1,6 +1,7 @@
Change Log 0.1.9
----------------
- [TODO] add LeQua2024
- [TODO] add LeQua2024 and normalized match distance to qp.error
- [TODO] add Friedman's method and DeBias
- Added a default classifier for aggregative quantifiers, which now can be instantiated without specifying
the classifier. The default classifier can be accessed in qp.environ['DEFAULT_CLS'] and is assigned to

6
README.md
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@ -13,7 +13,7 @@ for facilitating the analysis and interpretation of the experimental results.
### Last updates:
* Version 0.1.8 is released! major changes can be consulted [here](CHANGE_LOG.txt).
* Version 0.1.9 is released! major changes can be consulted [here](CHANGE_LOG.txt).
* The developer API documentation is available [here](https://hlt-isti.github.io/QuaPy/build/html/modules.html)
### Installation
@ -24,7 +24,7 @@ pip install quapy
### Cite QuaPy
If you find QuaPy useful (and we hope you will), plese consider citing the original paper in your research:
If you find QuaPy useful (and we hope you will), please consider citing the original paper in your research:
```
@inproceedings{moreo2021quapy,
@ -68,7 +68,7 @@ class prevalence of the training set. For this reason, any quantification model
should be tested across many samples, even ones characterized by class prevalence
values different or very different from those found in the training set.
QuaPy implements sampling procedures and evaluation protocols that automate this workflow.
See the [Wiki](https://github.com/HLT-ISTI/QuaPy/wiki) for detailed examples.
See the [documentation](https://hlt-isti.github.io/QuaPy/build/html/) for detailed examples.
## Features

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@ -708,16 +708,18 @@
</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>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
<li><a href="quapy.data.html#quapy.data.base.Dataset.load">load() (quapy.data.base.Dataset class method)</a>
<ul>
<li><a href="quapy.data.html#quapy.data.base.LabelledCollection.load">(quapy.data.base.LabelledCollection class method)</a>
</li>
</ul></li>
<li><a href="quapy.html#quapy.util.load_report">load_report() (in module quapy.util)</a>
</li>
<li><a href="quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression">LowRankLogisticRegression (class in quapy.classification.methods)</a>
</li>
<li><a href="quapy.classification.html#quapy.classification.neural.LSTMnet">LSTMnet (class in quapy.classification.neural)</a>

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docs/build/html/index.html vendored
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@ -765,6 +765,7 @@
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.util.download_file"><code class="docutils literal notranslate"><span class="pre">download_file()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.util.download_file_if_not_exists"><code class="docutils literal notranslate"><span class="pre">download_file_if_not_exists()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.util.get_quapy_home"><code class="docutils literal notranslate"><span class="pre">get_quapy_home()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.util.load_report"><code class="docutils literal notranslate"><span class="pre">load_report()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.util.map_parallel"><code class="docutils literal notranslate"><span class="pre">map_parallel()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.util.parallel"><code class="docutils literal notranslate"><span class="pre">parallel()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="quapy.html#quapy.util.parallel_unpack"><code class="docutils literal notranslate"><span class="pre">parallel_unpack()</span></code></a></li>

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docs/build/html/modules.html vendored
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@ -23,7 +23,7 @@
<link rel="index" title="Index" href="genindex.html" />
<link rel="search" title="Search" href="search.html" />
<link rel="next" title="quapy package" href="quapy.html" />
<link rel="prev" title="Welcome to QuaPy’s documentation!" href="index.html" />
<link rel="prev" title="Protocols" href="wiki/Protocols.html" />
</head>
<body class="wy-body-for-nav">
@ -45,7 +45,16 @@
</form>
</div>
</div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
<ul class="current">
<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 class="current">
<li class="toctree-l1 current"><a class="current reference internal" href="#">quapy</a><ul>
<li class="toctree-l2"><a class="reference internal" href="quapy.html">quapy package</a></li>
</ul>
@ -275,6 +284,7 @@
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.util.download_file"><code class="docutils literal notranslate"><span class="pre">download_file()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.util.download_file_if_not_exists"><code class="docutils literal notranslate"><span class="pre">download_file_if_not_exists()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.util.get_quapy_home"><code class="docutils literal notranslate"><span class="pre">get_quapy_home()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.util.load_report"><code class="docutils literal notranslate"><span class="pre">load_report()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.util.map_parallel"><code class="docutils literal notranslate"><span class="pre">map_parallel()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.util.parallel"><code class="docutils literal notranslate"><span class="pre">parallel()</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="quapy.html#quapy.util.parallel_unpack"><code class="docutils literal notranslate"><span class="pre">parallel_unpack()</span></code></a></li>
