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<div class="bodywrapper"> <div class="bodywrapper">
<div class="body" role="main"> <div class="body" role="main">
<div class="section" id="module-multiview_platform.mono_multi_view_classifiers.result_analysis"> <div class="section" id="module-summit.multiview_platform.result_analysis">
<span id="result-analysis-module"></span><h1>Result analysis module<a class="headerlink" href="#module-multiview_platform.mono_multi_view_classifiers.result_analysis" title="Permalink to this headline"></a></h1> <span id="result-analysis-module"></span><h1>Result analysis module<a class="headerlink" href="#module-summit.multiview_platform.result_analysis" title="Permalink to this headline"></a></h1>
</div> </div>
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<title>Multiview Platform &#8212; SuMMIT 0 documentation</title> <title>Multiview Platform &#8212; SuMMIT 0 documentation</title>
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<title>Welcome to the exection documentation &#8212; SuMMIT 0 documentation</title> <title>Welcome to the exection documentation &#8212; SuMMIT 0 documentation</title>
<link rel="stylesheet" href="_static/classic.css" type="text/css" /> <link rel="stylesheet" href="_static/classic.css" type="text/css" />
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<div class="bodywrapper"> <div class="bodywrapper">
<div class="body" role="main"> <div class="body" role="main">
<div class="section" id="module-multiview_platform.execute"> <div class="section" id="module-summit.execute">
<span id="welcome-to-the-exection-documentation"></span><h1>Welcome to the exection documentation<a class="headerlink" href="#module-multiview_platform.execute" title="Permalink to this headline"></a></h1> <span id="welcome-to-the-exection-documentation"></span><h1>Welcome to the exection documentation<a class="headerlink" href="#module-summit.execute" title="Permalink to this headline"></a></h1>
<p>This is the execution module, used to execute the code</p> <p>This is the execution module, used to execute the code</p>
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<title>Welcome to Supervised MultiModal Integration Tool’s documentation &#8212; SuMMIT 0 documentation</title> <title>Welcome to Supervised MultiModal Integration Tool’s documentation &#8212; SuMMIT 0 documentation</title>
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<p>The main advantage of the platform is that it allows to add and remove a classifier without modifying its core code (the procedure is described thoroughly in this documentation).</p> <p>The main advantage of the platform is that it allows to add and remove a classifier without modifying its core code (the procedure is described thoroughly in this documentation).</p>
<p>This documentation consists in a short read me, with instructions to install and get started with SuMMIT, then several use cases to discover the features, and all the documented sources.</p> <p>This documentation consists in a short read me, with instructions to install and get started with SuMMIT, then several use cases to discover the features, and all the documented sources.</p>
<div class="admonition note"> <div class="admonition note">
<p class="admonition-title">Note</p> <p class="first admonition-title">Note</p>
<p>The documentation, the platform and the tests are constantly being updated. <p class="last">The documentation, the platform and the tests are constantly being updated.
All the content labelled WIP is Work In Progress</p> All the content labelled WIP is Work In Progress</p>
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<div class="section" id="indices-and-tables"> <div class="section" id="indices-and-tables">
<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this headline"></a></h1> <h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this headline"></a></h1>
<ul class="simple"> <ul class="simple">
<li><p><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></p></li> <li><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></li>
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<h3>Navigation</h3> <h3>Navigation</h3>
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<div class="section" id="module-multiview_platform.mono_multi_view_classifiers.exec_classif"> <div class="section" id="classification-execution-module">
<span id="classification-execution-module"></span><h1>Classification execution module<a class="headerlink" href="#module-multiview_platform.mono_multi_view_classifiers.exec_classif" title="Permalink to this headline"></a></h1> <h1>Classification execution module<a class="headerlink" href="#classification-execution-module" title="Permalink to this headline"></a></h1>
<dl class="py function">
<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.arange_metrics">
<code class="sig-name descname">arange_metrics</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">metrics</span></em>, <em class="sig-param"><span class="n">metric_princ</span></em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.arange_metrics" title="Permalink to this definition"></a></dt>
<dd><p>Used to get the metrics list in the right order so that
the first one is the principal metric specified in args</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>metrics</strong> (<em>dict</em>) – The metrics that will be used in the benchmark</p></li>
<li><p><strong>metric_princ</strong> (<em>str</em>) – The name of the metric that need to be used for the hyper-parameter
optimization process</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><strong>metrics</strong> – The metrics list, but arranged so the first one is the principal one.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>list of lists</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.benchmark_init">
