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<title>pyFTS.distributed package — pyFTS 1.6 documentation</title>
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<div class="section" id="pyfts-distributed-package">
<h1>pyFTS.distributed package<a class="headerlink" href="#pyfts-distributed-package" title="Permalink to this headline">¶</a></h1>
<div class="section" id="module-pyFTS.distributed">
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-pyFTS.distributed" title="Permalink to this headline">¶</a></h2>
</div>
<div class="section" id="submodules">
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
</div>
<div class="section" id="pyfts-distributed-dispy-module">
<h2>pyFTS.distributed.dispy module<a class="headerlink" href="#pyfts-distributed-dispy-module" title="Permalink to this headline">¶</a></h2>
</div>
<div class="section" id="module-pyFTS.distributed.spark">
<span id="pyfts-distributed-spark-module"></span><h2>pyFTS.distributed.spark module<a class="headerlink" href="#module-pyFTS.distributed.spark" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="pyFTS.distributed.spark.create_multivariate_model">
<code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">create_multivariate_model</code><span class="sig-paren">(</span><em>**parameters</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.distributed.spark.create_multivariate_model" title="Permalink to this definition">¶</a></dt>
<dd><p>From the dictionary of parameters, create a multivariate FTS model</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>parameters</strong> – dictionary of parameters</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">multivariate FTS model</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.distributed.spark.create_spark_conf">
<code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">create_spark_conf</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.distributed.spark.create_spark_conf" title="Permalink to this definition">¶</a></dt>
<dd><p>Configure the Spark master node</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> – </td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"></td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.distributed.spark.create_univariate_model">
<code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">create_univariate_model</code><span class="sig-paren">(</span><em>**parameters</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.distributed.spark.create_univariate_model" title="Permalink to this definition">¶</a></dt>
<dd><p>From the dictionary of parameters, create an univariate FTS model</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>parameters</strong> – dictionary of parameters</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">univariate FTS model</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.distributed.spark.distributed_predict">
<code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">distributed_predict</code><span class="sig-paren">(</span><em>data</em>, <em>model</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.distributed.spark.distributed_predict" title="Permalink to this definition">¶</a></dt>
<dd><p>The main method for distributed forecasting with FTS models using Spark clusters.</p>
<p>It takes a trained FTS model and the test data, connect with the Spark cluster,
proceed the distributed forecasting and return the merged forecasted values.</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>model</strong> – an FTS trained model</li>
<li><strong>data</strong> – test data</li>
<li><strong>url</strong> – URL of the Spark master</li>
<li><strong>app</strong> – </li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">forecasted values</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.distributed.spark.distributed_train">
<code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">distributed_train</code><span class="sig-paren">(</span><em>model</em>, <em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.distributed.spark.distributed_train" title="Permalink to this definition">¶</a></dt>
<dd><p>The main method for distributed training of FTS models using Spark clusters.</p>
<p>It takes an empty model and the train data, connect with the Spark cluster, proceed the
distributed training and return the learned model.</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>model</strong> – An empty (non-trained) FTS model</li>
<li><strong>data</strong> – train data</li>
<li><strong>url</strong> – URL of the Spark master node</li>
<li><strong>app</strong> – Application name</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">trained model</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.distributed.spark.get_clustered_partitioner">
<code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">get_clustered_partitioner</code><span class="sig-paren">(</span><em>explanatory_variables</em>, <em>target_variable</em>, <em>**parameters</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.distributed.spark.get_clustered_partitioner" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the UoD partitioner from the ‘shared_partitioner’ fuzzy sets, special case for
clustered multivariate FTS.</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>explanatory_variables</strong> – the list with the names of the explanatory variables</li>
<li><strong>target_variable</strong> – the name of the target variable</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">Partitioner object</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.distributed.spark.get_partitioner">
<code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">get_partitioner</code><span class="sig-paren">(</span><em>shared_partitioner</em>, <em>type='common'</em>, <em>variables=[]</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.distributed.spark.get_partitioner" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the UoD partitioner from the ‘shared_partitioner’ fuzzy sets</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>shared_partitioner</strong> – the shared variable with the fuzzy sets</li>
<li><strong>type</strong> – the type of the partitioner</li>
<li><strong>variables</strong> – in case of a Multivariate FTS, the list of variables</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">Partitioner object</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.distributed.spark.get_variables">
<code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">get_variables</code><span class="sig-paren">(</span><em>**parameters</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.distributed.spark.get_variables" title="Permalink to this definition">¶</a></dt>
<dd><p>From the dictionary of parameters, return a tuple with the list of explanatory and target variables</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>parameters</strong> – dictionary of parameters</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">a tuple with the list of explanatory and target variables</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.distributed.spark.share_parameters">
<code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">share_parameters</code><span class="sig-paren">(</span><em>model</em>, <em>context</em>, <em>data</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.distributed.spark.share_parameters" title="Permalink to this definition">¶</a></dt>
<dd><p>Create a shared variable with a dictionary of the model parameters and hyperparameters</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>model</strong> – the FTS model to extract the parameters and hyperparameters</li>
<li><strong>context</strong> – Spark context</li>
<li><strong>data</strong> – dataset</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">the shared variable with the dictionary of parameters</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.distributed.spark.slave_forecast_multivariate">
<code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">slave_forecast_multivariate</code><span class="sig-paren">(</span><em>data</em>, <em>**parameters</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.distributed.spark.slave_forecast_multivariate" title="Permalink to this definition">¶</a></dt>
<dd><p>Receive test data, create a multivariate FTS model from the parameters and return the forecasted values</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>data</strong> – test data</li>
<li><strong>parameters</strong> – dictionary of parameters</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">forecasted values from the data input</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.distributed.spark.slave_forecast_univariate">
<code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">slave_forecast_univariate</code><span class="sig-paren">(</span><em>data</em>, <em>**parameters</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.distributed.spark.slave_forecast_univariate" title="Permalink to this definition">¶</a></dt>
<dd><p>Receive test data, create an univariate FTS model from the parameters and return the forecasted values</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>data</strong> – test data</li>
<li><strong>parameters</strong> – dictionary of parameters</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">forecasted values from the data input</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.distributed.spark.slave_train_multivariate">
<code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">slave_train_multivariate</code><span class="sig-paren">(</span><em>data</em>, <em>**parameters</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.distributed.spark.slave_train_multivariate" title="Permalink to this definition">¶</a></dt>
<dd><p>Receive train data, train a multivariate FTS model and return the learned rules</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>data</strong> – train data</li>
<li><strong>parameters</strong> – dictionary of parameters</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">Key/value list of the learned rules</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="pyFTS.distributed.spark.slave_train_univariate">
<code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">slave_train_univariate</code><span class="sig-paren">(</span><em>data</em>, <em>**parameters</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.distributed.spark.slave_train_univariate" title="Permalink to this definition">¶</a></dt>
<dd><p>Receive train data, train an univariate FTS model and return the learned rules</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>data</strong> – train data</li>
<li><strong>parameters</strong> – dictionary of parameters</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">Key/value list of the learned rules</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
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