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<a href="index.html" class="icon icon-home"> SuMMIT
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<li class="toctree-l1"><a class="reference internal" href="autoapi/summit/multiview_platform/monoview_classifiers/index.html">Available monoview classifiers</a></li>
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<p><img alt="Pipeline status" src="https://gitlab.lis-lab.fr/baptiste.bauvin/summit/badges/master/pipeline.svg" /> <a class="reference external" href="http://www.gnu.org/licenses/gpl-3.0"><img alt="License: GPL v3" src="https://img.shields.io/badge/License-GPL%20v3-blue.svg" /></a> <a class="reference external" href="http://baptiste.bauvin.pages.lis-lab.fr/summit/coverage/index.html"><img alt="Coverage" src="https://gitlab.lis-lab.fr/baptiste.bauvin/summit/badges/master/coverage.svg" /></a></p>
<div class="section" id="supervised-multimodal-integration-tool-s-readme">
<h1>Supervised MultiModal Integration Tool’s Readme<a class="headerlink" href="#supervised-multimodal-integration-tool-s-readme" title="Permalink to this headline">¶</a></h1>
<p>This project aims to be an easy-to-use solution to run a prior benchmark on a dataset and evaluate mono- & multi-view algorithms capacity to classify it correctly.</p>
<div class="section" id="getting-started">
<h2>Getting Started<a class="headerlink" href="#getting-started" title="Permalink to this headline">¶</a></h2>
<p>SuMMIT has been designed and uses continuous integration for Linux platforms (ubuntu 18.04), but we try to keep it as compatible as possible with Mac and Windows.</p>
<table border="1" class="docutils">
<colgroup>
<col width="34%" />
<col width="66%" />
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">Platform</th>
<th class="head">Last positive test</th>
</tr>
</thead>
<tbody valign="top">
<tr class="row-even"><td>Linux</td>
<td><img alt="Pipeline status" src="https://gitlab.lis-lab.fr/baptiste.bauvin/summit/badges/master/pipeline.svg" /></td>
</tr>
<tr class="row-odd"><td>Mac</td>
<td>1st of May, 2020</td>
</tr>
<tr class="row-even"><td>Windows</td>
<td>1st of May, 2020</td>
</tr>
</tbody>
</table>
<div class="section" id="prerequisites">
<h3>Prerequisites<a class="headerlink" href="#prerequisites" title="Permalink to this headline">¶</a></h3>
<p>To be able to use this project, you’ll need :</p>
<ul class="simple">
<li><a class="reference external" href="https://docs.python.org/3/">Python 3</a></li>
</ul>
<p>And the following python modules will be automatically installed :</p>
<ul class="simple">
<li><a class="reference external" href="http://www.numpy.org/">numpy</a>, <a class="reference external" href="https://scipy.org/">scipy</a>,</li>
<li><a class="reference external" href="http://matplotlib.org/">matplotlib</a> - Used to plot results,</li>
<li><a class="reference external" href="http://scikit-learn.org/stable/">sklearn</a> - Used for the monoview classifiers,</li>
<li><a class="reference external" href="https://pypi.python.org/pypi/joblib">joblib</a> - Used to compute on multiple threads,</li>
<li><a class="reference external" href="https://www.h5py.org">h5py</a> - Used to generate HDF5 datasets on hard drive and use them to spare RAM,</li><li><a class="reference external" href="https://docs.python.org/3/library/pickle.html">pickle</a> - Used to store some results,</li>
<li><a class="reference external" href="https://pandas.pydata.org/">pandas</a> - Used to manipulate data efficiently,</li>
<li><a class="reference external" href="https://pypi.org/project/six/">six</a> -</li>
<li><a class="reference external" href="https://pypi.org/project/m2r/">m2r</a> - Used to generate documentation from the readme,</li>
<li><a class="reference external" href="https://pypi.org/project/docutils/">docutils</a> - Used to generate documentation,</li>
<li><a class="reference external" href="https://pypi.org/project/PyYAML/">pyyaml</a> - Used to read the config files,</li>
<li><a class="reference external" href="https://plot.ly/">plotly</a> - Used to generate interactive HTML visuals,</li>
<li><a class="reference external" href="https://pypi.org/project/tabulate/">tabulate</a> - Used to generated the confusion matrix.</li>
</ul>
</div>
<div class="section" id="installing">
<h3>Installing<a class="headerlink" href="#installing" title="Permalink to this headline">¶</a></h3>
<p>Once you cloned the project from the <a class="reference external" href="https://gitlab.lis-lab.fr/baptiste.bauvin/summit/">gitlab repository</a>, you just have to use :</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span> path/to/summit/
pip install -e .
