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Petrônio Cândido 2018-08-30 16:04:52 -03:00
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@ -38,7 +67,6 @@
<li><a href="pyFTS/benchmarks/benchmarks.html">pyFTS.benchmarks.benchmarks</a></li> <li><a href="pyFTS/benchmarks/benchmarks.html">pyFTS.benchmarks.benchmarks</a></li>
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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
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>modules</a> |</li>
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@ -50,6 +80,12 @@
<div class="viewcode-block" id="open_benchmark_db"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.open_benchmark_db">[docs]</a><span class="k">def</span> <span class="nf">open_benchmark_db</span><span class="p">(</span><span class="n">name</span><span class="p">):</span> <div class="viewcode-block" id="open_benchmark_db"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.open_benchmark_db">[docs]</a><span class="k">def</span> <span class="nf">open_benchmark_db</span><span class="p">(</span><span class="n">name</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Open a connection with a Sqlite database designed to store benchmark results.</span>
<span class="sd"> :param name: database filenem</span>
<span class="sd"> :return: a sqlite3 database connection</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">conn</span> <span class="o">=</span> <span class="n">sqlite3</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="n">name</span><span class="p">)</span> <span class="n">conn</span> <span class="o">=</span> <span class="n">sqlite3</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
<span class="c1">#performance optimizations</span> <span class="c1">#performance optimizations</span>
@ -61,6 +97,11 @@
<div class="viewcode-block" id="create_benchmark_tables"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.create_benchmark_tables">[docs]</a><span class="k">def</span> <span class="nf">create_benchmark_tables</span><span class="p">(</span><span class="n">conn</span><span class="p">):</span> <div class="viewcode-block" id="create_benchmark_tables"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.create_benchmark_tables">[docs]</a><span class="k">def</span> <span class="nf">create_benchmark_tables</span><span class="p">(</span><span class="n">conn</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Create a sqlite3 table designed to store benchmark results.</span>
<span class="sd"> :param conn: a sqlite3 database connection</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">conn</span><span class="o">.</span><span class="n">cursor</span><span class="p">()</span> <span class="n">c</span> <span class="o">=</span> <span class="n">conn</span><span class="o">.</span><span class="n">cursor</span><span class="p">()</span>
<span class="n">c</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s1">&#39;&#39;&#39;CREATE TABLE if not exists benchmarks(</span> <span class="n">c</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s1">&#39;&#39;&#39;CREATE TABLE if not exists benchmarks(</span>
@ -73,6 +114,29 @@
<div class="viewcode-block" id="insert_benchmark"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.insert_benchmark">[docs]</a><span class="k">def</span> <span class="nf">insert_benchmark</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">conn</span><span class="p">):</span> <div class="viewcode-block" id="insert_benchmark"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.insert_benchmark">[docs]</a><span class="k">def</span> <span class="nf">insert_benchmark</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">conn</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Insert benchmark data on database</span>
<span class="sd"> :param data: a tuple with the benchmark data with format:</span>
<span class="sd"> ID: integer incremental primary key</span>
<span class="sd"> Date: Date/hour of benchmark execution</span>
<span class="sd"> Dataset: Identify on which dataset the dataset was performed</span>
<span class="sd"> Tag: a user defined word that indentify a benchmark set</span>
<span class="sd"> Type: forecasting type (point, interval, distribution)</span>
<span class="sd"> Model: FTS model</span>
<span class="sd"> Transformation: The name of data transformation, if one was used</span>
<span class="sd"> Order: the order of the FTS method</span>
<span class="sd"> Scheme: UoD partitioning scheme</span>
<span class="sd"> Partitions: Number of partitions</span>
<span class="sd"> Size: Number of rules of the FTS model</span>
<span class="sd"> Steps: prediction horizon, i. e., the number of steps ahead</span>
<span class="sd"> Measure: accuracy measure</span>
<span class="sd"> Value: the measure value</span>
<span class="sd"> :param conn: a sqlite3 database connection</span>
<span class="sd"> :return:</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">conn</span><span class="o">.</span><span class="n">cursor</span><span class="p">()</span> <span class="n">c</span> <span class="o">=</span> <span class="n">conn</span><span class="o">.</span><span class="n">cursor</span><span class="p">()</span>
<span class="n">c</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s2">&quot;INSERT INTO benchmarks(Date, Dataset, Tag, Type, Model, &quot;</span> <span class="n">c</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s2">&quot;INSERT INTO benchmarks(Date, Dataset, Tag, Type, Model, &quot;</span>
@ -83,6 +147,15 @@
<div class="viewcode-block" id="process_common_data"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.process_common_data">[docs]</a><span class="k">def</span> <span class="nf">process_common_data</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="nb">type</span><span class="p">,</span> <span class="n">job</span><span class="p">):</span> <div class="viewcode-block" id="process_common_data"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.process_common_data">[docs]</a><span class="k">def</span> <span class="nf">process_common_data</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="nb">type</span><span class="p">,</span> <span class="n">job</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Wraps benchmark information on a tuple for sqlite database</span>
<span class="sd"> :param dataset: benchmark dataset</span>
<span class="sd"> :param tag: benchmark set alias</span>
<span class="sd"> :param type: forecasting type</span>
<span class="sd"> :param job: a dictionary with benchmark data</span>
<span class="sd"> :return: tuple for sqlite database</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">job</span><span class="p">[</span><span class="s2">&quot;obj&quot;</span><span class="p">]</span> <span class="n">model</span> <span class="o">=</span> <span class="n">job</span><span class="p">[</span><span class="s2">&quot;obj&quot;</span><span class="p">]</span>
<span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">benchmark_only</span><span class="p">:</span> <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">benchmark_only</span><span class="p">:</span>
<span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="nb">type</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">shortname</span><span class="p">,</span> <span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="nb">type</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">shortname</span><span class="p">,</span>
@ -99,6 +172,13 @@
<div class="viewcode-block" id="get_dataframe_from_bd"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.get_dataframe_from_bd">[docs]</a><span class="k">def</span> <span class="nf">get_dataframe_from_bd</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="nb">filter</span><span class="p">):</span> <div class="viewcode-block" id="get_dataframe_from_bd"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Util.get_dataframe_from_bd">[docs]</a><span class="k">def</span> <span class="nf">get_dataframe_from_bd</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="nb">filter</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Query the sqlite benchmark database and return a pandas dataframe with the results</span>
<span class="sd"> :param file: the url of the benchmark database</span>
<span class="sd"> :param filter: sql conditions to filter</span>
<span class="sd"> :return: pandas dataframe with the query results</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">con</span> <span class="o">=</span> <span class="n">sqlite3</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="n">file</span><span class="p">)</span> <span class="n">con</span> <span class="o">=</span> <span class="n">sqlite3</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
<span class="n">sql</span> <span class="o">=</span> <span class="s2">&quot;SELECT * from benchmarks&quot;</span> <span class="n">sql</span> <span class="o">=</span> <span class="s2">&quot;SELECT * from benchmarks&quot;</span>
<span class="k">if</span> <span class="nb">filter</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span> <span class="k">if</span> <span class="nb">filter</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
@ -1244,43 +1324,24 @@
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Powered by <a href="http://sphinx-doc.org/">Sphinx 1.7.2</a> <a href="../../../genindex.html" title="General Index"
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101
docs/_build/html/_modules/pyFTS/benchmarks/arima.html vendored
View File

@ -1,29 +1,59 @@
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <!doctype html>
<html xmlns="http://www.w3.org/1999/xhtml"> <html xmlns="http://www.w3.org/1999/xhtml">
<head> <head>
<meta http-equiv="X-UA-Compatible" content="IE=Edge" /> <meta http-equiv="X-UA-Compatible" content="IE=Edge" />
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<title>pyFTS.benchmarks.arima &#8212; pyFTS 1.2.3 documentation</title> <title>pyFTS.benchmarks.arima &#8212; pyFTS 1.2.3 documentation</title>
<link rel="stylesheet" href="../../../_static/alabaster.css" type="text/css" /> <link rel="stylesheet" href="../../../_static/bizstyle.css" type="text/css" />
<link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" /> <link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
<script type="text/javascript" src="../../../_static/documentation_options.js"></script> <script type="text/javascript" src="../../../_static/documentation_options.js"></script>
<script type="text/javascript" src="../../../_static/jquery.js"></script> <script type="text/javascript" src="../../../_static/jquery.js"></script>
<script type="text/javascript" src="../../../_static/underscore.js"></script> <script type="text/javascript" src="../../../_static/underscore.js"></script>
<script type="text/javascript" src="../../../_static/doctools.js"></script> <script type="text/javascript" src="../../../_static/doctools.js"></script>
<script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> <script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script>
<script type="text/javascript" src="../../../_static/bizstyle.js"></script>
<link rel="index" title="Index" href="../../../genindex.html" /> <link rel="index" title="Index" href="../../../genindex.html" />
<link rel="search" title="Search" href="../../../search.html" /> <link rel="search" title="Search" href="../../../search.html" />
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<meta name="viewport" content="width=device-width, initial-scale=0.9, maximum-scale=0.9" />
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<h3>Navigation</h3>
<ul>
<li class="right" style="margin-right: 10px">
<a href="../../../genindex.html" title="General Index"
accesskey="I">index</a></li>
<li class="right" >
<a href="../../../py-modindex.html" title="Python Module Index"
>modules</a> |</li>
<li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.2.3 documentation</a> &#187;</li>
<li class="nav-item nav-item-1"><a href="../../index.html" accesskey="U">Module code</a> &#187;</li>
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<div class="sphinxsidebar" role="navigation" aria-label="main navigation">
<div class="sphinxsidebarwrapper">
<p class="logo"><a href="../../../index.html">
<img class="logo" src="../../../_static/logo_heading2.png" alt="Logo"/>
</a></p>
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<h3>Quick search</h3>
<div class="searchformwrapper">
<form class="search" action="../../../search.html" method="get">
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<script type="text/javascript">$('#searchbox').show(0);</script>
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<div class="document"> <div class="document">
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@ -245,43 +275,24 @@
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<h3>Quick search</h3>
<div class="searchformwrapper">
<form class="search" action="../../../search.html" method="get">
<input type="text" name="q" />
<input type="submit" value="Go" />
<input type="hidden" name="check_keywords" value="yes" />
<input type="hidden" name="area" value="default" />
</form>
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</div>
<script type="text/javascript">$('#searchbox').show(0);</script>
</div>
</div>
<div class="clearer"></div> <div class="clearer"></div>
</div> </div>
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&copy;2018, Machine Intelligence and Data Science Laboratory - UFMG - Brazil. <h3>Navigation</h3>
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<li class="right" >
<a href="../../../py-modindex.html" title="Python Module Index"
>modules</a> |</li>
<li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.2.3 documentation</a> &#187;</li>
<li class="nav-item nav-item-1"><a href="../../index.html" >Module code</a> &#187;</li>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 1.7.2.
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161
docs/_build/html/_modules/pyFTS/benchmarks/benchmarks.html vendored
View File

