From afb269fa294edd064e3f1758b33d817f55b7a9af Mon Sep 17 00:00:00 2001
From: Baptiste Bauvin <baptiste.bauvin@lis-lab.fr>
Date: Mon, 6 Apr 2020 17:21:26 -0400
Subject: [PATCH] Doc seem to be OK

---
 docs/build/.doctrees/environment.pickle       | Bin 210281 -> 209632 bytes
 docs/build/.doctrees/readme_link.doctree      | Bin 34399 -> 27946 bytes
 .../.doctrees/tutorials/example1.doctree      | Bin 402122 -> 402248 bytes
 docs/build/index.html                         |  10 +--
 docs/build/objects.inv                        | Bin 3717 -> 3721 bytes
 docs/build/readme_link.html                   |  48 +++----------
 docs/build/searchindex.js                     |   2 +-
 docs/build/tutorials/example1.html            |   8 ++-
 docs/build/tutorials/index.html               |   8 +--
 docs/source/conf.py                           |   4 ++
 docs/source/tutorials/example1.rst            |   2 +-
 docs/source/tutorials/example2.rst            |  68 +++++++++++-------
 docs/source/tutorials/example3.rst            |   8 +--
 docs/source/tutorials/example4.rst            |   2 +-
 .../images/example_1/folder_description.html  |   6 ++
 15 files changed, 86 insertions(+), 80 deletions(-)

diff --git a/docs/build/.doctrees/environment.pickle b/docs/build/.doctrees/environment.pickle
index 81d59798d6abefaca21321caa3d157dae8b1bfce..c8d14d6b810658976d1e863d33d0dd3857c954b0 100644
GIT binary patch
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diff --git a/docs/build/.doctrees/readme_link.doctree b/docs/build/.doctrees/readme_link.doctree
index d1506480256495f322bd5701f969869fe6624435..a58a03182607462323c1531e378980de95559100 100644
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diff --git a/docs/build/.doctrees/tutorials/example1.doctree b/docs/build/.doctrees/tutorials/example1.doctree
index 1a595c3c766728690e257293ca26919176ed58bd..9b08316162afd1d30755a3b5b338391c5346d137 100644
GIT binary patch
delta 178
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index 0d05ae4e..c4b7b816 100644
--- a/docs/build/index.html
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@@ -26,7 +26,7 @@
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
     <link rel="top" title="MultiviewPlatform 0 documentation" href="#" />
-    <link rel="next" title="Supervised MultiModal Integration Tool" href="readme_link.html" /> 
+    <link rel="next" title="Supervised MultiModal Integration Tool’s Readme" href="readme_link.html" /> 
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-          <a href="readme_link.html" title="Supervised MultiModal Integration Tool"
+          <a href="readme_link.html" title="Supervised MultiModal Integration Tool’s Readme"
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 </div>
 <div class="toctree-wrapper compound">
 <ul>
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+<li class="toctree-l1"><a class="reference internal" href="readme_link.html">Supervised MultiModal Integration Tool&#8217;s Readme</a></li>
 <li class="toctree-l1"><a class="reference internal" href="tutorials/index.html">SuMMIT Tutorials</a></li>
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   <h4>Next topic</h4>
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-                        title="next chapter">Supervised MultiModal Integration Tool</a></p>
+                        title="next chapter">Supervised MultiModal Integration Tool&#8217;s Readme</a></p>
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+          <a href="readme_link.html" title="Supervised MultiModal Integration Tool’s Readme"
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         <li class="nav-item nav-item-0"><a href="#">MultiviewPlatform 0 documentation</a> &#187;</li> 
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diff --git a/docs/build/readme_link.html b/docs/build/readme_link.html
index 98b0f499..b145d5fc 100644
--- a/docs/build/readme_link.html
+++ b/docs/build/readme_link.html
@@ -6,7 +6,7 @@
   <head>
     <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
     
-    <title>Supervised MultiModal Integration Tool &#8212; MultiviewPlatform 0 documentation</title>
+    <title>Supervised MultiModal Integration Tool’s Readme &#8212; MultiviewPlatform 0 documentation</title>
     