</pre></div>
</div>
<p>In the <cite>summit</cite> directory to install SuMMIT and its dependencies.</p>
</div>
<div class="section" id="running-the-tests">
<h3>Running the tests<a class="headerlink" href="#running-the-tests" title="Permalink to this headline">¶</a></h3>
<p>To run the test suite of SuMMIT, run :</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span> path/to/summit
pip install -e .<span class="o">[</span>dev<span class="o">]</span>
pytest
</pre></div>
</div>
<p>The coverage report is automatically generated and stored in the <code class="docutils literal notranslate"><span class="pre">htmlcov/</span></code> directory</p>
</div>
<div class="section" id="building-the-documentation">
<h3>Building the documentation<a class="headerlink" href="#building-the-documentation" title="Permalink to this headline">¶</a></h3>
<p>To locally build the <a class="reference external" href="http://baptiste.bauvin.pages.lis-lab.fr/summit/index.html">documentation</a> run :</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span> path/to/summit
pip install -e .<span class="o">[</span>doc<span class="o">]</span>
python setup.py build_sphinx
</pre></div>
</div>
<p>The built html files will be stored in <code class="docutils literal notranslate"><span class="pre">path/to/summit/build/sphinx/html</span></code></p>
</div>
<div class="section" id="running-on-simulated-data">
<h3>Running on simulated data<a class="headerlink" href="#running-on-simulated-data" title="Permalink to this headline">¶</a></h3>
<p>For your first go with SuMMIT, you can run it on simulated data with</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python
>>> from summit.execute import execute
>>> execute<span class="o">(</span><span class="s2">"example 1"</span><span class="o">)</span>
</pre></div>
</div>
<p>This will run the benchmark of <a class="reference external" href="http://baptiste.bauvin.pages.lis-lab.fr/summit/tutorials/example1.html">documentation’s Example 1</a>.</p>
<p>For more information about the examples, see the <a class="reference external" href="http://baptiste.bauvin.pages.lis-lab.fr/summit/index.html">documentation</a>.
Results will, by default, be stored in the results directory of the installation path :
<code class="docutils literal notranslate"><span class="pre">path/to/summit/multiview_platform/examples/results</span></code>.</p>
<p>The documentation proposes a detailed interpretation of the results and arguments of SuMMIT through <a class="reference external" href="http://baptiste.bauvin.pages.lis-lab.fr/summit/">6 tutorials</a>.</p>
</div>
<div class="section" id="dataset-compatibility">
<h3>Dataset compatibility<a class="headerlink" href="#dataset-compatibility" title="Permalink to this headline">¶</a></h3>
<p>In order to start a benchmark on your own dataset, you need to format it so SuMMIT can use it. To do so, a <a class="reference external" href="https://gitlab.lis-lab.fr/baptiste.bauvin/summit/-/blob/master/format_dataset.py">python script</a> is provided.</p>
<p>For more information, see <a class="reference external" href="http://baptiste.bauvin.pages.lis-lab.fr/summit/tutorials/example5.html">Example 5</a></p>
<div class="section" id="running-on-your-dataset">
<h4>Running on your dataset<a class="headerlink" href="#running-on-your-dataset" title="Permalink to this headline">¶</a></h4>
<p>Once you have formatted your dataset, to run SuMMIT on it you need to modify the config file as</p>
<div class="highlight-yaml notranslate"><div class="highlight"><pre><span></span><span class="nt">name</span><span class="p">:</span> <span class="p p-Indicator">[</span><span class="s">"your_file_name"</span><span class="p p-Indicator">]</span>
<span class="nt">pathf</span><span class="p">:</span> <span class="s">"path/to/your/dataset"</span>
</pre></div>
</div>
<p>It is however highly recommended to follow the documentation’s <a class="reference external" href="http://baptiste.bauvin.pages.lis-lab.fr/summit/tutorials/index.html">tutorials</a> to learn the use of each parameter.</p>
</div>
</div>
</div>
<div class="section" id="authors">
<h2>Authors<a class="headerlink" href="#authors" title="Permalink to this headline">¶</a></h2><ul class="simple">
<li><strong>Baptiste BAUVIN</strong></li>
<li><strong>Dominique BENIELLI</strong></li>
<li><strong>Alexis PROD’HOMME</strong></li>
</ul>
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