@ -295,7 +305,7 @@
</div>
</div>
<footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
<a href="index.html" class="btn btn-neutral float-left" title="Welcome to QuaPy’s documentation!" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
<a href="wiki/Protocols.html" class="btn btn-neutral float-left" title="Protocols" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
<a href="quapy.html" class="btn btn-neutral float-right" title="quapy package" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
</div>

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@ -45,7 +45,16 @@
</form>
</div>
</div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
<ul class="current">
<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 class="current">
<li class="toctree-l1 current"><a class="reference internal" href="modules.html">quapy</a><ul class="current">
<li class="toctree-l2 current"><a class="reference internal" href="quapy.html">quapy package</a><ul class="current">
<li class="toctree-l3 current"><a class="reference internal" href="quapy.html#subpackages">Subpackages</a><ul class="current">

33
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@ -45,7 +45,16 @@
</form>
</div>
</div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
<ul class="current">
<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 class="current">
<li class="toctree-l1 current"><a class="reference internal" href="modules.html">quapy</a><ul class="current">
<li class="toctree-l2 current"><a class="reference internal" href="quapy.html">quapy package</a><ul class="current">
<li class="toctree-l3 current"><a class="reference internal" href="quapy.html#subpackages">Subpackages</a><ul class="current">
@ -628,8 +637,8 @@ with replacement.</p>
information on this dataset, please follow the zenodo link).
This dataset is based on the data available publicly at
<a class="reference external" href="https://github.com/hsosik/WHOI-Plankton">WHOI-Plankton repo</a>.
The scripts for the processing are available at <a class="reference external" href="https://github.com/pglez82/IFCB_Zenodo">P. González’s repo</a>.
Basically, this is the IFCB dataset with precomputed features for testing quantification algorithms.</p>
The dataset already comes with processed features.
The scripts used for the processing are available at <a class="reference external" href="https://github.com/pglez82/IFCB_Zenodo">P. González’s repo</a>.</p>
<p>The datasets are downloaded only once, and stored for fast reuse.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
@ -722,7 +731,7 @@ This can be reproduced by using <a class="reference internal" href="#quapy.data.
<dl class="py function">
<dt class="sig sig-object py" id="quapy.data.datasets.fetch_UCIMulticlassDataset">
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_UCIMulticlassDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</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">test_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_UCIMulticlassDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_UCIMulticlassDataset" title="Permalink to this definition"></a></dt>
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_UCIMulticlassDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</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">min_test_split</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_train_instances</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">25000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_class_support</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">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><span class="pre">Dataset</span></a></span></span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_UCIMulticlassDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_UCIMulticlassDataset" title="Permalink to this definition"></a></dt>
<dd><p>Loads a UCI multiclass dataset as an instance of <a class="reference internal" href="#quapy.data.base.Dataset" title="quapy.data.base.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">quapy.data.base.Dataset</span></code></a>.</p>
<p>The list of available datasets is taken from <a class="reference external" href="https://archive.ics.uci.edu/">https://archive.ics.uci.edu/</a>, following these criteria:
- It has more than 1000 instances
@ -743,7 +752,13 @@ This can be reproduced by using <a class="reference internal" href="#quapy.data.
<li><p><strong>dataset_name</strong> – a dataset name</p></li>
<li><p><strong>data_home</strong> – specify the quapy home directory where collections will be dumped (leave empty to use the default
~/quay_data/ directory)</p></li>
<li><p><strong>test_split</strong> – proportion of documents to be included in the test set. The rest conforms the training set</p></li>
<li><p><strong>min_test_split</strong> – minimum proportion of instances to be included in the test set. This value is interpreted
as a minimum proportion, meaning that the real proportion could be higher in case the training proportion
(1-<cite>min_test_split`% of the instances) surpasses `max_train_instances</cite>. In such case, only <cite>max_train_instances</cite>
are taken for training, and the rest (irrespective of <cite>min_test_split</cite>) is taken for test.</p></li>
<li><p><strong>max_train_instances</strong> – maximum number of instances to keep for training (defaults to 25000)</p></li>
<li><p><strong>min_class_support</strong> – minimum number of istances per class. Classes with fewer instances
are discarded (deafult is 100)</p></li>
<li><p><strong>verbose</strong> – set to True (default is False) to get information (stats) about the dataset</p></li>
</ul>
</dd>
@ -755,7 +770,7 @@ This can be reproduced by using <a class="reference internal" href="#quapy.data.