<code class="sig-name descname">benchmark_init</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">directory</span></em>, <em class="sig-param"><span class="n">classification_indices</span></em>, <em class="sig-param"><span class="n">labels</span></em>, <em class="sig-param"><span class="n">labels_dictionary</span></em>, <em class="sig-param"><span class="n">k_folds</span></em>, <em class="sig-param"><span class="n">dataset_var</span></em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.benchmark_init" title="Permalink to this definition"></a></dt>
<dd><p>Initializes the benchmark, by saving the indices of the train
examples and the cross validation folds.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>directory</strong> (<em>str</em>) – The benchmark’s result directory</p></li>
<li><p><strong>classification_indices</strong> (<em>numpy array</em>) – The indices of the examples, splitted for the train/test split</p></li>
<li><p><strong>labels</strong> (<em>numpy array</em>) – The labels of the dataset</p></li>
<li><p><strong>labels_dictionary</strong> (<em>dict</em>) – The dictionary with labels as keys and their names as values</p></li>
<li><p><strong>k_folds</strong> (<em>sklearn.model_selection.Folds object</em>) – The folds for the cross validation process</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.exec_benchmark">
<code class="sig-name descname">exec_benchmark</code><span class="sig-paren">(</span><em class="sig-param">nb_cores</em>, <em class="sig-param">stats_iter</em>, <em class="sig-param">benchmark_arguments_dictionaries</em>, <em class="sig-param">directory</em>, <em class="sig-param">metrics</em>, <em class="sig-param">dataset_var</em>, <em class="sig-param">track_tracebacks</em>, <em class="sig-param">exec_one_benchmark_mono_core=&lt;function exec_one_benchmark_mono_core&gt;</em>, <em class="sig-param">analyze=&lt;function analyze&gt;</em>, <em class="sig-param">delete=&lt;function delete_HDF5&gt;</em>, <em class="sig-param">analyze_iterations=&lt;function analyze_iterations&gt;</em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.exec_benchmark" title="Permalink to this definition"></a></dt>
<dd><p>Used to execute the needed benchmark(s) on multicore or mono-core functions.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>nb_cores</strong> (<em>int</em>) – Number of threads that the benchmarks can use.</p></li>
<li><p><strong>stats_iter</strong> (<em>int</em>) – Number of statistical iterations that have to be done.</p></li>
<li><p><strong>benchmark_arguments_dictionaries</strong> (<em>list of dictionaries</em>) – All the needed arguments for the benchmarks.</p></li>
<li><p><strong>classification_indices</strong> (<em>list of lists of numpy.ndarray</em>) – For each statistical iteration a couple of numpy.ndarrays is stored with the indices for the training set and
the ones of the testing set.</p></li>
<li><p><strong>directories</strong> (<em>list of strings</em>) – List of the paths to the result directories for each statistical iteration.</p></li>
<li><p><strong>directory</strong> (<em>string</em>) – Path to the main results directory.</p></li>
<li><p><strong>multi_class_labels</strong> (<em>ist of lists of numpy.ndarray</em>) – For each label couple, for each statistical iteration a triplet of numpy.ndarrays is stored with the
indices for the biclass training set, the ones for the biclass testing set and the ones for the
multiclass testing set.</p></li>
<li><p><strong>metrics</strong> (<em>list of lists</em>) – metrics that will be used to evaluate the algorithms performance.</p></li>
<li><p><strong>labels_dictionary</strong> (<em>dictionary</em>) – Dictionary mapping labels indices to labels names.</p></li>
<li><p><strong>nb_labels</strong> (<em>int</em>) – Total number of different labels in the dataset.</p></li>
<li><p><strong>dataset_var</strong> (<em>HDF5 dataset file</em>) – The full dataset that wil be used by the benchmark.</p></li>
<li><p><strong>classifiers_names</strong> (<em>list of strings</em>) – List of the benchmarks’s monoview classifiers names.</p></li>
<li><p><strong>rest_of_the_args</strong> – Just used for testing purposes</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><strong>results</strong> – The results of the benchmark.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>list of lists</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.exec_classif">
<code class="sig-name descname">exec_classif</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">arguments</span></em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.exec_classif" title="Permalink to this definition"></a></dt>
<dd><p>Runs the benchmark with the given arguments</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>arguments</strong> – </p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><ul class="simple">
<li><p><em>&gt;&gt;&gt; exec_classif([–config_path, /path/to/config/files/])</em></p></li>
<li><p><em>&gt;&gt;&gt;</em></p></li>
</ul>
</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.extract_dict">
<code class="sig-name descname">extract_dict</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">classifier_config</span></em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.extract_dict" title="Permalink to this definition"></a></dt>
<dd><p>Reverse function of get_path_dict</p>
</dd></dl>
<dl class="py function">
<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.get_path_dict">
<code class="sig-name descname">get_path_dict</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">multiview_classifier_args</span></em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.get_path_dict" title="Permalink to this definition"></a></dt>
<dd><p>This function is used to generate a dictionary with each key being
the path to the value.