     <link rel="stylesheet" href="_static/classic.css" type="text/css" />
     <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
@@ -56,8 +56,8 @@
             
   <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">
-<h1>Supervised MultiModal Integration Tool<a class="headerlink" href="#supervised-multimodal-integration-tool" title="Permalink to this headline">¶</a></h1>
+<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>
@@ -100,10 +100,11 @@
 <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. For more information about the examples, see the <a class="reference external" href="http://baptiste.bauvin.pages.lis-lab.fr/summit/">documentation</a>.
+<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>.
-The documentation proposes a detailed interpretation of the results through 6 <a class="reference external" href="http://baptiste.bauvin.pages.lis-lab.fr/summit/">tutorials</a>.</p>
+<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>
@@ -119,32 +120,8 @@ to read it carefully before playing around with the parameters.</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.</p>
-<div class="section" id="if-you-already-have-an-hdf5-dataset-file-it-must-be-formatted-as">
-<h4>If you already have an HDF5 dataset file it must be formatted as :<a class="headerlink" href="#if-you-already-have-an-hdf5-dataset-file-it-must-be-formatted-as" title="Permalink to this headline">¶</a></h4>
-<ul class="simple">
-<li>One dataset for each view called <code class="docutils literal"><span class="pre">ViewI</span></code> with <code class="docutils literal"><span class="pre">I</span></code> being the view index with 2 attribures :<ul>
-<li><code class="docutils literal"><span class="pre">attrs[&quot;name&quot;]</span></code> a string for the name of the view</li>
-<li><code class="docutils literal"><span class="pre">attrs[&quot;sparse&quot;]</span></code> a boolean specifying whether the view is sparse or not (WIP)</li>
-</ul>
-</li>
-<li>One dataset for the labels called <code class="docutils literal"><span class="pre">Labels</span></code> with one attribute :<ul>
-<li><code class="docutils literal"><span class="pre">attrs[&quot;names&quot;]</span></code> a list of strings encoded in utf-8 naming the labels in the right order</li>
-</ul>
-</li>
-<li>One group for the additional data called <code class="docutils literal"><span class="pre">Metadata</span></code> containing at least 1 dataset :<ul>
-<li><code class="docutils literal"><span class="pre">&quot;example_ids&quot;</span></code>, a numpy array of type <code class="docutils literal"><span class="pre">S100</span></code>, with the ids of the examples in the right order</li>
-</ul>
-</li>
-<li>And three attributes :<ul>
-<li><code class="docutils literal"><span class="pre">attrs[&quot;nbView&quot;]</span></code> an int counting the total number of views in the dataset</li>
-<li><code class="docutils literal"><span class="pre">attrs[&quot;nbClass&quot;]</span></code> an int counting the total number of different labels in the dataset</li>
-<li><code class="docutils literal"><span class="pre">attrs[&quot;datasetLength&quot;]</span></code> an int counting the total number of examples in the dataset</li>
-</ul>
-</li>
-</ul>
-<p>The <code class="docutils literal"><span class="pre">format_dataset.py</span></code> file is documented and can be used to format a multiview dataset in a SuMMIT-compatible HDF5 file.</p>
-</div>
+<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>
@@ -181,16 +158,13 @@ pathf: &quot;path/to/your/dataset&quot;