<dl class="py function">
<dt class="sig sig-object py" id="quapy.data.datasets.fetch_UCIMulticlassLabelledCollection">
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_UCIMulticlassLabelledCollection</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</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">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_UCIMulticlassLabelledCollection"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_UCIMulticlassLabelledCollection" title="Permalink to this definition"></a></dt>
<span class="sig-prename descclassname"><span class="pre">quapy.data.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_UCIMulticlassLabelledCollection</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</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">min_class_support</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">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#quapy.data.base.LabelledCollection" title="quapy.data.base.LabelledCollection"><span class="pre">LabelledCollection</span></a></span></span><a class="reference internal" href="_modules/quapy/data/datasets.html#fetch_UCIMulticlassLabelledCollection"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.data.datasets.fetch_UCIMulticlassLabelledCollection" title="Permalink to this definition"></a></dt>
<dd><p>Loads a UCI multiclass collection as 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">quapy.data.base.LabelledCollection</span></code></a>.</p>
<p>The list of available datasets is taken from <a class="reference external" href="https://archive.ics.uci.edu/">https://archive.ics.uci.edu/</a>, following these criteria:
- It has more than 1000 instances
@ -776,7 +791,9 @@ This can be reproduced by using <a class="reference internal" href="#quapy.data.
<li><p><strong>dataset_name</strong> – a dataset name</p></li>
<li><p><strong>data_home</strong> – specify the quapy home directory where the dataset will be dumped (leave empty to use the default
~/quay_data/ directory)</p></li>
<li><p><strong>test_split</strong> – proportion of documents to be included in the test set. The rest conforms the training set</p></li>
<li><p><strong>test_split</strong> – proportion of instances to be included in the test set. The rest conforms the training set</p></li>
<li><p><strong>min_class_support</strong> – minimum number of istances per class. Classes with fewer instances
are discarded (deafult is 100)</p></li>
<li><p><strong>verbose</strong> – set to True (default is False) to get information (stats) about the dataset</p></li>
</ul>
</dd>
@ -798,7 +815,7 @@ We refer to the <a class="reference external" href="https://ceur-ws.org/Vol-3180
A Detailed Overview of LeQua&#64; CLEF 2022: Learning to Quantify.</a> for a detailed description
on the tasks and datasets.</p>
<p>The datasets are downloaded only once, and stored for fast reuse.</p>
<p>See <cite>lequa2022_experiments.py</cite> provided in the example folder, that can serve as a guide on how to use these
<p>See <cite>4.lequa2022_experiments.py</cite> provided in the example folder, that can serve as a guide on how to use these
datasets.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>

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@ -46,7 +46,16 @@
</form>
</div>
</div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
<ul class="current">
<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 class="current">
<li class="toctree-l1 current"><a class="reference internal" href="modules.html">quapy</a><ul class="current">
<li class="toctree-l2 current"><a class="current reference internal" href="#">quapy package</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#subpackages">Subpackages</a><ul>
@ -162,6 +171,7 @@
<li class="toctree-l4"><a class="reference internal" href="#quapy.util.download_file"><code class="docutils literal notranslate"><span class="pre">download_file()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#quapy.util.download_file_if_not_exists"><code class="docutils literal notranslate"><span class="pre">download_file_if_not_exists()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#quapy.util.get_quapy_home"><code class="docutils literal notranslate"><span class="pre">get_quapy_home()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#quapy.util.load_report"><code class="docutils literal notranslate"><span class="pre">load_report()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#quapy.util.map_parallel"><code class="docutils literal notranslate"><span class="pre">map_parallel()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#quapy.util.parallel"><code class="docutils literal notranslate"><span class="pre">parallel()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="#quapy.util.parallel_unpack"><code class="docutils literal notranslate"><span class="pre">parallel_unpack()</span></code></a></li>
@ -2928,6 +2938,11 @@ This directory is <cite>~/quapy_data</cite></p>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="quapy.util.load_report">
<span class="sig-prename descclassname"><span class="pre">quapy.util.</span></span><span class="sig-name descname"><span class="pre">load_report</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">as_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/util.html#load_report"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.util.load_report" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="quapy.util.map_parallel">
<span class="sig-prename descclassname"><span class="pre">quapy.util.</span></span><span class="sig-name descname"><span class="pre">map_parallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/util.html#map_parallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.util.map_parallel" title="Permalink to this definition"></a></dt>

54
docs/build/html/quapy.method.html vendored
View File

@ -45,7 +45,16 @@
</form>
</div>
</div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
<ul class="current">
<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 class="current">
<li class="toctree-l1 current"><a class="reference internal" href="modules.html">quapy</a><ul class="current">
<li class="toctree-l2 current"><a class="reference internal" href="quapy.html">quapy package</a><ul class="current">
<li class="toctree-l3 current"><a class="reference internal" href="quapy.html#subpackages">Subpackages</a><ul class="current">
@ -104,7 +113,7 @@
