Incremental module
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@ -117,6 +117,7 @@
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<li><a href="pyFTS/models/hofts.html">pyFTS.models.hofts</a></li>
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<li><a href="pyFTS/models/hwang.html">pyFTS.models.hwang</a></li>
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<li><a href="pyFTS/models/ifts.html">pyFTS.models.ifts</a></li>
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<li><a href="pyFTS/models/incremental/Retrainer.html">pyFTS.models.incremental.Retrainer</a></li>
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<li><a href="pyFTS/models/ismailefendi.html">pyFTS.models.ismailefendi</a></li>
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<li><a href="pyFTS/models/multivariate/FLR.html">pyFTS.models.multivariate.FLR</a></li>
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<li><a href="pyFTS/models/multivariate/common.html">pyFTS.models.multivariate.common</a></li>
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@ -72,8 +72,14 @@
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<div class="body" role="main">
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<h1>Source code for pyFTS.models.ensemble.ensemble</h1><div class="highlight"><pre>
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<span></span><span class="ch">#!/usr/bin/python</span>
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<span class="c1"># -*- coding: utf8 -*-</span>
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<span></span><span class="sd">"""</span>
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<span class="sd">EnsembleFTS wraps several FTS methods to ensemble their forecasts, providing point,</span>
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<span class="sd">interval and probabilistic forecasting.</span>
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<span class="sd">Silva, P. C. L et al. Probabilistic Forecasting with Seasonal Ensemble Fuzzy Time-Series</span>
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<span class="sd">XIII Brazilian Congress on Computational Intelligence, 2017. Rio de Janeiro, Brazil.</span>
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<span class="sd">"""</span>
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<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
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@ -96,7 +102,7 @@
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<span class="sd"> """</span>
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<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
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<span class="nb">super</span><span class="p">(</span><span class="n">EnsembleFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">"Ensemble FTS"</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">"EnsembleFTS"</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"Ensemble FTS"</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">has_point_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
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@ -108,13 +114,16 @@
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<span class="bp">self</span><span class="o">.</span><span class="n">parameters</span> <span class="o">=</span> <span class="p">[]</span>
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<span class="sd">"""A list with the parameters for each component model"""</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"alpha"</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">)</span>
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<span class="sd">"""The quantiles """</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">point_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'point_method'</span><span class="p">,</span> <span class="s1">'mean'</span><span class="p">)</span>
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<span class="sd">"""The method used to mix the several model's forecasts into a unique point forecast. Options: mean, median, quantile"""</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">interval_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'interval_method'</span><span class="p">,</span> <span class="s1">'quantile'</span><span class="p">)</span>
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<span class="sd">"""The method used to mix the several model's forecasts into a interval forecast. Options: quantile, extremum, normal"""</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="mi">1</span>
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<div class="viewcode-block" id="EnsembleFTS.append_model"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.append_model">[docs]</a> <span class="k">def</span> <span class="nf">append_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">):</span>
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<span class="sd">"""</span>
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<span class="sd"> Append a new model to the ensemble</span>
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<span class="sd"> Append a new trained model to the ensemble</span>
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<span class="sd"> :param model: FTS model</span>
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@ -341,6 +350,9 @@
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<div class="viewcode-block" id="AllMethodEnsembleFTS"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS">[docs]</a><span class="k">class</span> <span class="nc">AllMethodEnsembleFTS</span><span class="p">(</span><span class="n">EnsembleFTS</span><span class="p">):</span>
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<span class="sd">"""</span>
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<span class="sd"> Creates an EnsembleFTS with all point forecast methods, sharing the same partitioner</span>
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<span class="sd"> """</span>
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<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
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<span class="nb">super</span><span class="p">(</span><span class="n">AllMethodEnsembleFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">3</span>
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@ -72,8 +72,10 @@
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<div class="body" role="main">
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<h1>Source code for pyFTS.models.ensemble.multiseasonal</h1><div class="highlight"><pre>
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<span></span><span class="ch">#!/usr/bin/python</span>
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<span class="c1"># -*- coding: utf8 -*-</span>
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<span></span><span class="sd">"""</span>
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<span class="sd">Silva, P. C. L et al. Probabilistic Forecasting with Seasonal Ensemble Fuzzy Time-Series</span>
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<span class="sd">XIII Brazilian Congress on Computational Intelligence, 2017. Rio de Janeiro, Brazil.</span>