@ -1,29 +1,59 @@
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <!doctype html>
<html xmlns="http://www.w3.org/1999/xhtml"> <html xmlns="http://www.w3.org/1999/xhtml">
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<meta http-equiv="X-UA-Compatible" content="IE=Edge" /> <meta http-equiv="X-UA-Compatible" content="IE=Edge" />
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<title>pyFTS.benchmarks.benchmarks &#8212; pyFTS 1.2.3 documentation</title> <title>pyFTS.benchmarks.benchmarks &#8212; pyFTS 1.2.3 documentation</title>
<link rel="stylesheet" href="../../../_static/alabaster.css" type="text/css" /> <link rel="stylesheet" href="../../../_static/bizstyle.css" type="text/css" />
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@ -352,7 +382,7 @@
<div class="viewcode-block" id="run_point"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.run_point">[docs]</a><span class="k">def</span> <span class="nf">run_point</span><span class="p">(</span><span class="n">mfts</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">train_data</span><span class="p">,</span> <span class="n">test_data</span><span class="p">,</span> <span class="n">window_key</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> <div class="viewcode-block" id="run_point"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.run_point">[docs]</a><span class="k">def</span> <span class="nf">run_point</span><span class="p">(</span><span class="n">mfts</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">train_data</span><span class="p">,</span> <span class="n">test_data</span><span class="p">,</span> <span class="n">window_key</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span> <span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Point forecast benchmark function to be executed on cluster nodes</span> <span class="sd"> Run the point forecasting benchmarks</span>
<span class="sd"> :param mfts: FTS model</span> <span class="sd"> :param mfts: FTS model</span>
<span class="sd"> :param partitioner: Universe of Discourse partitioner</span> <span class="sd"> :param partitioner: Universe of Discourse partitioner</span>
@ -416,7 +446,7 @@
<div class="viewcode-block" id="run_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.run_interval">[docs]</a><span class="k">def</span> <span class="nf">run_interval</span><span class="p">(</span><span class="n">mfts</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">train_data</span><span class="p">,</span> <span class="n">test_data</span><span class="p">,</span> <span class="n">window_key</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> <div class="viewcode-block" id="run_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.run_interval">[docs]</a><span class="k">def</span> <span class="nf">run_interval</span><span class="p">(</span><span class="n">mfts</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">train_data</span><span class="p">,</span> <span class="n">test_data</span><span class="p">,</span> <span class="n">window_key</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span> <span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Interval forecast benchmark function to be executed on cluster nodes</span> <span class="sd"> Run the interval forecasting benchmarks</span>
<span class="sd"> :param mfts: FTS model</span> <span class="sd"> :param mfts: FTS model</span>
<span class="sd"> :param partitioner: Universe of Discourse partitioner</span> <span class="sd"> :param partitioner: Universe of Discourse partitioner</span>
@ -475,7 +505,7 @@
<div class="viewcode-block" id="run_probabilistic"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.run_probabilistic">[docs]</a><span class="k">def</span> <span class="nf">run_probabilistic</span><span class="p">(</span><span class="n">mfts</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">train_data</span><span class="p">,</span> <span class="n">test_data</span><span class="p">,</span> <span class="n">window_key</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> <div class="viewcode-block" id="run_probabilistic"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.run_probabilistic">[docs]</a><span class="k">def</span> <span class="nf">run_probabilistic</span><span class="p">(</span><span class="n">mfts</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">train_data</span><span class="p">,</span> <span class="n">test_data</span><span class="p">,</span> <span class="n">window_key</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span> <span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Probabilistic forecast benchmark function to be executed on cluster nodes</span> <span class="sd"> Run the probabilistic forecasting benchmarks</span>
<span class="sd"> :param mfts: FTS model</span> <span class="sd"> :param mfts: FTS model</span>
<span class="sd"> :param partitioner: Universe of Discourse partitioner</span> <span class="sd"> :param partitioner: Universe of Discourse partitioner</span>
@ -537,6 +567,15 @@
<div class="viewcode-block" id="process_point_jobs"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.process_point_jobs">[docs]</a><span class="k">def</span> <span class="nf">process_point_jobs</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="n">job</span><span class="p">,</span> <span class="n">conn</span><span class="p">):</span> <div class="viewcode-block" id="process_point_jobs"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.process_point_jobs">[docs]</a><span class="k">def</span> <span class="nf">process_point_jobs</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="n">job</span><span class="p">,</span> <span class="n">conn</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Extract information from a dictionary with point benchmark results and save it on a database</span>
<span class="sd"> :param dataset: the benchmark dataset name</span>
<span class="sd"> :param tag: alias for the benchmark group being executed</span>
<span class="sd"> :param job: a dictionary with the benchmark results</span>
<span class="sd"> :param conn: a connection to a Sqlite database</span>
<span class="sd"> :return:</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">bUtil</span><span class="o">.</span><span class="n">process_common_data</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="s1">&#39;point&#39;</span><span class="p">,</span><span class="n">job</span><span class="p">)</span> <span class="n">data</span> <span class="o">=</span> <span class="n">bUtil</span><span class="o">.</span><span class="n">process_common_data</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="s1">&#39;point&#39;</span><span class="p">,</span><span class="n">job</span><span class="p">)</span>
@ -555,6 +594,15 @@
<div class="viewcode-block" id="process_interval_jobs"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.process_interval_jobs">[docs]</a><span class="k">def</span> <span class="nf">process_interval_jobs</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="n">job</span><span class="p">,</span> <span class="n">conn</span><span class="p">):</span> <div class="viewcode-block" id="process_interval_jobs"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.process_interval_jobs">[docs]</a><span class="k">def</span> <span class="nf">process_interval_jobs</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="n">job</span><span class="p">,</span> <span class="n">conn</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Extract information from an dictionary with interval benchmark results and save it on a database</span>
<span class="sd"> :param dataset: the benchmark dataset name</span>
<span class="sd"> :param tag: alias for the benchmark group being executed</span>
<span class="sd"> :param job: a dictionary with the benchmark results</span>
<span class="sd"> :param conn: a connection to a Sqlite database</span>
<span class="sd"> :return:</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">bUtil</span><span class="o">.</span><span class="n">process_common_data</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="s1">&#39;interval&#39;</span><span class="p">,</span> <span class="n">job</span><span class="p">)</span> <span class="n">data</span> <span class="o">=</span> <span class="n">bUtil</span><span class="o">.</span><span class="n">process_common_data</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="s1">&#39;interval&#39;</span><span class="p">,</span> <span class="n">job</span><span class="p">)</span>
@ -591,6 +639,15 @@
<div class="viewcode-block" id="process_probabilistic_jobs"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.process_probabilistic_jobs">[docs]</a><span class="k">def</span> <span class="nf">process_probabilistic_jobs</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="n">job</span><span class="p">,</span> <span class="n">conn</span><span class="p">):</span> <div class="viewcode-block" id="process_probabilistic_jobs"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.process_probabilistic_jobs">[docs]</a><span class="k">def</span> <span class="nf">process_probabilistic_jobs</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="n">job</span><span class="p">,</span> <span class="n">conn</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Extract information from an dictionary with probabilistic benchmark results and save it on a database</span>
<span class="sd"> :param dataset: the benchmark dataset name</span>
<span class="sd"> :param tag: alias for the benchmark group being executed</span>
<span class="sd"> :param job: a dictionary with the benchmark results</span>
<span class="sd"> :param conn: a connection to a Sqlite database</span>
<span class="sd"> :return:</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">bUtil</span><span class="o">.</span><span class="n">process_common_data</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="s1">&#39;density&#39;</span><span class="p">,</span> <span class="n">job</span><span class="p">)</span> <span class="n">data</span> <span class="o">=</span> <span class="n">bUtil</span><span class="o">.</span><span class="n">process_common_data</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="s1">&#39;density&#39;</span><span class="p">,</span> <span class="n">job</span><span class="p">)</span>
@ -606,6 +663,16 @@
<div class="viewcode-block" id="print_point_statistics"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.print_point_statistics">[docs]</a><span class="k">def</span> <span class="nf">print_point_statistics</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">models</span><span class="p">,</span> <span class="n">externalmodels</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">externalforecasts</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">indexers</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span> <div class="viewcode-block" id="print_point_statistics"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.print_point_statistics">[docs]</a><span class="k">def</span> <span class="nf">print_point_statistics</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">models</span><span class="p">,</span> <span class="n">externalmodels</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">externalforecasts</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">indexers</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Run point benchmarks on given models and data and print the results</span>
<span class="sd"> :param data: test data</span>
<span class="sd"> :param models: a list of FTS models to benchmark</span>
<span class="sd"> :param externalmodels: a list with benchmark models (façades for other methods)</span>
<span class="sd"> :param externalforecasts:</span>
<span class="sd"> :param indexers:</span>
<span class="sd"> :return:</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">ret</span> <span class="o">=</span> <span class="s2">&quot;Model &amp; Order &amp; RMSE &amp; SMAPE &amp; Theil&#39;s U </span><span class="se">\\\\</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span> <span class="n">ret</span> <span class="o">=</span> <span class="s2">&quot;Model &amp; Order &amp; RMSE &amp; SMAPE &amp; Theil&#39;s U </span><span class="se">\\\\</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span>
<span class="k">for</span> <span class="n">count</span><span class="p">,</span><span class="n">model</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">models</span><span class="p">,</span><span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span> <span class="k">for</span> <span class="n">count</span><span class="p">,</span><span class="n">model</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">models</span><span class="p">,</span><span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="n">_rmse</span><span class="p">,</span> <span class="n">_smape</span><span class="p">,</span> <span class="n">_u</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">get_point_statistics</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">indexers</span><span class="p">)</span> <span class="n">_rmse</span><span class="p">,</span> <span class="n">_smape</span><span class="p">,</span> <span class="n">_u</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">get_point_statistics</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">indexers</span><span class="p">)</span>
@ -629,6 +696,13 @@
<div class="viewcode-block" id="print_interval_statistics"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.print_interval_statistics">[docs]</a><span class="k">def</span> <span class="nf">print_interval_statistics</span><span class="p">(</span><span class="n">original</span><span class="p">,</span> <span class="n">models</span><span class="p">):</span> <div class="viewcode-block" id="print_interval_statistics"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.print_interval_statistics">[docs]</a><span class="k">def</span> <span class="nf">print_interval_statistics</span><span class="p">(</span><span class="n">original</span><span class="p">,</span> <span class="n">models</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Run interval benchmarks on given models and data and print the results</span>
<span class="sd"> :param data: test data</span>
<span class="sd"> :param models: a list of FTS models to benchmark</span>
<span class="sd"> :return:</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">ret</span> <span class="o">=</span> <span class="s2">&quot;Model &amp; Order &amp; Sharpness &amp; Resolution &amp; Coverage &amp; .05 &amp; .25 &amp; .75 &amp; .95 </span><span class="se">\\\\</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span> <span class="n">ret</span> <span class="o">=</span> <span class="s2">&quot;Model &amp; Order &amp; Sharpness &amp; Resolution &amp; Coverage &amp; .05 &amp; .25 &amp; .75 &amp; .95 </span><span class="se">\\\\</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span>
<span class="k">for</span> <span class="n">fts</span> <span class="ow">in</span> <span class="n">models</span><span class="p">:</span> <span class="k">for</span> <span class="n">fts</span> <span class="ow">in</span> <span class="n">models</span><span class="p">:</span>
<span class="n">_sharp</span><span class="p">,</span> <span class="n">_res</span><span class="p">,</span> <span class="n">_cov</span><span class="p">,</span> <span class="n">_q5</span><span class="p">,</span> <span class="n">_q25</span><span class="p">,</span> <span class="n">_q75</span><span class="p">,</span> <span class="n">_q95</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">get_interval_statistics</span><span class="p">(</span><span class="n">original</span><span class="p">,</span> <span class="n">fts</span><span class="p">)</span> <span class="n">_sharp</span><span class="p">,</span> <span class="n">_res</span><span class="p">,</span> <span class="n">_cov</span><span class="p">,</span> <span class="n">_q5</span><span class="p">,</span> <span class="n">_q25</span><span class="p">,</span> <span class="n">_q75</span><span class="p">,</span> <span class="n">_q95</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">get_interval_statistics</span><span class="p">(</span><span class="n">original</span><span class="p">,</span> <span class="n">fts</span><span class="p">)</span>
@ -645,6 +719,13 @@
<div class="viewcode-block" id="print_distribution_statistics"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.print_distribution_statistics">[docs]</a><span class="k">def</span> <span class="nf">print_distribution_statistics</span><span class="p">(</span><span class="n">original</span><span class="p">,</span> <span class="n">models</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="n">resolution</span><span class="p">):</span> <div class="viewcode-block" id="print_distribution_statistics"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.print_distribution_statistics">[docs]</a><span class="k">def</span> <span class="nf">print_distribution_statistics</span><span class="p">(</span><span class="n">original</span><span class="p">,</span> <span class="n">models</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="n">resolution</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Run probabilistic benchmarks on given models and data and print the results</span>
<span class="sd"> :param data: test data</span>
<span class="sd"> :param models: a list of FTS models to benchmark</span>
<span class="sd"> :return:</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">ret</span> <span class="o">=</span> <span class="s2">&quot;Model &amp; Order &amp; Interval &amp; Distribution </span><span class="se">\\\\</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span> <span class="n">ret</span> <span class="o">=</span> <span class="s2">&quot;Model &amp; Order &amp; Interval &amp; Distribution </span><span class="se">\\\\</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span>
<span class="k">for</span> <span class="n">fts</span> <span class="ow">in</span> <span class="n">models</span><span class="p">:</span> <span class="k">for</span> <span class="n">fts</span> <span class="ow">in</span> <span class="n">models</span><span class="p">:</span>
<span class="n">_crps1</span><span class="p">,</span> <span class="n">_crps2</span><span class="p">,</span> <span class="n">_t1</span><span class="p">,</span> <span class="n">_t2</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">get_distribution_statistics</span><span class="p">(</span><span class="n">original</span><span class="p">,</span> <span class="n">fts</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="n">resolution</span><span class="p">)</span> <span class="n">_crps1</span><span class="p">,</span> <span class="n">_crps2</span><span class="p">,</span> <span class="n">_t1</span><span class="p">,</span> <span class="n">_t2</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">get_distribution_statistics</span><span class="p">(</span><span class="n">original</span><span class="p">,</span> <span class="n">fts</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="n">resolution</span><span class="p">)</span>
@ -736,6 +817,9 @@
<div class="viewcode-block" id="plot_density_rectange"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.plot_density_rectange">[docs]</a><span class="k">def</span> <span class="nf">plot_density_rectange</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">cmap</span><span class="p">,</span> <span class="n">density</span><span class="p">,</span> <span class="n">fig</span><span class="p">,</span> <span class="n">resolution</span><span class="p">,</span> <span class="n">time_from</span><span class="p">,</span> <span class="n">time_to</span><span class="p">):</span> <div class="viewcode-block" id="plot_density_rectange"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.plot_density_rectange">[docs]</a><span class="k">def</span> <span class="nf">plot_density_rectange</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">cmap</span><span class="p">,</span> <span class="n">density</span><span class="p">,</span> <span class="n">fig</span><span class="p">,</span> <span class="n">resolution</span><span class="p">,</span> <span class="n">time_from</span><span class="p">,</span> <span class="n">time_to</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Auxiliar function to plot_compared_intervals_ahead</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="kn">from</span> <span class="nn">matplotlib.patches</span> <span class="k">import</span> <span class="n">Rectangle</span> <span class="kn">from</span> <span class="nn">matplotlib.patches</span> <span class="k">import</span> <span class="n">Rectangle</span>
<span class="kn">from</span> <span class="nn">matplotlib.collections</span> <span class="k">import</span> <span class="n">PatchCollection</span> <span class="kn">from</span> <span class="nn">matplotlib.collections</span> <span class="k">import</span> <span class="n">PatchCollection</span>
<span class="n">patches</span> <span class="o">=</span> <span class="p">[]</span> <span class="n">patches</span> <span class="o">=</span> <span class="p">[]</span>
@ -1192,43 +1276,24 @@
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View File