         <div class="sphinxsidebarwrapper">
   <h3><a href="index.html">Table Of Contents</a></h3>
   <ul>
-<li><a class="reference internal" href="#">Supervised MultiModal Integration Tool</a><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><ul>
-<li><a class="reference internal" href="#if-you-already-have-an-hdf5-dataset-file-it-must-be-formatted-as">If you already have an HDF5 dataset file it must be formatted as :</a></li>
-</ul>
-</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>
diff --git a/docs/build/searchindex.js b/docs/build/searchindex.js
index 9e0ec50b..838b0968 100644
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package","multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fat_late_fusion package","multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fat_scm_late_fusion package","multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fusion package","multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fusion.Methods package","multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fusion.Methods.EarlyFusionPackage package","multiview_platform.mono_multi_view_classifiers.multiview_classifiers.fusion.Methods.LateFusionPackage package","multiview_platform.mono_multi_view_classifiers.multiview_classifiers.pseudo_cq_fusion package","multiview_platform.mono_multi_view_classifiers.utils package","multiview_platform.tests package","multiview_platform.tests.test_metrics package","multiview_platform.tests.test_mono_view package","multiview_platform.tests.test_monoview_classifiers package","multiview_platform.tests.test_multiview_classifiers package","multiview_platform.tests.test_multiview_classifiers.Test_DifficultyMeasure package","multiview_platform.tests.test_multiview_classifiers.Test_DisagreeFusion package","multiview_platform.tests.test_multiview_classifiers.Test_DoubleFaultFusion package","multiview_platform.tests.test_multiview_classifiers.Test_EntropyFusion package","multiview_platform.tests.test_multiview_classifiers.Test_Fusion package","multiview_platform.tests.test_multiview_classifiers.Test_PseudoCQMeasure package","multiview_platform.tests.test_utils package","Example 0 : Getting started with SuMMIT on digits","Example 1 : First big step with SuMMIT","Example 2 : Understanding the hyper-parameter optimization","Example 3 : Understanding the statistical iterations","Taking control : Use your own dataset","Taking control : Use your own algorithms","Hyper-parameter 101","SuMMIT Tutorials","Install SuMMIT"],titleterms:{"1560_12_25":41,"15_42":41,accuracy_scor:41,adding:[44,45],addit:44,algorithm:45,all:[41,45],alreadi:[],analysi:0,analyze_result:[16,17,18,19,20,21,22,26],argument:5,arrai:45,author:5,automat:5,bare:44,bayesianinfer:25,big:41,build:45,choic:46,classif:[6,12,40],classifi:[8,9,41,45],compat:5,complex:45,conclus:[40,42],config_fil:41,configur:27,content:[13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39],context:43,contributor:5,control:[44,45],convers:44,cross:46,csv:41,data:5,dataset:[5,27,41,44,45],depend:41,difficulty_fus:16,digit:40,disagree_fus:17,discov:5,discoveri:40,divers:9,document:[2,3],double_fault_fus:18,durat:43,earlyfus:23,earlyfusionpackag:24,entropy_fus:19,error_analysis_2d:41,error_analysis_bar:41,exampl:[40,41,42,43,44,45],exec_classif:14,exect:2,execut:[6,10,13,27],experi:42,fat_late_fus:20,fat_scm_late_fus:21,few:42,file:41,first:[40,41],fold:46,format:[],framework:7,fusion:[9,22,23,24,25],gener:41,get:[5,40,41],get_multiview_db:27,get_v:45,grid:[42,46],hand:42,have:[],hdf5:44,how:43,html:41,hyper:[42,46],hyper_parameter_search:27,impact:42,indice:3,inform:[40,44],instal:5,install:[5,48],integrat:[3,5],interpret:45,introduct:41,intuit:46,iter:43,latefus:23,latefusionpackag:25,launch:48,learn:42,list:45,log:41,main:40,majorityvot:25,make:45,make_file_config:27,manipul:45,method:[23,24,25],metric:[7,46],modul:[0,6,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39],mono:12,mono_multi_view_classifi:[14,15,16,17,18,19,20,21,22,23,24,25,26,27],monoview:45,more:[40,42,45],multiclass:[11,27],multimod:[3,5],multiview:[1,45],multiview_classifi:[15,16,17,18,19,20,21,22,23,24,25,26],multiview_platform:[4,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39],multiview_result_analysi:27,must:[],mutliview:12,necess:44,object:45,optim:[42,46],own:[44,45],packag:[14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39],paramet:[42,46],pickl:41,platform:1,png:41,prerequisit:5,pseudo_cq_fus:26,random:[42,46],random_st:41,readm:5,refer:13,report:42,result:[0,40,42],result_analysi:14,rule:41,run:[5,40],scmforlinear:25,score:40,search:[42,46],setup:48,simpl:45,simul:5,size:42,split:[42,46],start:[5,40,41],statist:43,step:41,structur:44,submodul:[13,14,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39],subpackag:[13,14,15,22,23,28,32,37],summit:[40,41,47,48],supervis:[3,5],svmforlinear:25,tabl:3,take:[44,45],task:45,test:[13,28,29,30,31,32,33,34,35,36,37,38,39,46],test_accuracy_scor:29,test_adaboost:31,test_compat:31,test_configur:39,test_difficultymeasur:33,test_difficultymeasuremodul:33,test_disagreefus:34,test_disagreefusionmodul:34,test_diversity_util:32,test_doublefaultfus:35,test_doublefaultfusionmodul:35,test_entropyfus:36,test_entropyfusionmodul:36,test_execclassif:28,test_execclassifmonoview:30,test_execut:39,test_fus:37,test_fusionmodul:37,test_getmultiviewdb:39,test_metr:29,test_mono_view:30,test_monoview_classifi:31,test_monoviewutil:30,test_multiclass:39,test_multiview_classifi:[32,33,34,35,36,37,38],test_pseudocqfusionmodul:38,test_pseudocqmeasur:38,test_resultanalysi:28,test_util:39,them:41,thi:41,tool:[3,5,48],train:46,transform:27,tutori:[41,47],understand:[42,43,46],usage:42,use:[44,45],util:[10,11,27],valid:46,version:13,view:45,weightedlinear:[24,25],welcom:[2,3],work:45,yml:41,you:[],your:[5,44,45]}})
\ No newline at end of file
diff --git a/docs/build/tutorials/example1.html b/docs/build/tutorials/example1.html
index 68756c61..9600ad11 100644
--- a/docs/build/tutorials/example1.html
+++ b/docs/build/tutorials/example1.html
@@ -209,7 +209,7 @@
 <div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">execute</span><span class="p">(</span><span class="s1">&#39;example 1&#39;</span><span class="p">)</span>
 </pre></div>
 </div>
-<p>The execution should take less than five minutes. We will first analyze the results and parse through the information the platform output.</p>
+<p>The execution should take less than one minute. We will first analyze the results and parse through the information the platform outputs.</p>
 <p><strong>Understanding the results</strong></p>
 <p>The result structure can be startling at first, but, as the platform provides a lot of information, it has to be organized.</p>
 <p>The results are stored in <a class="reference external" href="https://gitlab.lis-lab.fr/baptiste.bauvin/summit/-/tree/master/multiview_platform/examples/results/example_1/">a directory</a>. Here, you will find a directory with the name of the database used for the benchmark, here : <code class="docutils literal"><span class="pre">summit_doc/</span></code></p>
@@ -228,6 +228,10 @@
   padding: 0;
 }
 