If given {“key1”:{“key1_1”:value1}, “key2”:value2}, it will return
{“key1.key1_1”:value1, “key2”:value2}</p>
</dd></dl>
<dl class="py function">
<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.init_benchmark">
<code class="sig-name descname">init_benchmark</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">cl_type</span></em>, <em class="sig-param"><span class="n">monoview_algos</span></em>, <em class="sig-param"><span class="n">multiview_algos</span></em>, <em class="sig-param"><span class="n">args</span></em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.init_benchmark" title="Permalink to this definition"></a></dt>
<dd><p>Used to create a list of all the algorithm packages names used for the benchmark.</p>
<p>First this function will check if the benchmark need mono- or/and multiview
algorithms and adds to the right
dictionary the asked algorithms. If none is asked by the user, all will be added.</p>
<p>If the keyword <cite>“Benchmark”</cite> is used, all mono- and multiview algorithms will be added.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>cl_type</strong> (<em>List of string</em>) – List of types of needed benchmark</p></li>
<li><p><strong>multiview_algos</strong> (<em>List of strings</em>) – List of multiview algorithms needed for the benchmark</p></li>
<li><p><strong>monoview_algos</strong> (<em>Listof strings</em>) – List of monoview algorithms needed for the benchmark</p></li>
<li><p><strong>args</strong> (<em>ParsedArgumentParser args</em>) – All the input args (used to tune the algorithms)</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><strong>benchmark</strong> – Dictionary resuming which mono- and multiview algorithms which will be used in the benchmark.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>Dictionary of dictionaries</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.init_kwargs">
<code class="sig-name descname">init_kwargs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">args</span></em>, <em class="sig-param"><span class="n">classifiers_names</span></em>, <em class="sig-param"><span class="n">framework</span><span class="o">=</span><span class="default_value">'monoview'</span></em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.init_kwargs" title="Permalink to this definition"></a></dt>
<dd><p>Used to init kwargs thanks to a function in each monoview classifier package.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>args</strong> (<em>parsed args objects</em>) – All the args passed by the user.</p></li>
<li><p><strong>classifiers_names</strong> (<em>list of strings</em>) – List of the benchmarks’s monoview classifiers names.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><p><strong>kwargs</strong> – Dictionary resuming all the specific arguments for the benchmark, one dictionary for each classifier.</p>
<p>For example, for Adaboost, the KWARGS will be <cite>{“n_estimators”:&lt;value&gt;, “base_estimator”:&lt;value&gt;}</cite></p>
</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>Dictionary</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.init_kwargs_func">
<code class="sig-name descname">init_kwargs_func</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">args</span></em>, <em class="sig-param"><span class="n">benchmark</span></em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.init_kwargs_func" title="Permalink to this definition"></a></dt>
<dd><p>Dispached the kwargs initialization to monoview and multiview and creates
the kwargs variable</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>args</strong> (<em>parsed args objects</em>) – All the args passed by the user.</p></li>
<li><p><strong>benchmark</strong> (<em>dict</em>) – The name of the mono- and mutli-view classifiers to run in the benchmark</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><strong>kwargs</strong> – The arguments for each mono- and multiview algorithms</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>dict</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.init_monoview_exps">
<code class="sig-name descname">init_monoview_exps</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">classifier_names</span></em>, <em class="sig-param"><span class="n">views_dictionary</span></em>, <em class="sig-param"><span class="n">nb_class</span></em>, <em class="sig-param"><span class="n">kwargs_init</span></em>, <em class="sig-param"><span class="n">hps_method</span></em>, <em class="sig-param"><span class="n">hps_kwargs</span></em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.init_monoview_exps" title="Permalink to this definition"></a></dt>
<dd><p>Used to add each monoview exeperience args to the list of monoview experiences args.</p>
<p>First this function will check if the benchmark need mono- or/and multiview algorithms and adds to the right
dictionary the asked algorithms. If none is asked by the user, all will be added.</p>
<p>If the keyword <cite>“Benchmark”</cite> is used, all mono- and multiview algorithms will be added.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>classifier_names</strong> (<em>dictionary</em>) – All types of monoview and multiview experiments that have to be benchmarked</p></li>
<li><p><strong>argument_dictionaries</strong> (<em>dictionary</em>) – Maps monoview and multiview experiments arguments.</p></li>
<li><p><strong>views_dictionary</strong> (<em>dictionary</em>) – Maps the view names to their index in the HDF5 dataset</p></li>
<li><p><strong>nb_class</strong> (<em>integer</em>) – Number of different labels in the classification</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><strong>benchmark</strong> – Dictionary resuming which mono- and multiview algorithms which will be used in the benchmark.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>Dictionary of dictionaries</p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.is_dict_in">
<code class="sig-name descname">is_dict_in</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dictionary</span></em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.is_dict_in" title="Permalink to this definition"></a></dt>