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<span class="sd">"""</span>
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<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">Util</span> <span class="k">as</span> <span class="n">cUtil</span>
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<h1>Source code for pyFTS.models.incremental.Retrainer</h1><div class="highlight"><pre>
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<span></span><span class="sd">"""</span>
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<span class="sd">Meta model that wraps another FTS method and continously retrain it using a data window with the most recent data</span>
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<span class="sd">"""</span>
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<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">fts</span><span class="p">,</span> <span class="n">flrg</span>
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<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="k">import</span> <span class="n">Grid</span>
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<div class="viewcode-block" id="Retrainer"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.Retrainer.Retrainer">[docs]</a><span class="k">class</span> <span class="nc">Retrainer</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
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<span class="sd">"""</span>
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<span class="sd"> Meta model for incremental/online learning</span>
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<span class="sd"> """</span>
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<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
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<span class="nb">super</span><span class="p">(</span><span class="n">Retrainer</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">partitioner_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'partitioner_method'</span><span class="p">,</span> <span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">)</span>
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<span class="sd">"""The partitioner method to be called when a new model is build"""</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">partitioner_params</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'partitioner_params'</span><span class="p">,</span> <span class="p">{</span><span class="s1">'npart'</span><span class="p">:</span> <span class="mi">10</span><span class="p">})</span>
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<span class="sd">"""The partitioner method parameters"""</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="kc">None</span>
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<span class="sd">"""The most recent trained partitioner"""</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">fts_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'fts_method'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
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<span class="sd">"""The FTS method to be called when a new model is build"""</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">fts_params</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'fts_params'</span><span class="p">,</span> <span class="p">{})</span>
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<span class="sd">"""The FTS method specific parameters"""</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="kc">None</span>
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<span class="sd">"""The most recent trained model"""</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">window_length</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'window_length'</span><span class="p">,</span><span class="mi">100</span><span class="p">)</span>
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<span class="sd">"""The memory window length"""</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">auto_update</span> <span class="o">=</span> <span class="kc">False</span>
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<span class="sd">"""If true the model is updated at each time and not recreated"""</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">True</span>
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<div class="viewcode-block" id="Retrainer.train"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.Retrainer.Retrainer.train">[docs]</a> <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner_method</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner_params</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fts_method</span><span class="p">(</span><span class="n">partitioner</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">fts_params</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
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<div class="viewcode-block" id="Retrainer.forecast"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.Retrainer.Retrainer.forecast">[docs]</a> <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
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<span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
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<span class="n">horizon</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">window_length</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
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|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">k</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="n">horizon</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
|
||||
<span class="n">_train</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="n">horizon</span><span class="p">:</span> <span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">]</span>
|
||||
<span class="n">_test</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">auto_update</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">_train</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">_train</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">_test</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">))</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="sd">"""String representation of the model"""</span>
|
||||
|
||||
<span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> The length (number of rules) of the model</span>
|
||||
|
||||
<span class="sd"> :return: number of rules</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span></div>
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="clearer"></div>
|
||||
</div>
|
||||
<div class="related" role="navigation" aria-label="related navigation">
|
||||
<h3>Navigation</h3>
|
||||
<ul>
|
||||
<li class="right" style="margin-right: 10px">
|
||||
<a href="../../../../genindex.html" title="General Index"
|
||||
>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> »</li>
|
||||
<li class="nav-item nav-item-1"><a href="../../../index.html" >Module code</a> »</li>
|
||||
</ul>
|
||||
</div>
|
||||
<div class="footer" role="contentinfo">
|
||||
© Copyright 2018, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
|
||||
Created using <a href="http://sphinx-doc.org/">Sphinx</a> 1.7.2.
