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&#169; Copyright 2019, Baptiste BAUVIN.
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<div class="bodywrapper">
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<a class="reference external image-reference" 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 image-reference" href="https://gitlab.lis-lab.fr/baptiste.bauvin/summit/badges/develop/pipeline.svg"><img alt="Build Status" src="https://gitlab.lis-lab.fr/baptiste.bauvin/summit/badges/develop/pipeline.svg" /></a>
<div class="section" id="supervised-multimodal-integration-tool-s-readme">
<h1>Supervised MultiModal Integration Tool&#8217;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- &amp; 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>
<div class="section" id="prerequisites-will-be-automatically-installed">
<h3>Prerequisites (will be automatically installed)<a class="headerlink" href="#prerequisites-will-be-automatically-installed" title="Permalink to this headline"></a></h3>
<p>To be able to use this project, you&#8217;ll need :</p>
<ul class="simple">
<li><a class="reference external" href="https://docs.python.org/3/">Python 3.6</a></li>
</ul>
<p>And the following python modules :</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-guess"><div class="highlight"><pre><span></span><span class="err">cd path/to/summit/</span>
<span class="err">pip install -e .</span>
</pre></div>
</div>
<p>In the <code class="docutils literal"><span class="pre">summit</span></code> directory to install SuMMIT and its dependencies.</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>In order to run it you&#8217;ll need to try on <strong>simulated</strong> data with the command</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">multiview_platform.execute</span> <span class="kn">import</span> <span class="n">execute</span>
<span class="n">execute</span><span class="p">(</span><span class="s2">&quot;example 1&quot;</span><span class="p">)</span>
</pre></div>
</div>
<p>This will run the first example.</p>
<p>For more information about the examples, see the <a class="reference external" href="http://baptiste.bauvin.pages.lis-lab.fr/summit/">documentation</a>.
Results will be stored in the results directory of the installation path :
<code class="docutils literal"><span class="pre">path/to/summit/multiview_platform/examples/results</span></code>.</p>
<p>The documentation proposes a detailed interpretation of the results through <a class="reference external" href="http://baptiste.bauvin.pages.lis-lab.fr/summit/">6 tutorials</a>.</p>
</div>
<div class="section" id="discovering-the-arguments">
<h3>Discovering the arguments<a class="headerlink" href="#discovering-the-arguments" title="Permalink to this headline"></a></h3>
<p>All the arguments of the platform are stored in a YAML config file. Some config files are given as examples.
The file stored in <code class="docutils literal"><span class="pre">summit/config_files/config.yml</span></code> is documented and it is highly recommended
to read it carefully before playing around with the parameters.</p>
<p>You can create your own configuration file. In order to run the platform with it, run :</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">multiview_platform.execute</span> <span class="kn">import</span> <span class="n">execute</span>
<span class="n">execute</span><span class="p">(</span><span class="n">config_path</span><span class="o">=</span><span class="s2">&quot;/absolute/path/to/your/config/file&quot;</span><span class="p">)</span>
</pre></div>
</div>
<p>For further information about classifier-specific arguments, see the <a class="reference external" href="http://baptiste.bauvin.pages.lis-lab.fr/summit/">documentation</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/example4.html">Example 6</a></p>
</div>
<div class="section" id="running-on-your-dataset">
<h3>Running on your dataset<a class="headerlink" href="#running-on-your-dataset" title="Permalink to this headline"></a></h3>
<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"><div class="highlight"><pre><span></span>name: [&quot;your_file_name&quot;]
*
pathf: &quot;path/to/your/dataset&quot;
</pre></div>
</div>
<p>This will run a full benchmark on your dataset using all available views and labels.</p>
<p>It is highly recommended to follow the documentation&#8217;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 class="section" id="author">
<h2>Author<a class="headerlink" href="#author" 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&#8217;HOMME</strong></li>
</ul>
</div>
</div>
<p>&lt;../../README.md&gt;</p>
</div>
......@@ -151,46 +52,29 @@ pathf: &quot;path/to/your/dataset&quot;
</div>
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<h3><a href="index.html">Table Of Contents</a></h3>
<ul>
<li><a class="reference internal" href="#">Supervised MultiModal Integration Tool&#8217;s Readme</a><ul>
<li><a class="reference internal" href="#getting-started">Getting Started</a><ul>
<li><a class="reference internal" href="#prerequisites-will-be-automatically-installed">Prerequisites (will be automatically installed)</a></li>
<li><a class="reference internal" href="#installing">Installing</a></li>
<li><a class="reference internal" href="#running-on-simulated-data">Running on simulated data</a></li>
<li><a class="reference internal" href="#discovering-the-arguments">Discovering the arguments</a></li>
<li><a class="reference internal" href="#dataset-compatibility">Dataset compatibility</a></li>
<li><a class="reference internal" href="#running-on-your-dataset">Running on your dataset</a></li>
</ul>
</li>
<li><a class="reference internal" href="#author">Author</a></li>
</ul>
</li>
</ul>
<h4>Previous topic</h4>
<p class="topless"><a href="index.html"
title="previous chapter">Welcome to Supervised MultiModal Integration Tool&#8217;s documentation</a></p>
title="previous chapter">Welcome to Supervised MultiModal Integration Tool’s documentation</a></p>
<h4>Next topic</h4>
<p class="topless"><a href="tutorials/index.html"
title="next chapter">SuMMIT Tutorials</a></p>
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......@@ -210,12 +94,12 @@ pathf: &quot;path/to/your/dataset&quot;
<li class="right" >
<a href="index.html" title="Welcome to Supervised MultiModal Integration Tool’s documentation"
>previous</a> |</li>
<li class="nav-item nav-item-0"><a href="index.html">MultiviewPlatform 0 documentation</a> &#187;</li>
<li class="nav-item nav-item-0"><a href="index.html">SuMMIT 0 documentation</a> &#187;</li>
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</div>
<div class="footer" role="contentinfo">
&#169; Copyright 2019, Baptiste BAUVIN.
Created using <a href="http://sphinx-doc.org/">Sphinx</a> 1.4.8.
Created using <a href="http://sphinx-doc.org/">Sphinx</a> 3.0.2.
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<li class="right" >
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......@@ -51,71 +41,65 @@
<p>In ths file, we explain how to add a metric to the platform.</p>
<p>In order to do that, on needs to add a file with the following functions
which are mandatory for the metric to work with the platform.</p>
<dl class="function">
<dl class="py function">
<dt id="multiview_platform.mono_multi_view_classifiers.metrics.framework.get_config">
<code class="descname">get_config</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.metrics.framework.get_config" title="Permalink to this definition"></a></dt>
<dd><p>Get the metric&#8217;s configuration as a string.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>kwargs</strong> (<em>dict</em>) &#8211; The arguments stored in this dictionary must be keyed by string of
integers as &#8220;0&#8221;, .., etc and decrypted in the function. These arguments
are a configuration of the metric.</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>configString</strong> &#8211; The string describing the metric&#8217;s configuration.</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body">string</td>
</tr>
</tbody>
</table>
<code class="sig-name descname">get_config</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.metrics.framework.get_config" title="Permalink to this definition"></a></dt>
<dd><p>Get the metric’s configuration as a string.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>kwargs</strong> (<em>dict</em>) – The arguments stored in this dictionary must be keyed by string of
integers as “0”, .., etc and decrypted in the function. These arguments
are a configuration of the metric.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><strong>configString</strong> – The string describing the metric’s configuration.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>string</p>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dl class="py function">
<dt id="multiview_platform.mono_multi_view_classifiers.metrics.framework.get_scorer">
<code class="descname">get_scorer</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.metrics.framework.get_scorer" title="Permalink to this definition"></a></dt>
<dd><p>Get the metric&#8217;s scorer as in the sklearn.metrics package.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>kwargs</strong> (<em>dict</em>) &#8211; The arguments stored in this dictionary must be keyed by string of
integers as &#8220;0&#8221;, .., etc and decrypted in the function. These arguments
are a configuration of the metric.</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>scorer</strong> &#8211; Callable object that returns a scalar score; greater is better. (cf sklearn.metrics.make_scorer)</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body">object</td>
</tr>
</tbody>
</table>
<code class="sig-name descname">get_scorer</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.metrics.framework.get_scorer" title="Permalink to this definition"></a></dt>
<dd><p>Get the metric’s scorer as in the sklearn.metrics package.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>kwargs</strong> (<em>dict</em>) – The arguments stored in this dictionary must be keyed by string of
integers as “0”, .., etc and decrypted in the function. These arguments
are a configuration of the metric.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><strong>scorer</strong> – Callable object that returns a scalar score; greater is better. (cf sklearn.metrics.make_scorer)</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>object</p>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dl class="py function">
<dt id="multiview_platform.mono_multi_view_classifiers.metrics.framework.score">
<code class="descname">score</code><span class="sig-paren">(</span><em>y_true</em>, <em>y_pred</em>, <em>multiclass=False</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.metrics.framework.score" title="Permalink to this definition"></a></dt>
<dd><p>Get the metric&#8217;s score from the ground truth (<code class="docutils literal"><span class="pre">y_true</span></code>) and predictions (<code class="docutils literal"><span class="pre">y_pred</span></code>).</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>y_true</strong> (<em>array-like, shape = (n_samples,)</em>) &#8211; Target values (class labels).</li>
<li><strong>y_pred</strong> (<em>array-like, shape = (n_samples,)</em>) &#8211; Predicted target values (class labels).</li>
<li><strong>multiclass</strong> (<em>boolean (default=False)</em>) &#8211; Parameter specifying whether the target values are multiclass or not.</li>
<li><strong>kwargs</strong> (<em>dict</em>) &#8211; The arguments stored in this dictionary must be keyed by string of
integers as &#8220;0&#8221;, .., etc and decrypted in the function</li>
<code class="sig-name descname">score</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">y_true</span></em>, <em class="sig-param"><span class="n">y_pred</span></em>, <em class="sig-param"><span class="n">multiclass</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.metrics.framework.score" title="Permalink to this definition"></a></dt>
<dd><p>Get the metric’s score from the ground truth (<code class="docutils literal notranslate"><span class="pre">y_true</span></code>) and predictions (<code class="docutils literal notranslate"><span class="pre">y_pred</span></code>).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>y_true</strong> (<em>array-like</em><em>, </em><em>shape =</em><em> (</em><em>n_samples</em><em>,</em><em>)</em>) – Target values (class labels).</p></li>
<li><p><strong>y_pred</strong> (<em>array-like</em><em>, </em><em>shape =</em><em> (</em><em>n_samples</em><em>,</em><em>)</em>) – Predicted target values (class labels).</p></li>
<li><p><strong>multiclass</strong> (<em>boolean</em><em> (</em><em>default=False</em><em>)</em>) – Parameter specifying whether the target values are multiclass or not.</p></li>
<li><p><strong>kwargs</strong> (<em>dict</em>) – The arguments stored in this dictionary must be keyed by string of
integers as “0”, .., etc and decrypted in the function</p></li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>score</strong> &#8211; Returns the score of the prediction.</p>
</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><p class="first last">float</p>
</td>
</tr>
</tbody>
</table>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><strong>score</strong> – Returns the score of the prediction.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>float</p>
</dd>
</dl>
</dd></dl>
</div>
......@@ -129,20 +113,20 @@ integers as &#8220;0&#8221;, .., etc and decrypted in the function</li>
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