@ -1,29 +1,59 @@
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<li class="right" >
<a href="../../../py-modindex.html" title="Python Module Index"
>modules</a> |</li>
<li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.2.3 documentation</a> &#187;</li>
<li class="nav-item nav-item-1"><a href="../../index.html" accesskey="U">Module code</a> &#187;</li>
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@ -104,43 +134,24 @@
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<h1>Source code for pyFTS.benchmarks.parallel_benchmarks</h1><div class="highlight"><pre>
<span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">joblib Parallelized Benchmarks to FTS methods</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">import</span> <span class="nn">datetime</span>
<span class="kn">import</span> <span class="nn">multiprocessing</span>
<span class="kn">import</span> <span class="nn">time</span>
<span class="kn">from</span> <span class="nn">copy</span> <span class="k">import</span> <span class="n">deepcopy</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">joblib</span> <span class="k">import</span> <span class="n">Parallel</span><span class="p">,</span> <span class="n">delayed</span>
<span class="kn">from</span> <span class="nn">pyFTS.benchmarks</span> <span class="k">import</span> <span class="n">benchmarks</span><span class="p">,</span> <span class="n">Util</span> <span class="k">as</span> <span class="n">bUtil</span>
<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">Util</span>
<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="k">import</span> <span class="n">Grid</span>
<div class="viewcode-block" id="run_point"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.parallel_benchmarks.run_point">[docs]</a><span class="k">def</span> <span class="nf">run_point</span><span class="p">(</span><span class="n">mfts</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">train_data</span><span class="p">,</span> <span class="n">test_data</span><span class="p">,</span> <span class="n">transformation</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">indexer</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Point forecast benchmark function to be executed on threads</span>
<span class="sd"> :param mfts: FTS model</span>
<span class="sd"> :param partitioner: Universe of Discourse partitioner</span>
<span class="sd"> :param train_data: data used to train the model</span>
<span class="sd"> :param test_data: ata used to test the model</span>
<span class="sd"> :param window_key: id of the sliding window</span>
<span class="sd"> :param transformation: data transformation</span>
<span class="sd"> :param indexer: seasonal indexer</span>
<span class="sd"> :return: a dictionary with the benchmark results </span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">pttr</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">partitioner</span><span class="o">.</span><span class="vm">__module__</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">_key</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span> <span class="o">+</span> <span class="s2">&quot; n = &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span> <span class="o">+</span> <span class="n">pttr</span> <span class="o">+</span> <span class="s2">&quot; q = &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">partitioner</span><span class="o">.</span><span class="n">partitions</span><span class="p">)</span>
<span class="n">mfts</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="n">partitioner</span>
<span class="k">if</span> <span class="n">transformation</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">mfts</span><span class="o">.</span><span class="n">append_transformation</span><span class="p">(</span><span class="n">transformation</span><span class="p">)</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">mfts</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">train_data</span><span class="p">,</span> <span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
<span class="n">_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">times</span> <span class="o">=</span> <span class="n">_end</span> <span class="o">-</span> <span class="n">_start</span>
<span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">_rmse</span><span class="p">,</span> <span class="n">_smape</span><span class="p">,</span> <span class="n">_u</span> <span class="o">=</span> <span class="n">benchmarks</span><span class="o">.</span><span class="n">get_point_statistics</span><span class="p">(</span><span class="n">test_data</span><span class="p">,</span> <span class="n">mfts</span><span class="p">,</span> <span class="n">indexer</span><span class="p">)</span>
<span class="n">_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">times</span> <span class="o">+=</span> <span class="n">_end</span> <span class="o">-</span> <span class="n">_start</span>
<span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
<span class="n">_rmse</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">_smape</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">_u</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">times</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">ret</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;key&#39;</span><span class="p">:</span> <span class="n">_key</span><span class="p">,</span> <span class="s1">&#39;obj&#39;</span><span class="p">:</span> <span class="n">mfts</span><span class="p">,</span> <span class="s1">&#39;rmse&#39;</span><span class="p">:</span> <span class="n">_rmse</span><span class="p">,</span> <span class="s1">&#39;smape&#39;</span><span class="p">:</span> <span class="n">_smape</span><span class="p">,</span> <span class="s1">&#39;u&#39;</span><span class="p">:</span> <span class="n">_u</span><span class="p">,</span> <span class="s1">&#39;time&#39;</span><span class="p">:</span> <span class="n">times</span><span class="p">}</span>
<span class="nb">print</span><span class="p">(</span><span class="n">ret</span><span class="p">)</span>
<span class="k">return</span> <span class="n">ret</span></div>
<div class="viewcode-block" id="point_sliding_window"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.parallel_benchmarks.point_sliding_window">[docs]</a><span class="k">def</span> <span class="nf">point_sliding_window</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">windowsize</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">models</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">partitioners</span><span class="o">=</span><span class="p">[</span><span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">],</span>
<span class="n">partitions</span><span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">],</span> <span class="n">max_order</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">transformation</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">indexer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dump</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">save</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sintetic</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Parallel sliding window benchmarks for FTS point forecasters</span>
<span class="sd"> :param data: </span>
<span class="sd"> :param windowsize: size of sliding window</span>
<span class="sd"> :param train: percentual of sliding window data used to train the models</span>
<span class="sd"> :param models: FTS point forecasters</span>
<span class="sd"> :param partitioners: Universe of Discourse partitioner</span>
<span class="sd"> :param partitions: the max number of partitions on the Universe of Discourse </span>
<span class="sd"> :param max_order: the max order of the models (for high order models)</span>
<span class="sd"> :param transformation: data transformation</span>
<span class="sd"> :param indexer: seasonal indexer</span>
<span class="sd"> :param dump: </span>
<span class="sd"> :param save: save results</span>
<span class="sd"> :param file: file path to save the results</span>
<span class="sd"> :param sintetic: if true only the average and standard deviation of the results</span>
<span class="sd"> :return: DataFrame with the results</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">_process_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Process Start: {0: %H:%M:%S}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()))</span>
<span class="n">num_cores</span> <span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">cpu_count</span><span class="p">()</span>
<span class="n">pool</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">objs</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">rmse</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">smape</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">u</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">times</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">benchmarks</span><span class="o">.</span><span class="n">get_point_methods</span><span class="p">():</span>
<span class="n">mfts</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
<span class="k">if</span> <span class="n">mfts</span><span class="o">.</span><span class="n">is_high_order</span><span class="p">:</span>
<span class="k">for</span> <span class="n">order</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_order</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
<span class="k">if</span> <span class="n">order</span> <span class="o">&gt;=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">min_order</span><span class="p">:</span>
<span class="n">mfts</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
<span class="n">mfts</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">order</span>
<span class="n">pool</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mfts</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">pool</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mfts</span><span class="p">)</span>
<span class="n">experiments</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="ow">in</span> <span class="n">Util</span><span class="o">.</span><span class="n">sliding_window</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">windowsize</span><span class="p">,</span> <span class="n">train</span><span class="p">):</span>
<span class="n">experiments</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">if</span> <span class="n">dump</span><span class="p">:</span> <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Window: </span><span class="si">{0}</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ct</span><span class="p">))</span>
<span class="k">for</span> <span class="n">partition</span> <span class="ow">in</span> <span class="n">partitions</span><span class="p">:</span>
<span class="k">for</span> <span class="n">partitioner</span> <span class="ow">in</span> <span class="n">partitioners</span><span class="p">:</span>
<span class="n">data_train_fs</span> <span class="o">=</span> <span class="n">partitioner</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">partition</span><span class="p">,</span> <span class="n">transformation</span><span class="o">=</span><span class="n">transformation</span><span class="p">)</span>
<span class="n">results</span> <span class="o">=</span> <span class="n">Parallel</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="n">num_cores</span><span class="p">)(</span>
<span class="n">delayed</span><span class="p">(</span><span class="n">run_point</span><span class="p">)(</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">m</span><span class="p">),</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">data_train_fs</span><span class="p">),</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">train</span><span class="p">),</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">test</span><span class="p">),</span>
<span class="n">transformation</span><span class="p">)</span>
<span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">pool</span><span class="p">)</span>
<span class="k">for</span> <span class="n">tmp</span> <span class="ow">in</span> <span class="n">results</span><span class="p">:</span>
<span class="k">if</span> <span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">objs</span><span class="p">:</span>
<span class="n">objs</span><span class="p">[</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;obj&#39;</span><span class="p">]</span>
<span class="n">rmse</span><span class="p">[</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">smape</span><span class="p">[</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">u</span><span class="p">[</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">times</span><span class="p">[</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">rmse</span><span class="p">[</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;rmse&#39;</span><span class="p">])</span>
<span class="n">smape</span><span class="p">[</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;smape&#39;</span><span class="p">])</span>
<span class="n">u</span><span class="p">[</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;u&#39;</span><span class="p">])</span>
<span class="n">times</span><span class="p">[</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;time&#39;</span><span class="p">])</span>
<span class="n">_process_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Process End: {0: %H:%M:%S}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Process Duration: </span><span class="si">{0}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">_process_end</span> <span class="o">-</span> <span class="n">_process_start</span><span class="p">))</span>
<span class="k">return</span> <span class="n">Util</span><span class="o">.</span><span class="n">save_dataframe_point</span><span class="p">(</span><span class="n">experiments</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">objs</span><span class="p">,</span> <span class="n">rmse</span><span class="p">,</span> <span class="n">save</span><span class="p">,</span> <span class="n">sintetic</span><span class="p">,</span> <span class="n">smape</span><span class="p">,</span> <span class="n">times</span><span class="p">,</span> <span class="n">u</span><span class="p">)</span></div>
<div class="viewcode-block" id="run_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.parallel_benchmarks.run_interval">[docs]</a><span class="k">def</span> <span class="nf">run_interval</span><span class="p">(</span><span class="n">mfts</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">train_data</span><span class="p">,</span> <span class="n">test_data</span><span class="p">,</span> <span class="n">transformation</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">indexer</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Interval forecast benchmark function to be executed on threads</span>
<span class="sd"> :param mfts: FTS model</span>
<span class="sd"> :param partitioner: Universe of Discourse partitioner</span>
<span class="sd"> :param train_data: data used to train the model</span>
<span class="sd"> :param test_data: ata used to test the model</span>
<span class="sd"> :param window_key: id of the sliding window</span>
<span class="sd"> :param transformation: data transformation</span>
<span class="sd"> :param indexer: seasonal indexer</span>
<span class="sd"> :return: a dictionary with the benchmark results </span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">pttr</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">partitioner</span><span class="o">.</span><span class="vm">__module__</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">_key</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span> <span class="o">+</span> <span class="s2">&quot; n = &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span> <span class="o">+</span> <span class="n">pttr</span> <span class="o">+</span> <span class="s2">&quot; q = &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">partitioner</span><span class="o">.</span><span class="n">partitions</span><span class="p">)</span>
<span class="n">mfts</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="n">partitioner</span>
<span class="k">if</span> <span class="n">transformation</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">mfts</span><span class="o">.</span><span class="n">append_transformation</span><span class="p">(</span><span class="n">transformation</span><span class="p">)</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">mfts</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">train_data</span><span class="p">,</span> <span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
<span class="n">_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">times</span> <span class="o">=</span> <span class="n">_end</span> <span class="o">-</span> <span class="n">_start</span>
<span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">_sharp</span><span class="p">,</span> <span class="n">_res</span><span class="p">,</span> <span class="n">_cov</span> <span class="o">=</span> <span class="n">benchmarks</span><span class="o">.</span><span class="n">get_interval_statistics</span><span class="p">(</span><span class="n">test_data</span><span class="p">,</span> <span class="n">mfts</span><span class="p">)</span>
<span class="n">_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">times</span> <span class="o">+=</span> <span class="n">_end</span> <span class="o">-</span> <span class="n">_start</span>
<span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
<span class="n">_sharp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">_res</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">_cov</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">times</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">ret</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;key&#39;</span><span class="p">:</span> <span class="n">_key</span><span class="p">,</span> <span class="s1">&#39;obj&#39;</span><span class="p">:</span> <span class="n">mfts</span><span class="p">,</span> <span class="s1">&#39;sharpness&#39;</span><span class="p">:</span> <span class="n">_sharp</span><span class="p">,</span> <span class="s1">&#39;resolution&#39;</span><span class="p">:</span> <span class="n">_res</span><span class="p">,</span> <span class="s1">&#39;coverage&#39;</span><span class="p">:</span> <span class="n">_cov</span><span class="p">,</span> <span class="s1">&#39;time&#39;</span><span class="p">:</span> <span class="n">times</span><span class="p">}</span>
<span class="nb">print</span><span class="p">(</span><span class="n">ret</span><span class="p">)</span>
<span class="k">return</span> <span class="n">ret</span></div>
<div class="viewcode-block" id="interval_sliding_window"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.parallel_benchmarks.interval_sliding_window">[docs]</a><span class="k">def</span> <span class="nf">interval_sliding_window</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">windowsize</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">models</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">partitioners</span><span class="o">=</span><span class="p">[</span><span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">],</span>
<span class="n">partitions</span><span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">],</span> <span class="n">max_order</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">transformation</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">indexer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dump</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">save</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sintetic</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Parallel sliding window benchmarks for FTS point_to_interval forecasters</span>
<span class="sd"> :param data: </span>
<span class="sd"> :param windowsize: size of sliding window</span>
<span class="sd"> :param train: percentual of sliding window data used to train the models</span>
<span class="sd"> :param models: FTS point forecasters</span>
<span class="sd"> :param partitioners: Universe of Discourse partitioner</span>
<span class="sd"> :param partitions: the max number of partitions on the Universe of Discourse </span>