|
||||
</div>
|
||||
</body>
|
||||
</html>
|
||||
@ -77,7 +77,8 @@
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">FuzzySet</span>
|
||||
|
||||
<div class="viewcode-block" id="fuzzyfy_instance"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.common.fuzzyfy_instance">[docs]</a><span class="k">def</span> <span class="nf">fuzzyfy_instance</span><span class="p">(</span><span class="n">data_point</span><span class="p">,</span> <span class="n">var</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">data_point</span><span class="p">,</span> <span class="n">var</span><span class="o">.</span><span class="n">partitioner</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'sets'</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">'fuzzy'</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="o">=</span><span class="n">var</span><span class="o">.</span><span class="n">alpha_cut</span><span class="p">)</span></div>
|
||||
<span class="n">fsets</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">data_point</span><span class="p">,</span> <span class="n">var</span><span class="o">.</span><span class="n">partitioner</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'sets'</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">'fuzzy'</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="o">=</span><span class="n">var</span><span class="o">.</span><span class="n">alpha_cut</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="p">[(</span><span class="n">var</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">fs</span><span class="p">)</span> <span class="k">for</span> <span class="n">fs</span> <span class="ow">in</span> <span class="n">fsets</span><span class="p">]</span></div>
|
||||
|
||||
|
||||
|
||||
|
||||
@ -162,12 +162,17 @@
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">datepart</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">mf</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="n">centroid</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">FuzzySet</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">mf</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="n">centroid</span><span class="p">,</span> <span class="n">alpha</span><span class="p">,</span> <span class="nb">type</span> <span class="o">=</span> <span class="s1">'datetime'</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">FuzzySet</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">mf</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="n">centroid</span><span class="p">,</span> <span class="n">alpha</span><span class="p">,</span>
|
||||
<span class="nb">type</span><span class="o">=</span><span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'type'</span><span class="p">,</span> <span class="s1">'datetime'</span><span class="p">),</span>
|
||||
<span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">datepart</span> <span class="o">=</span> <span class="n">datepart</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">=</span> <span class="s1">'seasonal'</span>
|
||||
|
||||
<div class="viewcode-block" id="FuzzySet.membership"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.FuzzySet.membership">[docs]</a> <span class="k">def</span> <span class="nf">membership</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s1">'datetime'</span><span class="p">:</span>
|
||||
<span class="n">dp</span> <span class="o">=</span> <span class="n">strip_datepart</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">datepart</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">dp</span> <span class="o">=</span> <span class="n">x</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">mf</span><span class="p">(</span><span class="n">dp</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">parameters</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span></div></div>
|
||||
</pre></div>
|
||||
|
||||
|
||||
@ -138,6 +138,17 @@
|
||||
<span class="o">**</span><span class="n">kwargs</span><span class="p">))</span>
|
||||
<span class="n">tmp</span><span class="o">.</span><span class="n">centroid</span> <span class="o">=</span> <span class="n">c</span>
|
||||
<span class="n">sets</span><span class="p">[</span><span class="n">set_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span>
|
||||
<span class="k">elif</span> <span class="n">c</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">max</span> <span class="o">-</span> <span class="n">partlen</span><span class="p">:</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="n">Composite</span><span class="p">(</span><span class="n">set_name</span><span class="p">,</span> <span class="n">superset</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">tmp</span><span class="o">.</span><span class="n">append_set</span><span class="p">(</span><span class="n">FuzzySet</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">season</span><span class="p">,</span> <span class="n">set_name</span><span class="p">,</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span><span class="p">,</span>
|
||||
<span class="p">[</span><span class="mf">0.0000001</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span>
|
||||
<span class="n">pl2</span><span class="p">],</span> <span class="mf">0.0</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=.</span><span class="mi">5</span><span class="p">,</span>
|
||||
<span class="o">**</span><span class="n">kwargs</span><span class="p">))</span>
|
||||
<span class="n">tmp</span><span class="o">.</span><span class="n">append_set</span><span class="p">(</span><span class="n">FuzzySet</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">season</span><span class="p">,</span> <span class="n">set_name</span><span class="p">,</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span><span class="p">,</span>
|
||||