<span id="quapy-method-aggregative-module"></span><h2>quapy.method.aggregative module<a class="headerlink" href="#module-quapy.method.aggregative" title="Permalink to this heading"></a></h2>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.aggregative.ACC">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">ACC</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">solver</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Literal</span><span class="p"><span class="pre">[</span></span><span class="s"><span class="pre">'minimize'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'exact'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'exact-raise'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'exact-cc'</span></span><span class="p"><span class="pre">]</span></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">'minimize'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Literal</span><span class="p"><span class="pre">[</span></span><span class="s"><span class="pre">'inversion'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'invariant-ratio'</span></span><span class="p"><span class="pre">]</span></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">'inversion'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Literal</span><span class="p"><span class="pre">[</span></span><span class="s"><span class="pre">'clip'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'mapsimplex'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'condsoftmax'</span></span><span class="p"><span class="pre">]</span></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">'clip'</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/aggregative.html#ACC"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.ACC" 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.aggregative.</span></span><span class="sig-name descname"><span class="pre">ACC</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">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">BaseEstimator</span><span class="p"><span class="pre">]</span></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">None</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">solver</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Literal</span><span class="p"><span class="pre">[</span></span><span class="s"><span class="pre">'minimize'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'exact'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'exact-raise'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'exact-cc'</span></span><span class="p"><span class="pre">]</span></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">'minimize'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Literal</span><span class="p"><span class="pre">[</span></span><span class="s"><span class="pre">'inversion'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'invariant-ratio'</span></span><span class="p"><span class="pre">]</span></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">'inversion'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Literal</span><span class="p"><span class="pre">[</span></span><span class="s"><span class="pre">'clip'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'mapsimplex'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'condsoftmax'</span></span><span class="p"><span class="pre">]</span></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">'clip'</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/aggregative.html#ACC"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.ACC" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeCrispQuantifier" title="quapy.method.aggregative.AggregativeCrispQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">AggregativeCrispQuantifier</span></code></a></p>
<p><a class="reference external" href="https://link.springer.com/article/10.1007/s10618-008-0097-y">Adjusted Classify &amp; Count</a>,
the “adjusted” variant of <a class="reference internal" href="#quapy.method.aggregative.CC" title="quapy.method.aggregative.CC"><code class="xref py py-class docutils literal notranslate"><span class="pre">CC</span></code></a>, that corrects the predictions of CC
@ -512,7 +521,7 @@ about soft predictions.</p>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.aggregative.BayesianCC">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">BayesianCC</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="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</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">0.75</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_warmup</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">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</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">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mcmc_seed</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">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#BayesianCC"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.BayesianCC" 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.aggregative.</span></span><span class="sig-name descname"><span class="pre">BayesianCC</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">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">BaseEstimator</span><span class="p"><span class="pre">]</span></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">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_split</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</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">0.75</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_warmup</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">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</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">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mcmc_seed</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">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#BayesianCC"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.BayesianCC" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeCrispQuantifier" title="quapy.method.aggregative.AggregativeCrispQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">AggregativeCrispQuantifier</span></code></a></p>
<p><a class="reference external" href="https://arxiv.org/abs/2302.09159">Bayesian quantification</a> method,
which is a variant of <a class="reference internal" href="#quapy.method.aggregative.ACC" title="quapy.method.aggregative.ACC"><code class="xref py py-class docutils literal notranslate"><span class="pre">ACC</span></code></a> that calculates the posterior probability distribution
@ -623,7 +632,7 @@ on which the predictions are to be generated.</p></li>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.aggregative.CC">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">CC</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><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#CC"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.CC" 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.aggregative.</span></span><span class="sig-name descname"><span class="pre">CC</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">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">BaseEstimator</span><span class="p"><span class="pre">]</span></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">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#CC"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.CC" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeCrispQuantifier" title="quapy.method.aggregative.AggregativeCrispQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">AggregativeCrispQuantifier</span></code></a></p>
<p>The most basic Quantification method. One that simply classifies all instances and counts how many have been
attributed to each of the classes in order to compute class prevalence estimates.</p>
@ -670,7 +679,7 @@ attributed to each of the classes in order to compute class prevalence estimates
