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  <title> 5 The Phase Separation Binary Classifier: where to read more about it | Binary classification as a phase separation process - a short tutorial</title>
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<li><a href="./">Binary classification as a phase separation process</a></li>

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<li class="chapter" data-level="1" data-path="index.html"><a href="index.html"><i class="fa fa-check"></i><b>1</b> Introduction</a></li>
<li class="chapter" data-level="2" data-path="a-few-examples.html"><a href="a-few-examples.html"><i class="fa fa-check"></i><b>2</b> Nonlinear diffusion equations: a numerical example</a><ul>
<li class="chapter" data-level="2.1" data-path="a-few-examples.html"><a href="a-few-examples.html#propagation-with-randomly-generated-coefficients"><i class="fa fa-check"></i><b>2.1</b> Propagation with randomly generated coefficients</a></li>
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<li class="chapter" data-level="3" data-path="sec-PSBC.html"><a href="sec-PSBC.html"><i class="fa fa-check"></i><b>3</b> A glimpse at the PSBC model</a></li>
<li class="chapter" data-level="4" data-path="sec-mnist.html"><a href="sec-mnist.html"><i class="fa fa-check"></i><b>4</b> Applying the PSBC to pairs of digits of the MNIST database</a><ul>
<li class="chapter" data-level="4.1" data-path="sec-mnist.html"><a href="sec-mnist.html#multiclass-classification-using-the-psbc"><i class="fa fa-check"></i><b>4.1</b> Multiclass classification using the PSBC</a><ul>
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            <i class="fa fa-circle-o-notch fa-spin"></i><a href="./">Binary classification as a phase separation process - a short tutorial</a>
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<div id="the-phase-separation-binary-classifier-where-to-read-more-about-it" class="section level1">
<h1><span class="header-section-number"> 5</span> The Phase Separation Binary Classifier: where to read more about it</h1>
<p>There are quite a few places that you can read more about the Phase Separation Binary Classifier (PSBC), and also see further examples.</p>
<ul>
<li><p>I have posted a paper with all the mathematics behind the model. It is quite self contained. There is a preprint currently available on arXiv at <a href="https://arxiv.org/abs/2009.02467" class="uri">https://arxiv.org/abs/2009.02467</a>.</p></li>
<li><p>All the data is available at <span class="citation">(Monteiro <a href="#ref-Bin_phase_data" role="doc-biblioref">2020</a><a href="#ref-Bin_phase_data" role="doc-biblioref">b</a>)</span>.</p></li>
<li><p>There are many other examples of usage in the jupyter-notebooks:</p>
<ul>
<li><a href="https://github.com/rafael-a-monteiro-math/Binary_classification_phase_separation/blob/master/PSBC_Examples.ipynb">PSBC_Examples.ipynb</a> is a short tutorial that explains how to use the model in python.</li>
<li><a href="https://github.com/rafael-a-monteiro-math/Binary_classification_phase_separation/blob/master/PSBC_grid_search_notebook.ipynb">PSBC_grid_search_notebook.ipynb</a> shows you how to replicate the grid search in order to find good hyperaparamters.</li>
<li><a href="https://github.com/rafael-a-monteiro-math/Binary_classification_phase_separation/blob/master/PSBC_ensemble_learning_notebook.ipynb">PSBC_ensemble_learning_notebook.ipynb</a> explains how Ensemble learning was used in practice, besides showing how confusion matrices where created.</li>
<li><a href="https://github.com/rafael-a-monteiro-math/Binary_classification_phase_separation/blob/master/PSBC_training_notebook.ipynb">PSBC_training_notebook.ipynb</a> can be used to train the model once grid search has been carried out. Actually, if you have the output of grid search already in hands, you can run this nootebook without runing th grid search notebook.</li>
<li><a href="https://github.com/rafael-a-monteiro-math/Binary_classification_phase_separation/blob/master/PSBC_using_statistical_files.ipynb">PSBC_using_statistical_files.ipynb</a> shows how the data we anbalize in the paper ccan be retrieved from pickled files.</li>
</ul></li>
</ul>
<p>In order to keep things short I didn’t explain how callbacks are used in order to save validation data statistics. This was necessary because the keras/tensorflow API does not support this feature in “non-standard” models, like ours. If you want to see how this is done, please check both files
<a href="https://github.com/rafael-a-monteiro-math/Binary_classification_phase_separation/blob/master/MOTHER_PSBC/tf_PSBC_extra_libs_for_training_and_grid_search.py">tf_PSBC_extra_libs_for_training_and_grid_search.py</a> and <a href="https://github.com/rafael-a-monteiro-math/Binary_classification_phase_separation/blob/master/MOTHER_PSBC/tfversion_binary_phase_separation.py">tfversion_binary_phase_separation.py</a> at <span class="citation">(Monteiro <a href="#ref-Bin_phase_github" role="doc-biblioref">2020</a><a href="#ref-Bin_phase_github" role="doc-biblioref">a</a>)</span>.</p>
<ul>
<li>If you want to have access to the trainable examples I used, and to the computational statistics, you can either download the files <a href="https://zenodo.org/record/5525794/files/PSBC_BCs.tar.gz?download=1">PSBC_BCs.tar.gz, about 11Mn</a> for the tests regards different boundary conditions, and <a href="https://zenodo.org/record/5525794/files/PSBC_classifier_PCA.tar.gz?download=1">PSBC_classifier_PCA.tar.gz, about 2 Mb</a> at the companion <a href="https://doi.org/10.5281/zenodo.4005130"> data repository</a> to this project at Zenodo.<br />
</li>
<li>In this Github you will also find a manual named <a href="https://github.com/rafael-a-monteiro-math/Binary_classification_phase_separation/blob/master/README_v2.pdf">README_v2.pdf</a> to the data in the repository.</li>
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<h3>References</h3>
<div id="refs" class="references">
<div id="ref-Bin_phase_github">
<p>Monteiro, Rafael. 2020a. “Source Code for the Paper ‘Binary Classification as a Phase Separation Process’.” <em>GitHub Repository</em>. <a href="https://github.com/rafael-a-monteiro-math/Binary_classification_phase_separation">https://github.com/rafael-a-monteiro-math/Binary_classification_phase_separation</a>; GitHub.</p>
</div>
<div id="ref-Bin_phase_data">
<p>Monteiro, Rafael. 2020b. “Binary Classification as a Phase Separation Process (data repository).” Zenodo. <a href="https://doi.org/10.5281/zenodo.5525794">https://doi.org/10.5281/zenodo.5525794</a>.</p>
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