<span class="p">[</span><span class="n">c</span> <span class="o">-</span> <span class="n">partlen</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">c</span> <span class="o">+</span> <span class="n">partlen</span><span class="p">],</span> <span class="n">c</span><span class="p">,</span>
|
||||
<span class="o">**</span><span class="n">kwargs</span><span class="p">))</span>
|
||||
<span class="n">tmp</span><span class="o">.</span><span class="n">centroid</span> <span class="o">=</span> <span class="n">c</span>
|
||||
<span class="n">sets</span><span class="p">[</span><span class="n">set_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">sets</span><span class="p">[</span><span class="n">set_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">season</span><span class="p">,</span> <span class="n">set_name</span><span class="p">,</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span><span class="p">,</span>
|
||||
<span class="p">[</span><span class="n">c</span> <span class="o">-</span> <span class="n">partlen</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">c</span> <span class="o">+</span> <span class="n">partlen</span><span class="p">],</span> <span class="n">c</span><span class="p">,</span>
|
||||
|
||||
@ -164,7 +164,15 @@
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">mp</span><span class="p">,</span> <span class="mi">50</span><span class="p">))</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div></div>
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="sd">"""String representation of the model"""</span>
|
||||
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">+</span> <span class="s2">":</span><span class="se">\n</span><span class="s2">"</span>
|
||||
<span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">r</span><span class="p">])</span> <span class="o">+</span> <span class="s2">"</span><span class="se">\n</span><span class="s2">"</span>
|
||||
<span class="k">return</span> <span class="n">tmp</span></div>
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
|
||||
@ -116,7 +116,7 @@
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o"><</span> <span class="mi">2</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="kc">None</span>
|
||||
<span class="n">count</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">()))</span>
|
||||
<span class="n">ndata</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
|
||||
<span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
|
||||
<span class="n">threshold</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
|
||||
@ -128,7 +128,7 @@
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">min</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">_min</span> <span class="o">*</span> <span class="mf">1.1</span> <span class="k">if</span> <span class="n">_min</span> <span class="o"><</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">_min</span> <span class="o">*</span> <span class="mf">0.9</span><span class="p">)</span>
|
||||
|
||||
<span class="n">_max</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
|
||||
<span class="n">_max</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmax</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">_max</span> <span class="o">*</span> <span class="mf">1.1</span> <span class="k">if</span> <span class="n">_max</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="n">_max</span> <span class="o">*</span> <span class="mf">0.9</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">sets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
|
||||
|
||||
@ -256,7 +256,8 @@
|
||||
|
||||
<div class="viewcode-block" id="ProbabilityDistribution.cumulative"><a class="viewcode-back" href="../../../pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.cumulative">[docs]</a> <span class="k">def</span> <span class="nf">cumulative</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">values</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Return the cumulative probability densities for the input values</span>
|
||||
<span class="sd"> Return the cumulative probability densities for the input values, </span>
|
||||
<span class="sd"> such that F(x) = P(X <= x)</span>
|
||||
|
||||
<span class="sd"> :param values: A list of input values</span>
|
||||
<span class="sd"> :return: The cumulative probability densities for the input values</span>
|
||||
@ -275,7 +276,8 @@
|
||||
|
||||
<div class="viewcode-block" id="ProbabilityDistribution.quantile"><a class="viewcode-back" href="../../../pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.quantile">[docs]</a> <span class="k">def</span> <span class="nf">quantile</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">values</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Return the quantile values for the input values</span>
|
||||
<span class="sd"> Return the Universe of Discourse values in relation to the quantile input values, </span>
|
||||
<span class="sd"> such that Q(tau) = min( {x | F(x) >= tau })</span>
|
||||
|
||||
<span class="sd"> :param values: input values</span>
|
||||
<span class="sd"> :return: The list of the quantile values for the input values</span>
|
||||
@ -296,7 +298,7 @@
|
||||
|
||||
<div class="viewcode-block" id="ProbabilityDistribution.entropy"><a class="viewcode-back" href="../../../pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.entropy">[docs]</a> <span class="k">def</span> <span class="nf">entropy</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Return the entropy of the probability distribution, H[X] =</span>
|
||||
<span class="sd"> Return the entropy of the probability distribution, H(P) = E[ -ln P(X) ] = - ∑ P(x) log ( P(x) )</span>
|
||||
|
||||
<span class="sd"> :return:the entropy of the probability distribution</span>
|
||||
<span class="sd"> """</span>
|
||||
@ -306,7 +308,8 @@
|
||||
|
||||
<div class="viewcode-block" id="ProbabilityDistribution.crossentropy"><a class="viewcode-back" href="../../../pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.crossentropy">[docs]</a> <span class="k">def</span> <span class="nf">crossentropy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">q</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Cross entropy between the actual probability distribution and the informed one.</span>