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.aggregative.DMy">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">DMy</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">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">nbins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">divergence</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">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Callable</span><span class="p"><span class="pre">]</span></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">'HD'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cdf</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">search</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'optim_minimize'</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/aggregative.html#DMy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.DMy" 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.aggregative.</span></span><span class="sig-name descname"><span class="pre">DMy</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">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">BaseEstimator</span><span class="p"><span class="pre">]</span></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">None</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">nbins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">divergence</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">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Callable</span><span class="p"><span class="pre">]</span></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">'HD'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cdf</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">search</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'optim_minimize'</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/aggregative.html#DMy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.DMy" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeSoftQuantifier" title="quapy.method.aggregative.AggregativeSoftQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">AggregativeSoftQuantifier</span></code></a></p>
<p>Generic Distribution Matching quantifier for binary or multiclass quantification based on the space of posterior
probabilities. This implementation takes the number of bins, the divergence, and the possibility to work on CDF
@ -743,7 +752,7 @@ as instances, the posterior probabilities issued by the classifier and, as label
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.aggregative.DyS">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">DyS</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_bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">divergence</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">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Callable</span><span class="p"><span class="pre">]</span></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">'HD'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</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/aggregative.html#DyS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.DyS" 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.aggregative.</span></span><span class="sig-name descname"><span class="pre">DyS</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">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">BaseEstimator</span><span class="p"><span class="pre">]</span></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">None</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_bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">divergence</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">str</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Callable</span><span class="p"><span class="pre">]</span></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">'HD'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</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/aggregative.html#DyS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.DyS" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeSoftQuantifier" title="quapy.method.aggregative.AggregativeSoftQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">AggregativeSoftQuantifier</span></code></a>, <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><a class="reference external" href="https://ojs.aaai.org/index.php/AAAI/article/view/4376">DyS framework</a> (DyS).
DyS is a generalization of HDy method, using a Ternary Search in order to find the prevalence that
@ -796,7 +805,7 @@ as instances, the posterior probabilities issued by the classifier and, as label
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.aggregative.EMQ">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">EMQ</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">exact_train_prev</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">recalib</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/aggregative.html#EMQ"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.EMQ" 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.aggregative.</span></span><span class="sig-name descname"><span class="pre">EMQ</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">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">BaseEstimator</span><span class="p"><span class="pre">]</span></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">None</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">exact_train_prev</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">recalib</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/aggregative.html#EMQ"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.EMQ" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeSoftQuantifier" title="quapy.method.aggregative.AggregativeSoftQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">AggregativeSoftQuantifier</span></code></a></p>
<p><a class="reference external" href="https://ieeexplore.ieee.org/abstract/document/6789744">Expectation Maximization for Quantification</a> (EMQ),
aka <cite>Saerens-Latinne-Decaestecker</cite> (SLD) algorithm.
@ -953,7 +962,7 @@ to be recalibrated, then these posteriors are recalibrated accordingly.</p>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.aggregative.HDy">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">HDy</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><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#HDy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.HDy" 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.aggregative.</span></span><span class="sig-name descname"><span class="pre">HDy</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">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">BaseEstimator</span><span class="p"><span class="pre">]</span></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">None</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><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#HDy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.HDy" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeSoftQuantifier" title="quapy.method.aggregative.AggregativeSoftQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">AggregativeSoftQuantifier</span></code></a>, <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><a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S0020025512004069">Hellinger Distance y</a> (HDy).