<span class="sd"> :param max_order: the max order of the models (for high order models)</span>
<span class="sd"> :param transformation: data transformation</span>
<span class="sd"> :param indexer: seasonal indexer</span>
<span class="sd"> :param dump: </span>
<span class="sd"> :param save: save results</span>
<span class="sd"> :param file: file path to save the results</span>
<span class="sd"> :param sintetic: if true only the average and standard deviation of the results</span>
<span class="sd"> :return: DataFrame with the results</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">_process_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Process Start: {0: %H:%M:%S}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()))</span>
<span class="n">num_cores</span> <span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">cpu_count</span><span class="p">()</span>
<span class="n">pool</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">objs</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">sharpness</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">resolution</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">coverage</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">times</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">benchmarks</span><span class="o">.</span><span class="n">get_interval_methods</span><span class="p">():</span>
<span class="n">mfts</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
<span class="k">if</span> <span class="n">mfts</span><span class="o">.</span><span class="n">is_high_order</span><span class="p">:</span>
<span class="k">for</span> <span class="n">order</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_order</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
<span class="k">if</span> <span class="n">order</span> <span class="o">&gt;=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">min_order</span><span class="p">:</span>
<span class="n">mfts</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
<span class="n">mfts</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">order</span>
<span class="n">pool</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mfts</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">pool</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mfts</span><span class="p">)</span>
<span class="n">experiments</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="ow">in</span> <span class="n">Util</span><span class="o">.</span><span class="n">sliding_window</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">windowsize</span><span class="p">,</span> <span class="n">train</span><span class="p">):</span>
<span class="n">experiments</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">if</span> <span class="n">dump</span><span class="p">:</span> <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Window: </span><span class="si">{0}</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ct</span><span class="p">))</span>
<span class="k">for</span> <span class="n">partition</span> <span class="ow">in</span> <span class="n">partitions</span><span class="p">:</span>
<span class="k">for</span> <span class="n">partitioner</span> <span class="ow">in</span> <span class="n">partitioners</span><span class="p">:</span>
<span class="n">data_train_fs</span> <span class="o">=</span> <span class="n">partitioner</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">partition</span><span class="p">,</span> <span class="n">transformation</span><span class="o">=</span><span class="n">transformation</span><span class="p">)</span>
<span class="n">results</span> <span class="o">=</span> <span class="n">Parallel</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="n">num_cores</span><span class="p">)(</span>
<span class="n">delayed</span><span class="p">(</span><span class="n">run_interval</span><span class="p">)(</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">m</span><span class="p">),</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">data_train_fs</span><span class="p">),</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">train</span><span class="p">),</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">test</span><span class="p">),</span>
<span class="n">transformation</span><span class="p">)</span>
<span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">pool</span><span class="p">)</span>
<span class="k">for</span> <span class="n">tmp</span> <span class="ow">in</span> <span class="n">results</span><span class="p">:</span>
<span class="k">if</span> <span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">objs</span><span class="p">:</span>
<span class="n">objs</span><span class="p">[</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;obj&#39;</span><span class="p">]</span>
<span class="n">sharpness</span><span class="p">[</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">resolution</span><span class="p">[</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">coverage</span><span class="p">[</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">times</span><span class="p">[</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">sharpness</span><span class="p">[</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;sharpness&#39;</span><span class="p">])</span>
<span class="n">resolution</span><span class="p">[</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;resolution&#39;</span><span class="p">])</span>
<span class="n">coverage</span><span class="p">[</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;coverage&#39;</span><span class="p">])</span>
<span class="n">times</span><span class="p">[</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;time&#39;</span><span class="p">])</span>
<span class="n">_process_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Process End: {0: %H:%M:%S}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Process Duration: </span><span class="si">{0}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">_process_end</span> <span class="o">-</span> <span class="n">_process_start</span><span class="p">))</span>
<span class="k">return</span> <span class="n">Util</span><span class="o">.</span><span class="n">save_dataframe_interval</span><span class="p">(</span><span class="n">coverage</span><span class="p">,</span> <span class="n">experiments</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">objs</span><span class="p">,</span> <span class="n">resolution</span><span class="p">,</span> <span class="n">save</span><span class="p">,</span> <span class="n">sharpness</span><span class="p">,</span> <span class="n">sintetic</span><span class="p">,</span> <span class="n">times</span><span class="p">)</span></div>
<div class="viewcode-block" id="run_ahead"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.parallel_benchmarks.run_ahead">[docs]</a><span class="k">def</span> <span class="nf">run_ahead</span><span class="p">(</span><span class="n">mfts</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">train_data</span><span class="p">,</span> <span class="n">test_data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="n">resolution</span><span class="p">,</span> <span class="n">transformation</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">indexer</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Probabilistic m-step ahead forecast benchmark function to be executed on threads</span>
<span class="sd"> :param mfts: FTS model</span>
<span class="sd"> :param partitioner: Universe of Discourse partitioner</span>
<span class="sd"> :param train_data: data used to train the model</span>
<span class="sd"> :param test_data: ata used to test the model </span>
<span class="sd"> :param steps: </span>
<span class="sd"> :param resolution: </span>
<span class="sd"> :param transformation: data transformation</span>
<span class="sd"> :param indexer: seasonal indexer</span>
<span class="sd"> :return: a dictionary with the benchmark results </span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">pttr</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">partitioner</span><span class="o">.</span><span class="vm">__module__</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">_key</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span> <span class="o">+</span> <span class="s2">&quot; n = &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span> <span class="o">+</span> <span class="n">pttr</span> <span class="o">+</span> <span class="s2">&quot; q = &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">partitioner</span><span class="o">.</span><span class="n">partitions</span><span class="p">)</span>
<span class="n">mfts</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="n">partitioner</span>
<span class="k">if</span> <span class="n">transformation</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">mfts</span><span class="o">.</span><span class="n">append_transformation</span><span class="p">(</span><span class="n">transformation</span><span class="p">)</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">mfts</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">train_data</span><span class="p">,</span> <span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
<span class="n">_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">times</span> <span class="o">=</span> <span class="n">_end</span> <span class="o">-</span> <span class="n">_start</span>
<span class="n">_crps1</span><span class="p">,</span> <span class="n">_crps2</span><span class="p">,</span> <span class="n">_t1</span><span class="p">,</span> <span class="n">_t2</span> <span class="o">=</span> <span class="n">benchmarks</span><span class="o">.</span><span class="n">get_distribution_statistics</span><span class="p">(</span><span class="n">test_data</span><span class="p">,</span> <span class="n">mfts</span><span class="p">,</span> <span class="n">steps</span><span class="o">=</span><span class="n">steps</span><span class="p">,</span>
<span class="n">resolution</span><span class="o">=</span><span class="n">resolution</span><span class="p">)</span>
<span class="n">_t1</span> <span class="o">+=</span> <span class="n">times</span>
<span class="n">_t2</span> <span class="o">+=</span> <span class="n">times</span>
<span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
<span class="n">_crps1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">_crps2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">_t1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">_t2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="n">ret</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;key&#39;</span><span class="p">:</span> <span class="n">_key</span><span class="p">,</span> <span class="s1">&#39;obj&#39;</span><span class="p">:</span> <span class="n">mfts</span><span class="p">,</span> <span class="s1">&#39;CRPS_Interval&#39;</span><span class="p">:</span> <span class="n">_crps1</span><span class="p">,</span> <span class="s1">&#39;CRPS_Distribution&#39;</span><span class="p">:</span> <span class="n">_crps2</span><span class="p">,</span> <span class="s1">&#39;TIME_Interval&#39;</span><span class="p">:</span> <span class="n">_t1</span><span class="p">,</span> <span class="s1">&#39;TIME_Distribution&#39;</span><span class="p">:</span> <span class="n">_t2</span><span class="p">}</span>
<span class="nb">print</span><span class="p">(</span><span class="n">ret</span><span class="p">)</span>
<span class="k">return</span> <span class="n">ret</span></div>
<div class="viewcode-block" id="ahead_sliding_window"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.parallel_benchmarks.ahead_sliding_window">[docs]</a><span class="k">def</span> <span class="nf">ahead_sliding_window</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">windowsize</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span><span class="n">resolution</span><span class="p">,</span> <span class="n">models</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">partitioners</span><span class="o">=</span><span class="p">[</span><span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">],</span>
<span class="n">partitions</span><span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">],</span> <span class="n">max_order</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">transformation</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">indexer</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dump</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">save</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">sintetic</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Parallel sliding window benchmarks for FTS probabilistic forecasters</span>
<span class="sd"> :param data: </span>
<span class="sd"> :param windowsize: size of sliding window</span>
<span class="sd"> :param train: percentual of sliding window data used to train the models</span>
<span class="sd"> :param steps: </span>
<span class="sd"> :param resolution: </span>
<span class="sd"> :param models: FTS point forecasters</span>
<span class="sd"> :param partitioners: Universe of Discourse partitioner</span>
<span class="sd"> :param partitions: the max number of partitions on the Universe of Discourse </span>
<span class="sd"> :param max_order: the max order of the models (for high order models)</span>
<span class="sd"> :param transformation: data transformation</span>
<span class="sd"> :param indexer: seasonal indexer</span>
<span class="sd"> :param dump: </span>
<span class="sd"> :param save: save results</span>
<span class="sd"> :param file: file path to save the results</span>
<span class="sd"> :param sintetic: if true only the average and standard deviation of the results</span>
<span class="sd"> :return: DataFrame with the results </span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">_process_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Process Start: {0: %H:%M:%S}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()))</span>
<span class="n">num_cores</span> <span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">cpu_count</span><span class="p">()</span>
<span class="n">pool</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">objs</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">crps_interval</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">crps_distr</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">times1</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">times2</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">benchmarks</span><span class="o">.</span><span class="n">get_interval_methods</span><span class="p">():</span>
<span class="n">mfts</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
<span class="k">if</span> <span class="n">mfts</span><span class="o">.</span><span class="n">is_high_order</span><span class="p">:</span>
<span class="k">for</span> <span class="n">order</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_order</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
<span class="k">if</span> <span class="n">order</span> <span class="o">&gt;=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">min_order</span><span class="p">:</span>
<span class="n">mfts</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
<span class="n">mfts</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">order</span>
<span class="n">pool</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mfts</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">pool</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mfts</span><span class="p">)</span>
<span class="n">experiments</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="ow">in</span> <span class="n">Util</span><span class="o">.</span><span class="n">sliding_window</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">windowsize</span><span class="p">,</span> <span class="n">train</span><span class="p">):</span>
<span class="n">experiments</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">if</span> <span class="n">dump</span><span class="p">:</span> <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Window: </span><span class="si">{0}</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ct</span><span class="p">))</span>
<span class="k">for</span> <span class="n">partition</span> <span class="ow">in</span> <span class="n">partitions</span><span class="p">:</span>
<span class="k">for</span> <span class="n">partitioner</span> <span class="ow">in</span> <span class="n">partitioners</span><span class="p">:</span>
<span class="n">data_train_fs</span> <span class="o">=</span> <span class="n">partitioner</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">partition</span><span class="p">,</span> <span class="n">transformation</span><span class="o">=</span><span class="n">transformation</span><span class="p">)</span>
<span class="n">results</span> <span class="o">=</span> <span class="n">Parallel</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="n">num_cores</span><span class="p">)(</span>
<span class="n">delayed</span><span class="p">(</span><span class="n">run_ahead</span><span class="p">)(</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">m</span><span class="p">),</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">data_train_fs</span><span class="p">),</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">train</span><span class="p">),</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">test</span><span class="p">),</span>
<span class="n">steps</span><span class="p">,</span> <span class="n">resolution</span><span class="p">,</span> <span class="n">transformation</span><span class="p">)</span>
<span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">pool</span><span class="p">)</span>
<span class="k">for</span> <span class="n">tmp</span> <span class="ow">in</span> <span class="n">results</span><span class="p">:</span>
<span class="k">if</span> <span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">objs</span><span class="p">:</span>
<span class="n">objs</span><span class="p">[</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;obj&#39;</span><span class="p">]</span>
<span class="n">crps_interval</span><span class="p">[</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">crps_distr</span><span class="p">[</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">times1</span><span class="p">[</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">times2</span><span class="p">[</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">crps_interval</span><span class="p">[</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;CRPS_Interval&#39;</span><span class="p">])</span>
<span class="n">crps_distr</span><span class="p">[</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;CRPS_Distribution&#39;</span><span class="p">])</span>
<span class="n">times1</span><span class="p">[</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;TIME_Interval&#39;</span><span class="p">])</span>
<span class="n">times2</span><span class="p">[</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">tmp</span><span class="p">[</span><span class="s1">&#39;TIME_Distribution&#39;</span><span class="p">])</span>
<span class="n">_process_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Process End: {0: %H:%M:%S}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Process Duration: </span><span class="si">{0}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">_process_end</span> <span class="o">-</span> <span class="n">_process_start</span><span class="p">))</span>
<span class="k">return</span> <span class="n">Util</span><span class="o">.</span><span class="n">save_dataframe_ahead</span><span class="p">(</span><span class="n">experiments</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">objs</span><span class="p">,</span> <span class="n">crps_interval</span><span class="p">,</span> <span class="n">crps_distr</span><span class="p">,</span> <span class="n">times1</span><span class="p">,</span> <span class="n">times2</span><span class="p">,</span> <span class="n">save</span><span class="p">,</span> <span class="n">sintetic</span><span class="p">)</span></div>
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@ -95,43 +125,24 @@
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101
docs/_build/html/_modules/pyFTS/common/FLR.html vendored
View File