<dd><p>Returns True if any of the dictionary value is a dictionary itself.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>dictionary</strong> – </p>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="multiview_platform.mono_multi_view_classifiers.exec_classif.set_element">
<code class="sig-name descname">set_element</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dictionary</span></em>, <em class="sig-param"><span class="n">path</span></em>, <em class="sig-param"><span class="n">value</span></em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.exec_classif.set_element" title="Permalink to this definition"></a></dt>
<dd><p>Set value in dictionary at the location indicated by path</p>
</dd></dl>
</div> </div>
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<head> <head>
<meta charset="utf-8" /> <meta http-equiv="X-UA-Compatible" content="IE=Edge" />
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<title>Metrics framework &#8212; SuMMIT 0 documentation</title> <title>Metrics framework &#8212; SuMMIT 0 documentation</title>
<link rel="stylesheet" href="../../_static/classic.css" type="text/css" /> <link rel="stylesheet" href="../../_static/classic.css" type="text/css" />
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...@@ -36,72 +38,8 @@ ...@@ -36,72 +38,8 @@
<div class="bodywrapper"> <div class="bodywrapper">
<div class="body" role="main"> <div class="body" role="main">
<div class="section" id="module-multiview_platform.mono_multi_view_classifiers.metrics.framework"> <div class="section" id="metrics-framework">
<span id="metrics-framework"></span><h1>Metrics framework<a class="headerlink" href="#module-multiview_platform.mono_multi_view_classifiers.metrics.framework" title="Permalink to this headline"></a></h1> <h1>Metrics framework<a class="headerlink" href="#metrics-framework" title="Permalink to this headline"></a></h1>
<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="py function">
<dt id="multiview_platform.mono_multi_view_classifiers.metrics.framework.get_config">
<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="py function">
<dt id="multiview_platform.mono_multi_view_classifiers.metrics.framework.get_scorer">
<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="py function">
<dt id="multiview_platform.mono_multi_view_classifiers.metrics.framework.score">
<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>
</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> </div>
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<title>Diversity Fusion Classifiers &#8212; SuMMIT 0 documentation</title> <title>Diversity Fusion Classifiers &#8212; SuMMIT 0 documentation</title>
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<title>Utils execution module &#8212; SuMMIT 0 documentation</title> <title>Utils execution module &#8212; SuMMIT 0 documentation</title>
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<div class="section" id="utils-execution-module"> <div class="section" id="utils-execution-module">
<h1>Utils execution module<a class="headerlink" href="#utils-execution-module" title="Permalink to this headline"></a></h1> <h1>Utils execution module<a class="headerlink" href="#utils-execution-module" title="Permalink to this headline"></a></h1>
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<span id="utils-multiclass-module"></span><h1>Utils Multiclass module<a class="headerlink" href="#module-multiview_platform.mono_multi_view_classifiers.utils.multiclass" title="Permalink to this headline"></a></h1> <h1>Utils Multiclass module<a class="headerlink" href="#utils-multiclass-module" title="Permalink to this headline"></a></h1>
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<title>multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion package &#8212; SuMMIT 0 documentation</title> <title>multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion package &#8212; SuMMIT 0 documentation</title>
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<div class="section" id="multiview-platform-mono-multi-view-classifiers-multiview-classifiers-difficulty-fusion-difficulty-fusion-module"> <div class="section" id="multiview-platform-mono-multi-view-classifiers-multiview-classifiers-difficulty-fusion-difficulty-fusion-module">
<h2>multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.difficulty_fusion module<a class="headerlink" href="#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-difficulty-fusion-difficulty-fusion-module" title="Permalink to this headline"></a></h2> <h2>multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.difficulty_fusion module<a class="headerlink" href="#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-difficulty-fusion-difficulty-fusion-module" title="Permalink to this headline"></a></h2>
</div> </div>
<div class="section" id="module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion"> <div class="section" id="module-contents">
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion" title="Permalink to this headline"></a></h2> <h2>Module contents<a class="headerlink" href="#module-contents" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.DifficultyFusion">
<em class="property">class </em><code class="sig-name descname">DifficultyFusion</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">random_state</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">classifier_names</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">monoview_estimators</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">classifier_configs</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.DifficultyFusion" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.additions.diversity_utils.GlobalDiversityFusionClassifier</span></code></p>
<dl class="py method">
<dt id="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.DifficultyFusion.diversity_measure">