+#allUls {
+
+}
+
 .caret {
   cursor: pointer;
   -webkit-user-select: none; /* Safari 3.1+ */
@@ -272,10 +276,12 @@
 
 .nested {
   display: none;
+  list-style-type: none;
 }
 
 .active {
   display: block;
+  list-style-type: none;
 }
 
 .plif {
diff --git a/docs/build/tutorials/index.html b/docs/build/tutorials/index.html
index 0b33cefb..ef22adc5 100644
--- a/docs/build/tutorials/index.html
+++ b/docs/build/tutorials/index.html
@@ -27,7 +27,7 @@
     <link rel="search" title="Search" href="../search.html" />
     <link rel="top" title="MultiviewPlatform 0 documentation" href="../index.html" />
     <link rel="next" title="Install SuMMIT" href="installation.html" />
-    <link rel="prev" title="Readme" href="../readme_link.html" /> 
+    <link rel="prev" title="Supervised MultiModal Integration Tool’s Readme" href="../readme_link.html" /> 
   </head>
   <body role="document">
     <div class="related" role="navigation" aria-label="related navigation">
@@ -43,7 +43,7 @@
           <a href="installation.html" title="Install SuMMIT"
              accesskey="N">next</a> |</li>
         <li class="right" >
-          <a href="../readme_link.html" title="Readme"
+          <a href="../readme_link.html" title="Supervised MultiModal Integration Tool’s Readme"
              accesskey="P">previous</a> |</li>
         <li class="nav-item nav-item-0"><a href="../index.html">MultiviewPlatform 0 documentation</a> &#187;</li> 
       </ul>
@@ -78,7 +78,7 @@
         <div class="sphinxsidebarwrapper">
   <h4>Previous topic</h4>
   <p class="topless"><a href="../readme_link.html"
-                        title="previous chapter">Readme</a></p>
+                        title="previous chapter">Supervised MultiModal Integration Tool&#8217;s Readme</a></p>
   <h4>Next topic</h4>
   <p class="topless"><a href="installation.html"
                         title="next chapter">Install SuMMIT</a></p>
@@ -116,7 +116,7 @@
           <a href="installation.html" title="Install SuMMIT"
              >next</a> |</li>
         <li class="right" >
-          <a href="../readme_link.html" title="Readme"
+          <a href="../readme_link.html" title="Supervised MultiModal Integration Tool’s Readme"
              >previous</a> |</li>
         <li class="nav-item nav-item-0"><a href="../index.html">MultiviewPlatform 0 documentation</a> &#187;</li> 
       </ul>
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 24510aa3..5119cc7f 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -192,6 +192,10 @@ rst_prolog = """
     :language: yaml
     
 .. |platf| replace:: SuMMIT
+
+.. |HP| replace:: hyper-parameter
+
+.. |HPO| replace hyper-parameters optimization
 """
 
 
diff --git a/docs/source/tutorials/example1.rst b/docs/source/tutorials/example1.rst
index ddef5b50..c8b21aec 100644
--- a/docs/source/tutorials/example1.rst
+++ b/docs/source/tutorials/example1.rst
@@ -95,7 +95,7 @@ During the whole benchmark, the log file will be printed in the terminal. To sta
 
    >>> execute('example 1')
 
-The execution should take less than five minutes. We will first analyze the results and parse through the information the platform output.
+The execution should take less than one minute. We will first analyze the results and parse through the information the platform outputs.
 
 
 **Understanding the results**
diff --git a/docs/source/tutorials/example2.rst b/docs/source/tutorials/example2.rst
index e3c25ad7..11d3d7cc 100644
--- a/docs/source/tutorials/example2.rst
+++ b/docs/source/tutorials/example2.rst
@@ -14,7 +14,7 @@ In order to understand the process and it's usefulness, let's run some configura
 
 This example will focus only on some lines of the configuration file :
 
-- :yaml:`split:`, controlling the ration of size between the testing set and the training set,
+- :yaml:`split:`, controlling the ratio between the testing set and the training set,
 - :yaml:`hps_type:`, controlling the type of hyper-parameter search,
 - :yaml:`hps_args:`, controlling the parameters of the hyper-parameters search method,
 - :yaml:`nb_folds:`, controlling the number of folds in the cross-validation process.
@@ -23,19 +23,19 @@ Example 2.1 : No hyper-parameter optimization, impact of split size
 <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
 
 
-For this example, we only used a subset of the available classifiers in |platf|, to reduce the computation time and the complexity of the results.
+For this example, we only used a subset of the available classifiers in |platf|, to reduce the computation time and the complexity of the results. Here, we will learn how to define the train/test ratio and its impact on the benchmark.
 
-Each classifier will first be learned on the default hyper-parameters (as in :base_doc:`Example 1 <tutorials/example1.html>`)
+The monoview classifiers used are `Adaboost <https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html#sklearn.ensemble.AdaBoostClassifier>`_ and a `decision tree <https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html>`_,
+and the multivew classifier is a late fusion majority vote.
 