@ -1,29 +1,59 @@
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <!doctype html>
<html xmlns="http://www.w3.org/1999/xhtml"> <html xmlns="http://www.w3.org/1999/xhtml">
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<meta http-equiv="X-UA-Compatible" content="IE=Edge" /> <meta http-equiv="X-UA-Compatible" content="IE=Edge" />
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<link rel="index" title="Index" href="../../../genindex.html" /> <link rel="index" title="Index" href="../../../genindex.html" />
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>modules</a> |</li>
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@ -154,43 +184,24 @@
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101
docs/_build/html/_modules/pyFTS/common/FuzzySet.html vendored
View File

@ -1,29 +1,59 @@
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <!doctype html>
<html xmlns="http://www.w3.org/1999/xhtml"> <html xmlns="http://www.w3.org/1999/xhtml">
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<meta http-equiv="X-UA-Compatible" content="IE=Edge" /> <meta http-equiv="X-UA-Compatible" content="IE=Edge" />
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<title>pyFTS.common.FuzzySet &#8212; pyFTS 1.2.3 documentation</title> <title>pyFTS.common.FuzzySet &#8212; pyFTS 1.2.3 documentation</title>
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<script type="text/javascript" src="../../../_static/bizstyle.js"></script>
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@ -232,43 +262,24 @@
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101
docs/_build/html/_modules/pyFTS/common/Membership.html vendored
View File

@ -1,29 +1,59 @@
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <!doctype html>
<html xmlns="http://www.w3.org/1999/xhtml"> <html xmlns="http://www.w3.org/1999/xhtml">
<head> <head>
<meta http-equiv="X-UA-Compatible" content="IE=Edge" /> <meta http-equiv="X-UA-Compatible" content="IE=Edge" />
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<title>pyFTS.common.Membership &#8212; pyFTS 1.2.3 documentation</title> <title>pyFTS.common.Membership &#8212; pyFTS 1.2.3 documentation</title>
<link rel="stylesheet" href="../../../_static/alabaster.css" type="text/css" /> <link rel="stylesheet" href="../../../_static/bizstyle.css" type="text/css" />
<link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" /> <link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
<script type="text/javascript" src="../../../_static/documentation_options.js"></script> <script type="text/javascript" src="../../../_static/documentation_options.js"></script>
<script type="text/javascript" src="../../../_static/jquery.js"></script> <script type="text/javascript" src="../../../_static/jquery.js"></script>
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<script type="text/javascript" src="../../../_static/bizstyle.js"></script>
<link rel="index" title="Index" href="../../../genindex.html" /> <link rel="index" title="Index" href="../../../genindex.html" />
<link rel="search" title="Search" href="../../../search.html" /> <link rel="search" title="Search" href="../../../search.html" />
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<a href="../../../py-modindex.html" title="Python Module Index"
>modules</a> |</li>
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<form class="search" action="../../../search.html" method="get">
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@ -115,43 +145,24 @@
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<script type="text/javascript">$('#searchbox').show(0);</script>
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 1.7.2.
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</body> </body>
</html> </html>

101
docs/_build/html/_modules/pyFTS/common/SortedCollection.html vendored
View File

@ -1,29 +1,59 @@
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <!doctype html>
<html xmlns="http://www.w3.org/1999/xhtml"> <html xmlns="http://www.w3.org/1999/xhtml">
<head> <head>
<meta http-equiv="X-UA-Compatible" content="IE=Edge" /> <meta http-equiv="X-UA-Compatible" content="IE=Edge" />
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<title>pyFTS.common.SortedCollection &#8212; pyFTS 1.2.3 documentation</title> <title>pyFTS.common.SortedCollection &#8212; pyFTS 1.2.3 documentation</title>
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101
docs/_build/html/_modules/pyFTS/common/Transformations.html vendored
View File

@ -1,29 +1,59 @@
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <!doctype html>
<html xmlns="http://www.w3.org/1999/xhtml"> <html xmlns="http://www.w3.org/1999/xhtml">
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>modules</a> |</li>
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@ -271,43 +301,24 @@
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101
docs/_build/html/_modules/pyFTS/common/Util.html vendored
View File

@ -1,29 +1,59 @@
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <!doctype html>
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101
docs/_build/html/_modules/pyFTS/common/flrg.html vendored
View File

@ -1,29 +1,59 @@
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
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<html xmlns="http://www.w3.org/1999/xhtml"> <html xmlns="http://www.w3.org/1999/xhtml">
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<meta http-equiv="X-UA-Compatible" content="IE=Edge" /> <meta http-equiv="X-UA-Compatible" content="IE=Edge" />
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<title>pyFTS.common.flrg &#8212; pyFTS 1.2.3 documentation</title> <title>pyFTS.common.flrg &#8212; pyFTS 1.2.3 documentation</title>
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</html> </html>

101
docs/_build/html/_modules/pyFTS/common/fts.html vendored
View File

@ -1,29 +1,59 @@
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
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101
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@ -1,29 +1,59 @@
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109
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View File

@ -1,29 +1,59 @@
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
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@ -31,7 +61,13 @@
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<h1>Source code for pyFTS.data.AirPassengers</h1><div class="highlight"><pre> <h1>Source code for pyFTS.data.AirPassengers</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="k">import</span> <span class="n">common</span> <span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Monthly totals of a airline passengers from USA, from January 1949 through December 1960.</span>
<span class="sd">Source: Hyndman, R.J., Time Series Data Library, http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="k">import</span> <span class="n">common</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@ -62,43 +98,24 @@
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107
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@ -1,29 +1,59 @@
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@ -31,7 +61,11 @@
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<h1>Source code for pyFTS.data.Enrollments</h1><div class="highlight"><pre> <h1>Source code for pyFTS.data.Enrollments</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="k">import</span> <span class="n">common</span> <span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Yearly University of Alabama enrollments from 1971 to 1992.</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="k">import</span> <span class="n">common</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@ -57,43 +91,24 @@
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View File