HDy is a probabilistic method for training binary quantifiers, that models quantification as the problem of
@ -1068,7 +1077,7 @@ probabilities are independent of each other, meaning that, in general, they do n
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.aggregative.PACC">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">PACC</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">solver</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Literal</span><span class="p"><span class="pre">[</span></span><span class="s"><span class="pre">'minimize'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'exact'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'exact-raise'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'exact-cc'</span></span><span class="p"><span class="pre">]</span></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">'minimize'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Literal</span><span class="p"><span class="pre">[</span></span><span class="s"><span class="pre">'inversion'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'invariant-ratio'</span></span><span class="p"><span class="pre">]</span></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">'inversion'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Literal</span><span class="p"><span class="pre">[</span></span><span class="s"><span class="pre">'clip'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'mapsimplex'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'condsoftmax'</span></span><span class="p"><span class="pre">]</span></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">'clip'</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/aggregative.html#PACC"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.PACC" 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.aggregative.</span></span><span class="sig-name descname"><span class="pre">PACC</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">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">BaseEstimator</span><span class="p"><span class="pre">]</span></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">None</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">solver</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Literal</span><span class="p"><span class="pre">[</span></span><span class="s"><span class="pre">'minimize'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'exact'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'exact-raise'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'exact-cc'</span></span><span class="p"><span class="pre">]</span></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">'minimize'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Literal</span><span class="p"><span class="pre">[</span></span><span class="s"><span class="pre">'inversion'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'invariant-ratio'</span></span><span class="p"><span class="pre">]</span></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">'inversion'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Literal</span><span class="p"><span class="pre">[</span></span><span class="s"><span class="pre">'clip'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'mapsimplex'</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="s"><span class="pre">'condsoftmax'</span></span><span class="p"><span class="pre">]</span></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">'clip'</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/aggregative.html#PACC"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.PACC" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeSoftQuantifier" title="quapy.method.aggregative.AggregativeSoftQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">AggregativeSoftQuantifier</span></code></a></p>
<p><a class="reference external" href="https://ieeexplore.ieee.org/abstract/document/5694031">Probabilistic Adjusted Classify &amp; Count</a>,
the probabilistic variant of ACC that relies on the posterior probabilities returned by a probabilistic classifier.</p>
@ -1153,7 +1162,7 @@ as instances, the posterior probabilities issued by the classifier and, as label
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.aggregative.PCC">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">PCC</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><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#PCC"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.PCC" 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.aggregative.</span></span><span class="sig-name descname"><span class="pre">PCC</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">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">BaseEstimator</span><span class="p"><span class="pre">]</span></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">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#PCC"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.PCC" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeSoftQuantifier" title="quapy.method.aggregative.AggregativeSoftQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">AggregativeSoftQuantifier</span></code></a></p>
<p><a class="reference external" href="https://ieeexplore.ieee.org/abstract/document/5694031">Probabilistic Classify &amp; Count</a>,
the probabilistic variant of CC that relies on the posterior probabilities returned by a probabilistic classifier.</p>
@ -1212,7 +1221,7 @@ the probabilistic variant of CC that relies on the posterior probabilities retur
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method.aggregative.SMM">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method.aggregative.</span></span><span class="sig-name descname"><span class="pre">SMM</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><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#SMM"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.SMM" 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.aggregative.</span></span><span class="sig-name descname"><span class="pre">SMM</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">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">BaseEstimator</span><span class="p"><span class="pre">]</span></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">None</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><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/aggregative.html#SMM"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method.aggregative.SMM" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeSoftQuantifier" title="quapy.method.aggregative.AggregativeSoftQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">AggregativeSoftQuantifier</span></code></a>, <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><a class="reference external" href="https://ieeexplore.ieee.org/document/9260028">SMM method</a> (SMM).