<code class="sig-name descname">diversity_measure</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">classifiers_decisions</span></em>, <em class="sig-param"><span class="n">combination</span></em>, <em class="sig-param"><span class="n">y</span></em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.DifficultyFusion.diversity_measure" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
</div> </div>
</div> </div>
...@@ -86,7 +77,7 @@ ...@@ -86,7 +77,7 @@
<li><a class="reference internal" href="#submodules">Submodules</a></li> <li><a class="reference internal" href="#submodules">Submodules</a></li>
<li><a class="reference internal" href="#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-difficulty-fusion-analyze-results-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.analyze_results module</a></li> <li><a class="reference internal" href="#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-difficulty-fusion-analyze-results-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.analyze_results module</a></li>
<li><a class="reference internal" href="#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-difficulty-fusion-difficulty-fusion-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.difficulty_fusion module</a></li> <li><a class="reference internal" href="#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-difficulty-fusion-difficulty-fusion-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion.difficulty_fusion module</a></li>
<li><a class="reference internal" href="#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.difficulty_fusion">Module contents</a></li> <li><a class="reference internal" href="#module-contents">Module contents</a></li>
</ul> </ul>
</li> </li>
</ul> </ul>
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<meta charset="utf-8" /> <meta http-equiv="X-UA-Compatible" content="IE=Edge" />
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<title>multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion package &#8212; SuMMIT 0 documentation</title> <title>multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion package &#8212; SuMMIT 0 documentation</title>
<link rel="stylesheet" href="../_static/classic.css" type="text/css" /> <link rel="stylesheet" href="../_static/classic.css" type="text/css" />
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<div class="section" id="multiview-platform-mono-multi-view-classifiers-multiview-classifiers-disagree-fusion-disagree-fusion-module"> <div class="section" id="multiview-platform-mono-multi-view-classifiers-multiview-classifiers-disagree-fusion-disagree-fusion-module">
<h2>multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.disagree_fusion module<a class="headerlink" href="#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-disagree-fusion-disagree-fusion-module" title="Permalink to this headline"></a></h2> <h2>multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.disagree_fusion module<a class="headerlink" href="#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-disagree-fusion-disagree-fusion-module" title="Permalink to this headline"></a></h2>
</div> </div>
<div class="section" id="module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion"> <div class="section" id="module-contents">
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion" title="Permalink to this headline"></a></h2> <h2>Module contents<a class="headerlink" href="#module-contents" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.DisagreeFusion">
<em class="property">class </em><code class="sig-name descname">DisagreeFusion</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">random_state</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">classifier_names</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">monoview_estimators</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">classifier_configs</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.DisagreeFusion" title="Permalink to this definition"></a></dt>
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.additions.diversity_utils.CoupleDiversityFusionClassifier</span></code></p>
<dl class="py method">
<dt id="multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.DisagreeFusion.diversity_measure">
<code class="sig-name descname">diversity_measure</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">first_classifier_decision</span></em>, <em class="sig-param"><span class="n">second_classifier_decision</span></em>, <em class="sig-param"><span class="n">_</span></em><span class="sig-paren">)</span><a class="headerlink" href="#multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.DisagreeFusion.diversity_measure" title="Permalink to this definition"></a></dt>
<dd></dd></dl>
</dd></dl>
</div> </div>
</div> </div>
...@@ -86,7 +77,7 @@ ...@@ -86,7 +77,7 @@
<li><a class="reference internal" href="#submodules">Submodules</a></li> <li><a class="reference internal" href="#submodules">Submodules</a></li>
<li><a class="reference internal" href="#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-disagree-fusion-analyze-results-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.analyze_results module</a></li> <li><a class="reference internal" href="#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-disagree-fusion-analyze-results-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.analyze_results module</a></li>
<li><a class="reference internal" href="#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-disagree-fusion-disagree-fusion-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.disagree_fusion module</a></li> <li><a class="reference internal" href="#multiview-platform-mono-multi-view-classifiers-multiview-classifiers-disagree-fusion-disagree-fusion-module">multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion.disagree_fusion module</a></li>
<li><a class="reference internal" href="#module-multiview_platform.mono_multi_view_classifiers.multiview_classifiers.disagree_fusion">Module contents</a></li> <li><a class="reference internal" href="#module-contents">Module contents</a></li>
</ul> </ul>
</li> </li>
</ul> </ul>
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