@ -1,29 +1,59 @@
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@ -59,43 +89,24 @@
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111
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@ -1,29 +1,59 @@
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@ -31,7 +61,15 @@
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<h1>Source code for pyFTS.data.NASDAQ</h1><div class="highlight"><pre> <h1>Source code for pyFTS.data.NASDAQ</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="k">import</span> <span class="n">common</span> <span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">National Association of Securities Dealers Automated Quotations - Composite Index (NASDAQ IXIC)</span>
<span class="sd">Daily averaged index by business day, from 2000 to 2016.</span>
<span class="sd">Source: http://www.nasdaq.com/aspx/flashquotes.aspx?symbol=IXIC&amp;selected=IXIC</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="k">import</span> <span class="n">common</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@ -64,43 +102,24 @@
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101
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View File

@ -1,29 +1,59 @@
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
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@ -73,43 +103,24 @@
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@ -1,29 +1,59 @@
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@ -31,7 +61,15 @@
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<h1>Source code for pyFTS.data.SP500</h1><div class="highlight"><pre> <h1>Source code for pyFTS.data.SP500</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="k">import</span> <span class="n">common</span> <span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">S&amp;P500 - Standard &amp; Poor&#39;s 500</span>
<span class="sd">Daily averaged index, by business day, from 1950 to 2017.</span>
<span class="sd">Source: https://finance.yahoo.com/quote/%5EGSPC/history?p=%5EGSPC</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="k">import</span> <span class="n">common</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@ -62,43 +100,24 @@
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110
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View File

@ -1,29 +1,59 @@
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
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@ -31,7 +61,14 @@
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<h1>Source code for pyFTS.data.TAIEX</h1><div class="highlight"><pre> <h1>Source code for pyFTS.data.TAIEX</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="k">import</span> <span class="n">common</span> <span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">The Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX)</span>
<span class="sd">Daily averaged index by business day, from 1995 to 2014.</span>
<span class="sd">Source: http://www.twse.com.tw/en/products/indices/Index_Series.php</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="k">import</span> <span class="n">common</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@ -64,43 +101,24 @@
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101
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View File

@ -1,29 +1,59 @@
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
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@ -1,29 +1,59 @@
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<h1>Source code for pyFTS.data.henon</h1><div class="highlight"><pre> <h1>Source code for pyFTS.data.henon</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">M. Hénon. &quot;A two-dimensional mapping with a strange attractor&quot;. Commun. Math. Phys. 50, 69-77 (1976)</span>
<span class="sd">dx/dt = a + by(t-1) - x(t-1)^2</span>
<span class="sd">dy/dt = x</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<div class="viewcode-block" id="get_data"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.henon.get_data">[docs]</a><span class="k">def</span> <span class="nf">get_data</span><span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">a</span><span class="o">=</span><span class="mf">1.4</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">initial_values</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">1000</span><span class="p">):</span> <div class="viewcode-block" id="get_data"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.henon.get_data">[docs]</a><span class="k">def</span> <span class="nf">get_data</span><span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">a</span><span class="o">=</span><span class="mf">1.4</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">initial_values</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">1000</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Get a simple univariate time series data.</span>
<span class="sd"> :param var: the dataset field name to extract</span>
<span class="sd"> :return: numpy array</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">get_dataframe</span><span class="p">(</span><span class="n">a</span><span class="p">,</span><span class="n">b</span><span class="p">,</span> <span class="n">initial_values</span><span class="p">,</span> <span class="n">iterations</span><span class="p">)[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">values</span></div> <span class="k">return</span> <span class="n">get_dataframe</span><span class="p">(</span><span class="n">a</span><span class="p">,</span><span class="n">b</span><span class="p">,</span> <span class="n">initial_values</span><span class="p">,</span> <span class="n">iterations</span><span class="p">)[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">values</span></div>
<div class="viewcode-block" id="get_dataframe"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.henon.get_dataframe">[docs]</a><span class="k">def</span> <span class="nf">get_dataframe</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="mf">1.4</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">initial_values</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">1000</span><span class="p">):</span> <div class="viewcode-block" id="get_dataframe"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.henon.get_dataframe">[docs]</a><span class="k">def</span> <span class="nf">get_dataframe</span><span class="p">(</span><span class="n">a</span><span class="o">=</span><span class="mf">1.4</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">initial_values</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">1000</span><span class="p">):</span>
<span class="sd">&#39;&#39;&#39;</span> <span class="sd">&#39;&#39;&#39;</span>
<span class="sd"> M. Hénon. &quot;A two-dimensional mapping with a strange attractor&quot;. Commun. Math. Phys. 50, 69-77 (1976)</span> <span class="sd"> Return a dataframe with the bivariate Henon Map time series (x, y).</span>
<span class="sd"> dx/dt = a + by(t-1) - x(t-1)^2</span>
<span class="sd"> dy/dt = x</span>
<span class="sd"> :param a: Equation coefficient</span> <span class="sd"> :param a: Equation coefficient</span>
<span class="sd"> :param b: Equation coefficient</span> <span class="sd"> :param b: Equation coefficient</span>
@ -66,43 +106,24 @@
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<h1>Source code for pyFTS.data.logistic_map</h1><div class="highlight"><pre> <h1>Source code for pyFTS.data.logistic_map</h1><div class="highlight"><pre>
<span></span><span class="c1">#</span> <span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">May, Robert M. (1976). &quot;Simple mathematical models with very complicated dynamics&quot;.</span>
<span class="sd">Nature. 261 (5560): 459–467. doi:10.1038/261459a0.</span>
<span class="sd">x(t) = r * x(t-1) * (1 - x(t -1) )</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<div class="viewcode-block" id="get_data"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.logistic_map.get_data">[docs]</a><span class="k">def</span> <span class="nf">get_data</span><span class="p">(</span><span class="n">r</span> <span class="o">=</span> <span class="mi">4</span><span class="p">,</span> <span class="n">initial_value</span> <span class="o">=</span> <span class="mf">0.3</span><span class="p">,</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">100</span><span class="p">):</span> <div class="viewcode-block" id="get_data"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.logistic_map.get_data">[docs]</a><span class="k">def</span> <span class="nf">get_data</span><span class="p">(</span><span class="n">r</span> <span class="o">=</span> <span class="mi">4</span><span class="p">,</span> <span class="n">initial_value</span> <span class="o">=</span> <span class="mf">0.3</span><span class="p">,</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">100</span><span class="p">):</span>
<span class="sd">&#39;&#39;&#39;</span> <span class="sd">&#39;&#39;&#39;</span>
<span class="sd"> May, Robert M. (1976). &quot;Simple mathematical models with very complicated dynamics&quot;.</span> <span class="sd"> Return a list with the logistic map chaotic time series.</span>
<span class="sd"> Nature. 261 (5560): 459–467. doi:10.1038/261459a0.</span>
<span class="sd"> x(t) = r * x(t-1) * (1 - x(t -1) )</span>
<span class="sd"> :param r: Equation coefficient</span> <span class="sd"> :param r: Equation coefficient</span>
<span class="sd"> :param initial_value: Initial value of x. Default: 0.3</span> <span class="sd"> :param initial_value: Initial value of x. Default: 0.3</span>
@ -58,43 +90,24 @@
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<h1>Source code for pyFTS.data.lorentz</h1><div class="highlight"><pre> <h1>Source code for pyFTS.data.lorentz</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Lorenz, Edward Norton (1963). &quot;Deterministic nonperiodic flow&quot;. Journal of the Atmospheric Sciences. 20 (2): 130–141.</span>
<span class="sd">https://doi.org/10.1175/1520-0469(1963)020&lt;0130:DNF&gt;2.0.CO;2</span>
<span class="sd">dx/dt = a(y -x)</span>
<span class="sd">dy/dt = x(b - z) - y</span>
<span class="sd">dz/dt = xy - cz</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<div class="viewcode-block" id="get_data"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.lorentz.get_data">[docs]</a><span class="k">def</span> <span class="nf">get_data</span><span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">a</span> <span class="o">=</span> <span class="mf">10.0</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="mf">28.0</span><span class="p">,</span> <span class="n">c</span> <span class="o">=</span> <span class="mf">8.0</span> <span class="o">/</span> <span class="mf">3.0</span><span class="p">,</span> <span class="n">dt</span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">,</span> <div class="viewcode-block" id="get_data"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.lorentz.get_data">[docs]</a><span class="k">def</span> <span class="nf">get_data</span><span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">a</span> <span class="o">=</span> <span class="mf">10.0</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="mf">28.0</span><span class="p">,</span> <span class="n">c</span> <span class="o">=</span> <span class="mf">8.0</span> <span class="o">/</span> <span class="mf">3.0</span><span class="p">,</span> <span class="n">dt</span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">,</span>
<span class="n">initial_values</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">1000</span><span class="p">):</span> <span class="n">initial_values</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">1000</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Get a simple univariate time series data.</span>
<span class="sd"> :param var: the dataset field name to extract</span>
<span class="sd"> :return: numpy array</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">get_dataframe</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">initial_values</span><span class="p">,</span> <span class="n">iterations</span><span class="p">)[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">values</span></div> <span class="k">return</span> <span class="n">get_dataframe</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">initial_values</span><span class="p">,</span> <span class="n">iterations</span><span class="p">)[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">values</span></div>
<div class="viewcode-block" id="get_dataframe"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.lorentz.get_dataframe">[docs]</a><span class="k">def</span> <span class="nf">get_dataframe</span><span class="p">(</span><span class="n">a</span> <span class="o">=</span> <span class="mf">10.0</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="mf">28.0</span><span class="p">,</span> <span class="n">c</span> <span class="o">=</span> <span class="mf">8.0</span> <span class="o">/</span> <span class="mf">3.0</span><span class="p">,</span> <span class="n">dt</span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">,</span> <div class="viewcode-block" id="get_dataframe"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.lorentz.get_dataframe">[docs]</a><span class="k">def</span> <span class="nf">get_dataframe</span><span class="p">(</span><span class="n">a</span> <span class="o">=</span> <span class="mf">10.0</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="mf">28.0</span><span class="p">,</span> <span class="n">c</span> <span class="o">=</span> <span class="mf">8.0</span> <span class="o">/</span> <span class="mf">3.0</span><span class="p">,</span> <span class="n">dt</span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">,</span>
<span class="n">initial_values</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">1000</span><span class="p">):</span> <span class="n">initial_values</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">1000</span><span class="p">):</span>
<span class="sd">&#39;&#39;&#39;</span> <span class="sd">&#39;&#39;&#39;</span>
<span class="sd"> Lorenz, Edward Norton (1963). &quot;Deterministic nonperiodic flow&quot;. Journal of the Atmospheric Sciences. 20 (2): 130–141.</span> <span class="sd"> Return a dataframe with the multivariate Lorenz Map time series (x, y, z).</span>
<span class="sd"> https://doi.org/10.1175/1520-0469(1963)020&lt;0130:DNF&gt;2.0.CO;2</span>
<span class="sd"> dx/dt = a(y -x)</span>
<span class="sd"> dy/dt = x(b - z) - y</span>
<span class="sd"> dz/dt = xy - cz</span>
<span class="sd"> :param a: Equation coefficient. Default value: 10</span> <span class="sd"> :param a: Equation coefficient. Default value: 10</span>
<span class="sd"> :param b: Equation coefficient. Default value: 28</span> <span class="sd"> :param b: Equation coefficient. Default value: 28</span>
@ -77,43 +117,24 @@
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<h1>Source code for pyFTS.data.mackey_glass</h1><div class="highlight"><pre> <h1>Source code for pyFTS.data.mackey_glass</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Mackey, M. C. and Glass, L. (1977). Oscillation and chaos in physiological control systems.</span>
<span class="sd">Science, 197(4300):287-289.</span>
<span class="sd">dy/dt = -by(t)+ cy(t - tau) / 1+y(t-tau)^10</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<div class="viewcode-block" id="get_data"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.mackey_glass.get_data">[docs]</a><span class="k">def</span> <span class="nf">get_data</span><span class="p">(</span><span class="n">b</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">tau</span><span class="o">=</span><span class="mi">17</span><span class="p">,</span> <span class="n">initial_values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">1.5</span><span class="p">,</span> <span class="mi">18</span><span class="p">),</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">1000</span><span class="p">):</span> <div class="viewcode-block" id="get_data"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.mackey_glass.get_data">[docs]</a><span class="k">def</span> <span class="nf">get_data</span><span class="p">(</span><span class="n">b</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">tau</span><span class="o">=</span><span class="mi">17</span><span class="p">,</span> <span class="n">initial_values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">1.5</span><span class="p">,</span> <span class="mi">18</span><span class="p">),</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">1000</span><span class="p">):</span>
<span class="sd">&#39;&#39;&#39;</span> <span class="sd">&#39;&#39;&#39;</span>
<span class="sd"> Mackey, M. C. and Glass, L. (1977). Oscillation and chaos in physiological control systems.</span> <span class="sd"> Return a list with the Mackey-Glass chaotic time series.</span>
<span class="sd"> Science, 197(4300):287-289.</span>
<span class="sd"> dy/dt = -by(t)+ cy(t - tau) / 1+y(t-tau)^10</span>
<span class="sd"> :param b: Equation coefficient</span> <span class="sd"> :param b: Equation coefficient</span>
<span class="sd"> :param c: Equation coefficient</span> <span class="sd"> :param c: Equation coefficient</span>
@ -59,43 +93,24 @@
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<h1>Source code for pyFTS.data.rossler</h1><div class="highlight"><pre> <h1>Source code for pyFTS.data.rossler</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">O. E. Rössler, Phys. Lett. 57A, 397 (1976).</span>
<span class="sd">dx/dt = -z - y</span>
<span class="sd">dy/dt = x + ay</span>
<span class="sd">dz/dt = b + z( x - c )</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<div class="viewcode-block" id="get_data"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.rossler.get_data">[docs]</a><span class="k">def</span> <span class="nf">get_data</span><span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">a</span> <span class="o">=</span> <span class="mf">0.2</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="mf">0.2</span><span class="p">,</span> <span class="n">c</span> <span class="o">=</span> <span class="mf">5.7</span><span class="p">,</span> <span class="n">dt</span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">,</span> <div class="viewcode-block" id="get_data"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.rossler.get_data">[docs]</a><span class="k">def</span> <span class="nf">get_data</span><span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">a</span> <span class="o">=</span> <span class="mf">0.2</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="mf">0.2</span><span class="p">,</span> <span class="n">c</span> <span class="o">=</span> <span class="mf">5.7</span><span class="p">,</span> <span class="n">dt</span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">,</span>
<span class="n">initial_values</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.001</span><span class="p">],</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">5000</span><span class="p">):</span> <span class="n">initial_values</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.001</span><span class="p">],</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">5000</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Get a simple univariate time series data.</span>
<span class="sd"> :param var: the dataset field name to extract</span>
<span class="sd"> :return: numpy array</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">return</span> <span class="n">get_dataframe</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">initial_values</span><span class="p">,</span> <span class="n">iterations</span><span class="p">)[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">values</span></div> <span class="k">return</span> <span class="n">get_dataframe</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">initial_values</span><span class="p">,</span> <span class="n">iterations</span><span class="p">)[</span><span class="n">var</span><span class="p">]</span><span class="o">.</span><span class="n">values</span></div>
<div class="viewcode-block" id="get_dataframe"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.rossler.get_dataframe">[docs]</a><span class="k">def</span> <span class="nf">get_dataframe</span><span class="p">(</span><span class="n">a</span> <span class="o">=</span> <span class="mf">0.2</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="mf">0.2</span><span class="p">,</span> <span class="n">c</span> <span class="o">=</span> <span class="mf">5.7</span><span class="p">,</span> <span class="n">dt</span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">,</span> <div class="viewcode-block" id="get_dataframe"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.rossler.get_dataframe">[docs]</a><span class="k">def</span> <span class="nf">get_dataframe</span><span class="p">(</span><span class="n">a</span> <span class="o">=</span> <span class="mf">0.2</span><span class="p">,</span> <span class="n">b</span> <span class="o">=</span> <span class="mf">0.2</span><span class="p">,</span> <span class="n">c</span> <span class="o">=</span> <span class="mf">5.7</span><span class="p">,</span> <span class="n">dt</span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">,</span>
<span class="n">initial_values</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.001</span><span class="p">],</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">5000</span><span class="p">):</span> <span class="n">initial_values</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.001</span><span class="p">],</span> <span class="n">iterations</span><span class="o">=</span><span class="mi">5000</span><span class="p">):</span>
<span class="sd">&#39;&#39;&#39;</span> <span class="sd">&#39;&#39;&#39;</span>
<span class="sd"> O. E. Rössler, Phys. Lett. 57A, 397 (1976).</span> <span class="sd"> Return a dataframe with the multivariate Rössler Map time series (x, y, z).</span>
<span class="sd"> dx/dt = -z - y</span>
<span class="sd"> dy/dt = x + ay</span>
<span class="sd"> dz/dt = b + z( x - c )</span>
<span class="sd"> :param a: Equation coefficient. Default value: 0.2</span> <span class="sd"> :param a: Equation coefficient. Default value: 0.2</span>
<span class="sd"> :param b: Equation coefficient. Default value: 0.2</span> <span class="sd"> :param b: Equation coefficient. Default value: 0.2</span>
@ -76,43 +117,24 @@
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@ -1,29 +1,59 @@
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@ -31,7 +61,13 @@
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<h1>Source code for pyFTS.data.sunspots</h1><div class="highlight"><pre> <h1>Source code for pyFTS.data.sunspots</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="k">import</span> <span class="n">common</span> <span></span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">Monthly sunspot numbers from 1749 to May 2016</span>
<span class="sd">Source: https://www.esrl.noaa.gov/psd/gcos_wgsp/Timeseries/SUNSPOT/</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="k">import</span> <span class="n">common</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@ -61,43 +97,24 @@
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101
docs/_build/html/_modules/pyFTS/models/cheng.html vendored
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@ -1,29 +1,59 @@
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
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101
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View File