SMM is a simplification of matching distribution methods where the representation of the examples
@ -1481,7 +1490,7 @@ function returns <span class="math notranslate nohighlight">\(e^{s}\)</span></p>
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method._kdey.KDEyCS">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method._kdey.</span></span><span class="sig-name descname"><span class="pre">KDEyCS</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">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</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/_kdey.html#KDEyCS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._kdey.KDEyCS" 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._kdey.</span></span><span class="sig-name descname"><span class="pre">KDEyCS</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">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">BaseEstimator</span><span class="p"><span class="pre">]</span></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">None</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">bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_kdey.html#KDEyCS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._kdey.KDEyCS" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeSoftQuantifier" title="quapy.method.aggregative.AggregativeSoftQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">AggregativeSoftQuantifier</span></code></a></p>
<p>Kernel Density Estimation model for quantification (KDEy) relying on the Cauchy-Schwarz divergence (CS) as
the divergence measure to be minimized. This method was first proposed in the paper
@ -1509,7 +1518,6 @@ for <cite>k</cite>); or as a collection defining the specific set of data to use
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>
<li><p><strong>bandwidth</strong> – float, the bandwidth of the Kernel</p></li>
<li><p><strong>n_jobs</strong> – number of parallel workers</p></li>
</ul>
</dd>
</dl>
@ -1551,7 +1559,7 @@ as instances, the predictions issued by the classifier and, as labels, the true
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method._kdey.KDEyHD">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method._kdey.</span></span><span class="sig-name descname"><span class="pre">KDEyHD</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">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">divergence</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</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">'HD'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</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>, <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>, <em class="sig-param"><span class="n"><span class="pre">montecarlo_trials</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_kdey.html#KDEyHD"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._kdey.KDEyHD" 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._kdey.</span></span><span class="sig-name descname"><span class="pre">KDEyHD</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">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">BaseEstimator</span><span class="p"><span class="pre">]</span></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">None</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">divergence</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</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">'HD'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</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>, <em class="sig-param"><span class="n"><span class="pre">montecarlo_trials</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_kdey.html#KDEyHD"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._kdey.KDEyHD" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeSoftQuantifier" title="quapy.method.aggregative.AggregativeSoftQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">AggregativeSoftQuantifier</span></code></a>, <a class="reference internal" href="#quapy.method._kdey.KDEBase" title="quapy.method._kdey.KDEBase"><code class="xref py py-class docutils literal notranslate"><span class="pre">KDEBase</span></code></a></p>
<p>Kernel Density Estimation model for quantification (KDEy) relying on the squared Hellinger Disntace (HD) as
the divergence measure to be minimized. This method was first proposed in the paper
@ -1583,7 +1591,6 @@ for <cite>k</cite>); or as a collection defining the specific set of data to use
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>
<li><p><strong>bandwidth</strong> – float, the bandwidth of the Kernel</p></li>
<li><p><strong>n_jobs</strong> – number of parallel workers</p></li>
<li><p><strong>random_state</strong> – a seed to be set before fitting any base quantifier (default None)</p></li>
<li><p><strong>montecarlo_trials</strong> – number of Monte Carlo trials (default 10000)</p></li>
</ul>
@ -1622,7 +1629,7 @@ as instances, the predictions issued by the classifier and, as labels, the true
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method._kdey.KDEyML">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">quapy.method._kdey.</span></span><span class="sig-name descname"><span class="pre">KDEyML</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">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</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>, <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/method/_kdey.html#KDEyML"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._kdey.KDEyML" 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._kdey.</span></span><span class="sig-name descname"><span class="pre">KDEyML</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">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">BaseEstimator</span><span class="p"><span class="pre">]</span></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">None</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">bandwidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</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/method/_kdey.html#KDEyML"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._kdey.KDEyML" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method.aggregative.AggregativeSoftQuantifier" title="quapy.method.aggregative.AggregativeSoftQuantifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">AggregativeSoftQuantifier</span></code></a>, <a class="reference internal" href="#quapy.method._kdey.KDEBase" title="quapy.method._kdey.KDEBase"><code class="xref py py-class docutils literal notranslate"><span class="pre">KDEBase</span></code></a></p>
<p>Kernel Density Estimation model for quantification (KDEy) relying on the Kullback-Leibler divergence (KLD) as
the divergence measure to be minimized. This method was first proposed in the paper
@ -1652,7 +1659,6 @@ for <cite>k</cite>); or as a collection defining the specific set of data to use
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>
<li><p><strong>bandwidth</strong> – float, the bandwidth of the Kernel</p></li>
<li><p><strong>n_jobs</strong> – number of parallel workers</p></li>
<li><p><strong>random_state</strong> – a seed to be set before fitting any base quantifier (default None)</p></li>
</ul>
</dd>
@ -1746,7 +1752,7 @@ quantification. This implementation uses <a class="reference external" href="htt
for speeding-up the training phase.</p>