-The monoview classifiers that will be used are `Adaboost <https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html#sklearn.ensemble.AdaBoostClassifier>`_ and a `decision tree <https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html>`_,
-and the multivew classifier is a late fusion majority vote. In order to use only a subset of the available classifiers,
-three lines in the configuration file are useful :
+In order to use only a subset of the available classifiers, three lines in the configuration file are useful :
 
 - :yaml:`type:` (:base_source:`l45 <multiview_platform/examples/config_files/config_example_2_1_1.yml#L45>`) in which one has to specify which type of algorithms are needed, here we used  ``type: ["monoview","multiview"]``,
 - :yaml:`algos_monoview:` (:base_source:`l47 <multiview_platform/examples/config_files/config_example_2_1_1.yml#L45>`) in which one specifies the names of the monoview algorithms to run, here we used : :yaml:`algos_monoview: ["decision_tree", "adaboost", ]`
 - :yaml:`algos_multiview:` (:base_source:`l49 <multiview_platform/examples/config_files/config_example_2_1_1.yml#L45>`) is the same but with multiview algorithms, here we used : :yaml:`algos_multiview: ["majority_voting_fusion", ]`
 
-In order for the platform to understand the names, the user has to give the **name of the python module** in which the classifier is implemented in the platform.
+.. note::
+    For the platform to understand the names, the user has to give the **name of the python module** in which the classifier is implemented in the platform.
 
 In the config file, the default values for Adaboost's hyper-parameters are :
 
@@ -75,7 +75,12 @@ And for the late fusion majority vote :
 Learning on a few examples
 >>>>>>>>>>>>>>>>>>>>>>>>>>
 
-To run this example run,
+This example focuses on one line of the config file :
+
+* :yaml:`split: 0.80`(:base_source:`l37 <multiview_platform/examples/config_files/config_example_2_1_1.yml#L37>`).
+
+
+To run the first part of this example, run :
 
 .. code-block:: python
 
@@ -109,7 +114,7 @@ You should obtain these scores in ``multiview_platform/examples/results/example_
     :file: ./images/example_2/2_1/high_train_accs.html
 
 
-Here we learned on 80% of the dataset and tested on 20%, so the line in the  :base_source:`config file <multiview_platform/examples/config_files/config_example_2_1_2.yml#L37>` has become ``split: 0.2``.
+Here we learned on 80% of the dataset and tested on 20%, so the line in the  :base_source:`config file <multiview_platform/examples/config_files/config_example_2_1_2.yml#L37>` has become :yaml:`split: 0.2`.
 
 The difference between these two examples is noticeable as even if, while learning on more examples, the performance of the decision trees and the late fusion improved, the performance of Adaboost did not improve as it was already over-fitting on the small train set.
 
@@ -120,7 +125,7 @@ Conclusion
 The split ratio has two consequences :
 
 - Increasing the test set size decreases the information available in the train set size so either it helps to avoid overfitting (Adaboost) or it can hide useful information to the classifier and therefor decrease its performance (decision tree),
-- The second consequence is that increasing train size will increase the benchmark duration as the classifier will have to learn  on more examples, this duration modification is higher if the dataset has high dimensionality and if the algorithm is algorithmically complex.
+- The second consequence is that increasing train size will increase the benchmark duration as the classifiers will have to learn  on more examples, this duration modification is higher if the dataset has high dimensionality and if the algorithms are complex.
 
 .. _random:
 Example 2.2 : Usage of randomized hyper-parameter optimization :
@@ -133,7 +138,9 @@ This is only useful if one knows the optimal combination of hyper-parameter for
 
 However, most of the time, they are unknown to the user, and then have to be optimized by the platform.
 
-In this example, we will use an randomized search, one of the two hyper-parameter optimization methods implemented in |platf|. To do so we will go through five lines of the config file :
+In this example, we will use a randomized search, one of the two hyper-parameter optimization methods implemented in |platf|.
+
+To do so we will go through five lines of the config file :
 
 - :yaml:`hps_type:`, controlling the type of hyper-parameter search,
 - :yaml:`n_iter:`, controlling the number of random draws during the hyper-parameter search,
@@ -187,7 +194,7 @@ The computing time of this run should be longer than the previous examples (appr
         learn on the whole training set
 
 The instructions inside the brackets are the one that the hyper-parameter
-optimization (HPO) adds.
+optimization adds.
 