@ -1,29 +1,59 @@
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
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101
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View File

@ -1,29 +1,59 @@
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
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101
docs/_build/html/_modules/pyFTS/models/hofts.html vendored
View File

@ -1,29 +1,59 @@
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101
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101
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@ -1,29 +1,59 @@
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101
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View File

@ -1,29 +1,59 @@
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101
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@ -1,29 +1,59 @@
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101
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101
docs/_build/html/_modules/pyFTS/partitioners/CMeans.html vendored
View File

@ -1,29 +1,59 @@
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <!doctype html>
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@ -133,43 +163,24 @@
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101
docs/_build/html/_modules/pyFTS/partitioners/Entropy.html vendored
View File

@ -1,29 +1,59 @@
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
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101
docs/_build/html/_modules/pyFTS/partitioners/FCM.html vendored
View File

@ -1,29 +1,59 @@
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <!doctype html>
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101
docs/_build/html/_modules/pyFTS/partitioners/Grid.html vendored
View File

@ -1,29 +1,59 @@
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <!doctype html>
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@ -1,29 +1,59 @@
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@ -1,29 +1,59 @@
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101
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View File

@ -1,29 +1,59 @@
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <!doctype html>
<html xmlns="http://www.w3.org/1999/xhtml"> <html xmlns="http://www.w3.org/1999/xhtml">
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>modules</a> |</li>
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<li class="nav-item nav-item-1"><a href="../../index.html" accesskey="U">Module code</a> &#187;</li>
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<div class="document"> <div class="document">
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@ -288,43 +318,24 @@
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101
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View File

@ -1,29 +1,59 @@
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <!doctype html>
<html xmlns="http://www.w3.org/1999/xhtml"> <html xmlns="http://www.w3.org/1999/xhtml">
<head> <head>
<meta http-equiv="X-UA-Compatible" content="IE=Edge" /> <meta http-equiv="X-UA-Compatible" content="IE=Edge" />
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<title>pyFTS.probabilistic.kde &#8212; pyFTS 1.2.3 documentation</title> <title>pyFTS.probabilistic.kde &#8212; pyFTS 1.2.3 documentation</title>
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<script type="text/javascript" src="../../../_static/bizstyle.js"></script>
<link rel="index" title="Index" href="../../../genindex.html" /> <link rel="index" title="Index" href="../../../genindex.html" />
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<meta name="viewport" content="width=device-width, initial-scale=0.9, maximum-scale=0.9" />
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<h3>Navigation</h3>
<ul>
<li class="right" style="margin-right: 10px">
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accesskey="I">index</a></li>
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<a href="../../../py-modindex.html" title="Python Module Index"
>modules</a> |</li>
<li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.2.3 documentation</a> &#187;</li>
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</a></p>
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<div class="searchformwrapper">
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@ -92,43 +122,24 @@
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58
docs/_build/html/_sources/index.rst.txt vendored
View File

@ -1,20 +1,66 @@
.. pyFTS documentation master file, created by .. pyFTS documentation master file, created by
sphinx-quickstart on Wed Aug 29 13:47:28 2018. sphinx-quickstart on Wed Aug 29 13:47:28 2018.
You can adapt this file completely to your liking, but it should at least You can adapt this file completely to your liking, but it should at least
contain the root `toctree` directive. contain the root `toctree` directive.
Welcome to pyFTS's documentation! Welcome to pyFTS's documentation!
================================= =================================
What is pyFTS Library?
----------------------
.. image:: https://badges.frapsoft.com/os/v2/open-source.png?v=103
.. image:: https://img.shields.io/badge/License-GPLv3-blue.svg
.. image:: https://img.shields.io/badge/Made%20with-Python-1f425f.svg
This package is intended for students, researchers, data scientists or whose want to exploit the Fuzzy Time Series methods. These methods provide simple, easy to use, computationally cheap and human-readable models, suitable from statistic laymans to experts.
This tool is developed on `MINDS Lab <http://www.minds.eng.ufmg.br/>`_,
headed by Prof. Frederico Gadelha Guimarães from `Electrical
Engineering Departament <https://www.ppgee.ufmg.br/>`_ of `Federal University
of Minas Gerais (UFMG) <https://ufmg.br/>`_ at Brazil. Also colaborate
with this tool the Brazilian instituitions `Federal Institute of
North of Minas Gerais (IFNMG) <http://www.ifnmg.edu.br>`_ and `Federal Institute of Minas Gerais
(IFMG) <http://www.ifmg.edu.br>`_ .
.. raw:: html
<center>
<table><tr><td> <a href="http://www.minds.eng.ufmg.br/"><img src="https://github.com/petroniocandido/pyFTS/raw/master/img/minds_logo_medium.jpeg" alt="MINDS - Machine Intelligence and Data Science Lab" width="100"/></a></td>
<td>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</td>
<td><a href="https://www.ufmg.br/"><img src="https://www.ufmg.br/marca/ass3.jpg" alt="UFMG" width="100"/></a></td>
<td>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</td>
<td><a href="https://www.ifnmg.edu.br/"><img
src="https://www.ifnmg.edu.br/arquivos/2016/reitoria/Not%C3%ADcias/ifnmg_vertical_jpg.jpg" alt="IFNMG" width="130"/></a></td>
<td>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</td>
<td><a href="https://www.ifmg.edu.br/"><img
src="http://www.ifmg.edu.br/portal/imagens/logovertical.jpg" alt="IFMG" width="100"/></a></td>
</tr>
</table>
</center>
.. toctree:: .. toctree::
:maxdepth: 2 :maxdepth: 3
:caption: Contents: :caption: API Documentation:
quickstart
modules
How to reference pyFTS?
-----------------------
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.1405817.svg
:target: https://doi.org/10.5281/zenodo.1405817
Silva, P. C. L. et al. *pyFTS: Fuzzy Time Series for Python (Version v4.0).* Belo Horizonte. 2018. DOI: 10.5281/zenodo.1405817. Url: <https://doi.org/10.5281/zenodo.1405817>
Indices and tables
================== Indexes
------------------
* :ref:`genindex` * :ref:`genindex`
* :ref:`modindex` * :ref:`modindex`
* :ref:`search` * :ref:`search`