<p>Example:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">quapy.method.meta</span> <span class="kn">import</span> <span class="n">QuaNet</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">quapy.method_name.meta</span> <span class="kn">import</span> <span class="n">QuaNet</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">quapy.classification.neural</span> <span class="kn">import</span> <span class="n">NeuralClassifierTrainer</span><span class="p">,</span> <span class="n">CNNnet</span>
<span class="go">&gt;&gt;&gt;</span>
<span class="gp">&gt;&gt;&gt; </span><span class="c1"># use samples of 100 elements</span>
@ -1902,7 +1908,7 @@ possible to update each component of a nested object.</p>
<span class="target" id="module-quapy.method._threshold_optim"></span><dl class="py class">
<dt class="sig sig-object py" id="quapy.method._threshold_optim.MAX">
<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">MAX</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><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_threshold_optim.html#MAX"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._threshold_optim.MAX" 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">MAX</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">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">BaseEstimator</span><span class="p"><span class="pre">]</span></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">None</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><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_threshold_optim.html#MAX"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._threshold_optim.MAX" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method._threshold_optim.ThresholdOptimization" title="quapy.method._threshold_optim.ThresholdOptimization"><code class="xref py py-class docutils literal notranslate"><span class="pre">ThresholdOptimization</span></code></a></p>
<p>Threshold Optimization variant 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
@ -1944,7 +1950,7 @@ 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.MS">
<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">MS</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><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_threshold_optim.html#MS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._threshold_optim.MS" 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">MS</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">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">BaseEstimator</span><span class="p"><span class="pre">]</span></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">None</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><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_threshold_optim.html#MS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._threshold_optim.MS" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method._threshold_optim.ThresholdOptimization" title="quapy.method._threshold_optim.ThresholdOptimization"><code class="xref py py-class docutils literal notranslate"><span class="pre">ThresholdOptimization</span></code></a></p>
<p>Median Sweep. Threshold Optimization variant 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
@ -2015,7 +2021,7 @@ 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.MS2">
<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">MS2</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><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_threshold_optim.html#MS2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._threshold_optim.MS2" 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">MS2</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">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">BaseEstimator</span><span class="p"><span class="pre">]</span></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">None</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><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_threshold_optim.html#MS2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._threshold_optim.MS2" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method._threshold_optim.MS" title="quapy.method._threshold_optim.MS"><code class="xref py py-class docutils literal notranslate"><span class="pre">MS</span></code></a></p>
<p>Median Sweep 2. Threshold Optimization variant 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
@ -2057,7 +2063,7 @@ validation data, or as an integer, indicating that the misclassification rates s
<dl class="py class">
<dt class="sig sig-object py" id="quapy.method._threshold_optim.T50">
<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">T50</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><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_threshold_optim.html#T50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._threshold_optim.T50" 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">T50</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">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">BaseEstimator</span><span class="p"><span class="pre">]</span></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">None</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><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_threshold_optim.html#T50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._threshold_optim.T50" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method._threshold_optim.ThresholdOptimization" title="quapy.method._threshold_optim.ThresholdOptimization"><code class="xref py py-class docutils literal notranslate"><span class="pre">ThresholdOptimization</span></code></a></p>
<p>Threshold Optimization variant 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
@ -2099,7 +2105,7 @@ 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">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>
<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">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">BaseEstimator</span><span class="p"><span class="pre">]</span></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">None</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
@ -2194,7 +2200,7 @@ 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.X">
<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">X</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><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_threshold_optim.html#X"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._threshold_optim.X" 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">X</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">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">BaseEstimator</span><span class="p"><span class="pre">]</span></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">None</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><span class="sig-paren">)</span><a class="reference internal" href="_modules/quapy/method/_threshold_optim.html#X"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#quapy.method._threshold_optim.X" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <a class="reference internal" href="#quapy.method._threshold_optim.ThresholdOptimization" title="quapy.method._threshold_optim.ThresholdOptimization"><code class="xref py py-class docutils literal notranslate"><span class="pre">ThresholdOptimization</span></code></a></p>
<p>Threshold Optimization variant 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

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