 .. note::
 
@@ -198,7 +205,7 @@ optimization (HPO) adds.
 The results
 >>>>>>>>>>>
 
-Here, we used :yaml:`split: 0.8` and the results are far better than :base_doc:`earlier <tutorials/example2.html#learning-on-more-examples>`, as the classifiers are able to fit the task (the multiview classifier improved its accuracy from 0.46 in example 2.1.1 to 0.59).
+Here, we used :yaml:`split: 0.8` and the results are far better than :base_doc:`earlier <tutorials/example2.html#learning-on-more-examples>`, as the classifiers are able to fit the task (the multiview classifier improved its accuracy from 0.46 in Example 2.1.1 to 0.59).
 
 
 .. raw:: html
@@ -210,16 +217,16 @@ The choice made here is to allow a different amount of draws for mono and multiv
 
 .. note::
 
-    The mutliview algorithm used here is late fusion, which means it learns a monoview classifier on each view and then build a naive majority vote. In terms of hyper parameters, the late fusion classifier has to choose one monoview classifier and its HP **for each view**. This is why the :yaml:`equivalent_draws:` parameter is implemented, as with only 5 draws, the late fusion classifier is not able to remotely cover its hyper-parameter space, while the monoview algorithms have a much smaller problem to solve.
+    The mutliview algorithm used here is late fusion, which means it learns a monoview classifier on each view and then build a naive majority vote. In terms of hyper parameters, the late fusion classifier has to choose one monoview classifier and its HP **for each view**. This is why the :yaml:`equivalent_draws:` parameter is implemented, as with only 5 draws, the late fusion classifier is not able to remotely cover its hyper-parameter space, while the monoview algorithms have a much easier problem to solve.
 
 Conclusion
 >>>>>>>>>>
 
-Even if it adds a lot of computing, for most of the tasks, using the HPO is a necessity to be able to get the most of each classifier in terms of performance.
+Even if it adds a lot of computing, for most of the tasks, using the |HPO| is a necessity to be able to get the most of each classifier in terms of performance.
 
-The HPO is a matter of trade-off between classifier performance and computational demand.
+The |HPO| is a matter of trade-off between classifier performance and computational demand.
 For most algorithms the more draws you allow, the closer to ideal the outputted
-hyper-parameter (HP) set one will be, however, many draws mean much longer computational time.
+hyper-parameter set one will be, however, many draws mean much longer computational time.
 
 Similarly, the number of folds has a great importance in estimating the
 performance of a specific HP set, but more folds take also more time, as one has to train more times and on bigger parts of the dataset.
@@ -234,20 +241,20 @@ with different fold/draws settings :
 
 .. note::
 
-    The durations are for reference only as they highly depend on the hardware.
+    The durations are for reference only as they highly depend on hardware.
 
 
 
 
 
-Example 2.3 : Usage of grid search :
+Example 2.3 : Usage of grid search :v
 <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
 
 In |platf|, it is possible to use a grid search if one has several possible
 hyper-parameter values in mind to test.
 
-In order to set up the grid search one has to provide in the :yaml:`hps_args:`
-argument the names, parameters and values to test. If one wants to try
+In order to set up the grid search we have to provide the
+arguments names and values to test in in the :yaml:`hps_args:` argument. If we want to try
 several depths for a decision tree, and several :yaml:`n_estimators` values for Adaboost,
 
 .. code-block:: yaml
@@ -280,6 +287,15 @@ This will try to run the late fusion classifier with either
 - one decision tree per view, with a maximum depth of 3,
 - one Adaboost per view with 10 base estimators.
 