47
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@ -1,9 +1,26 @@
pyFTS.benchmarks package pyFTS.benchmarks package
======================== ========================
Module contents
---------------
.. automodule:: pyFTS.benchmarks
:members:
:undoc-members:
:show-inheritance:
Submodules Submodules
---------- ----------
pyFTS.benchmarks.benchmarks module
----------------------------------
.. automodule:: pyFTS.benchmarks.benchmarks
:members:
:undoc-members:
:show-inheritance:
pyFTS.benchmarks.Measures module pyFTS.benchmarks.Measures module
-------------------------------- --------------------------------
@ -36,22 +53,6 @@ pyFTS.benchmarks.arima module
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
pyFTS.benchmarks.benchmarks module
----------------------------------
.. automodule:: pyFTS.benchmarks.benchmarks
:members:
:undoc-members:
:show-inheritance:
pyFTS.benchmarks.distributed\_benchmarks module
-----------------------------------------------
.. automodule:: pyFTS.benchmarks.distributed_benchmarks
:members:
:undoc-members:
:show-inheritance:
pyFTS.benchmarks.knn module pyFTS.benchmarks.knn module
--------------------------- ---------------------------
@ -68,14 +69,6 @@ pyFTS.benchmarks.naive module
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
pyFTS.benchmarks.parallel\_benchmarks module
--------------------------------------------
.. automodule:: pyFTS.benchmarks.parallel_benchmarks
:members:
:undoc-members:
:show-inheritance:
pyFTS.benchmarks.quantreg module pyFTS.benchmarks.quantreg module
-------------------------------- --------------------------------
@ -85,10 +78,4 @@ pyFTS.benchmarks.quantreg module
:show-inheritance: :show-inheritance:
Module contents
---------------
.. automodule:: pyFTS.benchmarks
:members:
:undoc-members:
:show-inheritance:

14
docs/_build/html/_sources/pyFTS.common.rst.txt vendored
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@ -1,6 +1,14 @@
pyFTS.common package pyFTS.common package
==================== ====================
Module contents
---------------
.. automodule:: pyFTS.common
:members:
:undoc-members:
:show-inheritance:
Submodules Submodules
---------- ----------
@ -85,10 +93,4 @@ pyFTS.common.tree module
:show-inheritance: :show-inheritance:
Module contents
---------------
.. automodule:: pyFTS.common
:members:
:undoc-members:
:show-inheritance:

14
docs/_build/html/_sources/pyFTS.data.rst.txt vendored
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@ -1,6 +1,14 @@
pyFTS.data package pyFTS.data package
================== ==================
Module contents
---------------
.. automodule:: pyFTS.data
:members:
:undoc-members:
:show-inheritance:
Submodules Submodules
---------- ----------
@ -125,10 +133,4 @@ pyFTS.data.sunspots module
:show-inheritance: :show-inheritance:
Module contents
---------------
.. automodule:: pyFTS.data
:members:
:undoc-members:
:show-inheritance:

47
docs/_build/html/_sources/pyFTS.models.rst.txt vendored
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@ -1,6 +1,15 @@
pyFTS.models package pyFTS.models package
==================== ====================
Module contents
---------------
.. automodule:: pyFTS.models
:members:
:undoc-members:
:show-inheritance:
Subpackages Subpackages
----------- -----------
@ -14,6 +23,14 @@ Subpackages
Submodules Submodules
---------- ----------
pyFTS.models.song module
------------------------
.. automodule:: pyFTS.models.song
:members:
:undoc-members:
:show-inheritance:
pyFTS.models.chen module pyFTS.models.chen module
------------------------ ------------------------
@ -22,6 +39,15 @@ pyFTS.models.chen module
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
pyFTS.models.yu module
----------------------
.. automodule:: pyFTS.models.yu
:members:
:undoc-members:
:show-inheritance:
pyFTS.models.cheng module pyFTS.models.cheng module
------------------------- -------------------------
@ -78,27 +104,6 @@ pyFTS.models.sadaei module
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
pyFTS.models.song module
------------------------
.. automodule:: pyFTS.models.song
:members:
:undoc-members:
:show-inheritance:
pyFTS.models.yu module
----------------------
.. automodule:: pyFTS.models.yu
:members:
:undoc-members:
:show-inheritance:
Module contents
---------------
.. automodule:: pyFTS.models
:members:
:undoc-members:
:show-inheritance:

68
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@ -1,29 +1,21 @@
pyFTS.partitioners package pyFTS.partitioners package
========================== ==========================
Module contents
---------------
.. automodule:: pyFTS.partitioners
:members:
:undoc-members:
:show-inheritance:
Submodules Submodules
---------- ----------
pyFTS.partitioners.CMeans module pyFTS.partitioners.partitioner module
-------------------------------- -------------------------------------
.. automodule:: pyFTS.partitioners.CMeans .. automodule:: pyFTS.partitioners.partitioner
:members:
:undoc-members:
:show-inheritance:
pyFTS.partitioners.Entropy module
---------------------------------
.. automodule:: pyFTS.partitioners.Entropy
:members:
:undoc-members:
:show-inheritance:
pyFTS.partitioners.FCM module
-----------------------------
.. automodule:: pyFTS.partitioners.FCM
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
@ -44,6 +36,30 @@ pyFTS.partitioners.Huarng module
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
pyFTS.partitioners.Entropy module
---------------------------------
.. automodule:: pyFTS.partitioners.Entropy
:members:
:undoc-members:
:show-inheritance:
pyFTS.partitioners.CMeans module
--------------------------------
.. automodule:: pyFTS.partitioners.CMeans
:members:
:undoc-members:
:show-inheritance:
pyFTS.partitioners.FCM module
-----------------------------
.. automodule:: pyFTS.partitioners.FCM
:members:
:undoc-members:
:show-inheritance:
pyFTS.partitioners.Util module pyFTS.partitioners.Util module
------------------------------ ------------------------------
@ -60,19 +76,7 @@ pyFTS.partitioners.parallel\_util module
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
pyFTS.partitioners.partitioner module
-------------------------------------
.. automodule:: pyFTS.partitioners.partitioner
:members:
:undoc-members:
:show-inheritance:
Module contents
---------------
.. automodule:: pyFTS.partitioners
:members:
:undoc-members:
:show-inheritance:

14
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pyFTS.probabilistic package pyFTS.probabilistic package
=========================== ===========================
Module contents
---------------
.. automodule:: pyFTS.probabilistic
:members:
:undoc-members:
:show-inheritance:
Submodules Submodules
---------- ----------
@ -21,10 +29,4 @@ pyFTS.probabilistic.kde module
:show-inheritance: :show-inheritance:
Module contents
---------------
.. automodule:: pyFTS.probabilistic
:members:
:undoc-members:
:show-inheritance:

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pyFTS Quick Start
=================
How to install pyFTS?
---------------------
.. image:: https://img.shields.io/badge/Made%20with-Python-1f425f.svg
Before of all, pyFTS was developed and tested with Python 3.6. To install pyFTS using pip tool
pip install -U pyFTS
Ou clone directly from the GitHub repo for the most recent review:
pip install -U git+https://github.com/PYFTS/pyFTS
What are Fuzzy Time Series (FTS)?
---------------------------------
Fuzzy Time Series (FTS) are non parametric methods for time series forecasting based on Fuzzy Theory. The original method was proposed by [1] and improved later by many researchers. The general approach of the FTS methods, based on [2] is listed below:
1. **Data preprocessing**: Data transformation functions contained at `pyFTS.common.Transformations <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/common/Transformations.py>`_, like differentiation, Box-Cox, scaling and normalization.
2. **Universe of Discourse Partitioning**: This is the most important step. Here, the range of values of the numerical time series *Y(t)* will be splited in overlapped intervals and for each interval will be created a Fuzzy Set. This step is performed by pyFTS.partition module and its classes (for instance GridPartitioner, EntropyPartitioner, etc). The main parameters are:
- the number of intervals
- which fuzzy membership function (on `pyFTS.common.Membership <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/common/Membership.py>`_)
- partition scheme (`GridPartitioner <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Grid.py>`_, `EntropyPartitioner <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Entropy.py>`_, `FCMPartitioner <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/FCM.py>`_, `CMeansPartitioner <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/CMeans.py>`_, `HuarngPartitioner <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Huarng.py>`_)
Check out the jupyter notebook on `pyFTS/notebooks/Partitioners.ipynb <https://github.com/PYFTS/pyFTS/blob/master/pyFTS/notebooks/Partitioners.ipynb>`_ for sample codes.
3. **Data Fuzzyfication**: Each data point of the numerical time series *Y(t)* will be translated to a fuzzy representation (usually one or more fuzzy sets), and then a fuzzy time series *F(t)* is created.
4. **Generation of Fuzzy Rules**: In this step the temporal transition rules are created. These rules depends on the method and their characteristics:
- *order*: the number of time lags used on forecasting
- *weights*: the weighted models introduce weights on fuzzy rules for smoothing
- *seasonality*: seasonality models
- *steps ahead*: the number of steps ahed to predict. Almost all standard methods are based on one-step-ahead forecasting
- *forecasting type*: Almost all standard methods are point-based, but pyFTS also provides intervalar and probabilistic forecasting methods.
5. **Forecasting**: The forecasting step takes a sample (with minimum length equal to the model's order) and generate a fuzzy outputs (fuzzy set(s)) for the next time ahead.
6. **Defuzzyfication**: This step transform the fuzzy forecast into a real number.
7. **Data postprocessing**: The inverse operations of step 1.
Usage examples
--------------
There is nothing better than good code examples to start. `Then check out the demo Jupyter Notebooks of the implemented method os pyFTS! <https://github.com/PYFTS/pyFTS/tree/master/pyFTS/notebooks>`_.
A Google Colab example can also be found `here <https://drive.google.com/file/d/1zRBCHXOawwgmzjEoKBgmvBqkIrKxuaz9/view?usp=sharing>`_.
References
----------
1. Q. Song and B. S. Chissom, “Fuzzy time series and its models,” Fuzzy Sets Syst., vol. 54, no. 3, pp. 269–277, 1993.
2. S.-M. Chen, “Forecasting enrollments based on fuzzy time series,” Fuzzy Sets Syst., vol. 81, no. 3, pp. 311–319, 1996.
3. C. H. Cheng, R. J. Chang, and C. A. Yeh, “Entropy-based and trapezoidal fuzzification-based fuzzy time series approach for forecasting IT project cost”. Technol. Forecast. Social Change, vol. 73, no. 5, pp. 524–542, Jun. 2006.
4. K. H. Huarng, “Effective lengths of intervals to improve forecasting in fuzzy time series”. Fuzzy Sets Syst., vol. 123, no. 3, pp. 387–394, Nov. 2001.
5. H.-K. Yu, “Weighted fuzzy time series models for TAIEX forecasting”. Phys. A Stat. Mech. its Appl., vol. 349, no. 3, pp. 609–624, 2005.
6. R. Efendi, Z. Ismail, and M. M. Deris, “Improved weight Fuzzy Time Series as used in the exchange rates forecasting of US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1, p. 1350005, 2013.
7. H. J. Sadaei, R. Enayatifar, A. H. Abdullah, and A. Gani, “Short-term load forecasting using a hybrid model with a refined exponentially weighted fuzzy time series and an improved harmony search,” Int. J. Electr. Power Energy Syst., vol. 62, no. from 2005, pp. 118–129, 2014.
8. C.-H. Cheng, Y.-S. Chen, and Y.-L. Wu, “Forecasting innovation diffusion of products using trend-weighted fuzzy time-series model,” Expert Syst. Appl., vol. 36, no. 2, pp. 1826–1832, 2009.

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