+To run a grid search with this configuration, run :
+
+.. code-block:: python
+
+   >>> from multiview_platform.execute import execute
+   >>> execute("example 2.3")
+
+It will use :base_source:`this config file <multiview_platform/examples/config_files/config_example_2_3.yml>`.
+
 
 Hyper-parameter report
 <<<<<<<<<<<<<<<<<<<<<<
@@ -308,8 +324,8 @@ So to run a decision tree with these exact parameters, one just has to follow th
 .. code-block:: yaml
 
     hps_type: "None"
-    hps_args:
-        decision_tree:
-            criterion: gini
-            max_depth: 202
-            splitter: random
\ No newline at end of file
+    hps_args: {}
+    decision_tree:
+        criterion: gini
+        max_depth: 202
+        splitter: random
\ No newline at end of file
diff --git a/docs/source/tutorials/example3.rst b/docs/source/tutorials/example3.rst
index ec1154fb..0f5cc416 100644
--- a/docs/source/tutorials/example3.rst
+++ b/docs/source/tutorials/example3.rst
@@ -5,7 +5,7 @@ Example 3 : Understanding the statistical iterations
 Context
 -------
 
-In the previous example, we have seen that in order to output meaningful results, the platform splits the input dataset in a training set and a testing set.
+In the previous example, we have seen that in order to output meaningful results, the platform splits the input dataset in a training and a testing set.
 
 However, even if the split is done at random, one can draw a lucky (or unlucky) split and have great (or poor) performance on this specific split.
 
@@ -15,7 +15,7 @@ To settle this issue, the platform can run on multiple splits and return the mea
 How to use it
 -------------
 
-This feature is controlled by a single argument : ``stats_iter:`` in the config file.
+This feature is controlled by a single argument : :yaml:`stats_iter:` in the config file.
 Modifying this argument and setting more than one ``stats_iter`` will slightly modify the result directory's structure.
 Indeed, as the platform will perform a benchmark on multiple train/test split, the result directory will be larger in order to keep all the individual results.
 
@@ -70,7 +70,7 @@ Similarly for the f1-score :
 
 The main difference between this plot an the one from :base_doc:`Example 1 <tutorials/example1.html>` is that here, the scores are means over all the statistical iterations, and the standard deviations are plotted as vertical lines on top of the bars and printed after each score under the bars as "± <std>".
 
-This has also an impact on the error analysis of :base_doc:`Example 1 <tutorials/example1.html>`. Indeed, now it has multiple shades of gray depending on the number of iterations that succeeded or failed on the example :
+This has also an impact on the display of error analysis. Indeed, now it has multiple shades of gray depending on the number of iterations that succeeded or failed on the example :
 
 .. raw:: html
     :file: ./images/example_3/err.html
@@ -89,6 +89,6 @@ Increasing the number of statistical iterations can be costly in terms of comput
 
 .. note::
 
-    Parallelizing |platf|'s statistical iteration can improve its efficiency when using multiple iterations, it is currently work in progress
+    Parallelizing |platf|'s statistical iterations can improve its efficiency when using multiple iterations, it is currently work in progress.
 
 
diff --git a/docs/source/tutorials/example4.rst b/docs/source/tutorials/example4.rst
index e93ff236..077d6d79 100644
--- a/docs/source/tutorials/example4.rst
+++ b/docs/source/tutorials/example4.rst
@@ -10,7 +10,7 @@ The bare necessities
 
 At the moment, in order for the platform to work, the dataset must satisfy the following minimum requirements :
 
-- Each example must be described in each view, with no missing data (you can use external tools to fill the gaps, or use only the fully-described examples of you dataset)
+- Each example must be described in each view, with no missing data (you can use external tools to fill the gaps, or use only the fully-described examples of your dataset)
 
 The dataset structure
 ---------------------
diff --git a/docs/source/tutorials/images/example_1/folder_description.html b/docs/source/tutorials/images/example_1/folder_description.html
index 3256e27e..fc37f76c 100644
--- a/docs/source/tutorials/images/example_1/folder_description.html
+++ b/docs/source/tutorials/images/example_1/folder_description.html
@@ -11,6 +11,10 @@
   padding: 0;
 }
 
+#allUls {
+
+}
+
 .caret {
   cursor: pointer;
   -webkit-user-select: none; /* Safari 3.1+ */
@@ -55,10 +59,12 @@
 
 .nested {
   display: none;
+  list-style-type: none;
 }
 
 .active {
   display: block;
+  list-style-type: none;
 }
 
 .plif {
-- 
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