|
||||
<span class="sd"> Cross entropy between the actual probability distribution and the informed one, </span>
|
||||
<span class="sd"> H(P,Q) = - ∑ P(x) log ( Q(x) )</span>
|
||||
|
||||
<span class="sd"> :param q: a probabilistic.ProbabilityDistribution object</span>
|
||||
<span class="sd"> :return: Cross entropy between this probability distribution and the given distribution</span>
|
||||
@ -318,6 +321,7 @@
|
||||
<div class="viewcode-block" id="ProbabilityDistribution.kullbackleiblerdivergence"><a class="viewcode-back" href="../../../pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.kullbackleiblerdivergence">[docs]</a> <span class="k">def</span> <span class="nf">kullbackleiblerdivergence</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">q</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Kullback-Leibler divergence between the actual probability distribution and the informed one.</span>
|
||||
<span class="sd"> DKL(P || Q) = - ∑ P(x) log( P(X) / Q(x) )</span>
|
||||
|
||||
<span class="sd"> :param q: a probabilistic.ProbabilityDistribution object</span>
|
||||
<span class="sd"> :return: Kullback-Leibler divergence</span>
|
||||
@ -328,7 +332,7 @@
|
||||
|
||||
<div class="viewcode-block" id="ProbabilityDistribution.empiricalloglikelihood"><a class="viewcode-back" href="../../../pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.empiricalloglikelihood">[docs]</a> <span class="k">def</span> <span class="nf">empiricalloglikelihood</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Empirical Log Likelihood of the probability distribution</span>
|
||||
<span class="sd"> Empirical Log Likelihood of the probability distribution, L(P) = ∑ log( P(x) )</span>
|
||||
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
24
docs/build/html/_sources/pyFTS.models.incremental.rst.txt
vendored
Normal file
24
docs/build/html/_sources/pyFTS.models.incremental.rst.txt
vendored
Normal file
@ -0,0 +1,24 @@
|
||||
pyFTS.models.incremental package
|
||||
=============================
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: pyFTS.models.incremental
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
pyFTS.models.incremental.Retrainer module
|
||||
-------------------------------------
|
||||
|
||||
.. automodule:: pyFTS.models.incremental.Retrainer
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
|
||||
@ -16,6 +16,7 @@ Subpackages
|
||||
.. toctree::
|
||||
|
||||
pyFTS.models.ensemble
|
||||
pyFTS.models.incremental
|
||||
pyFTS.models.multivariate
|
||||
pyFTS.models.nonstationary
|
||||
pyFTS.models.seasonal
|
||||
|
||||
46
docs/build/html/genindex.html
vendored
46
docs/build/html/genindex.html
vendored
@ -117,7 +117,11 @@
|
||||
<li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS">AllMethodEnsembleFTS (class in pyFTS.models.ensemble.ensemble)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.FuzzySet.FuzzySet.alpha">alpha (pyFTS.common.FuzzySet.FuzzySet attribute)</a>
|
||||
|
||||
<ul>
|
||||
<li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.alpha">(pyFTS.models.ensemble.ensemble.EnsembleFTS attribute)</a>
|
||||
</li>
|
||||
</ul></li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.alpha_cut">alpha_cut (pyFTS.common.fts.FTS attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.analytic_tabular_dataframe">analytic_tabular_dataframe() (in module pyFTS.benchmarks.Util)</a>
|
||||
@ -213,7 +217,11 @@
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.SortedCollection.SortedCollection.around">around() (pyFTS.common.SortedCollection.SortedCollection method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.auto_update">auto_update (pyFTS.common.fts.FTS attribute)</a>
|
||||
|
||||
<ul>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.Retrainer.Retrainer.auto_update">(pyFTS.models.incremental.Retrainer.Retrainer attribute)</a>
|
||||
</li>
|
||||
</ul></li>
|
||||
<li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.averageloglikelihood">averageloglikelihood() (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)</a>
|
||||
</li>
|
||||
</ul></td>
|
||||
@ -508,6 +516,8 @@
|
||||
<li><a href="pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.forecast">(pyFTS.models.hofts.HighOrderFTS method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.html#pyFTS.models.hwang.HighOrderFTS.forecast">(pyFTS.models.hwang.HighOrderFTS method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.Retrainer.Retrainer.forecast">(pyFTS.models.incremental.Retrainer.Retrainer method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFTS.forecast">(pyFTS.models.ismailefendi.ImprovedWeightedFTS method)</a>
|
||||
</li>
|
||||
@ -609,6 +619,10 @@
|
||||
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.format_data">format_data() (pyFTS.models.multivariate.mvfts.MVFTS method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS">FTS (class in pyFTS.common.fts)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.Retrainer.Retrainer.fts_method">fts_method (pyFTS.models.incremental.Retrainer.Retrainer attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.Retrainer.Retrainer.fts_params">fts_params (pyFTS.models.incremental.Retrainer.Retrainer attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.common.fuzzify">fuzzify() (in module pyFTS.models.nonstationary.common)</a>
|
||||
</li>
|
||||
@ -1037,6 +1051,8 @@
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.interval_dataframe_synthetic_columns">interval_dataframe_synthetic_columns() (in module pyFTS.benchmarks.Util)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.interval_heuristic">interval_heuristic() (pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.interval_method">interval_method (pyFTS.models.ensemble.ensemble.EnsembleFTS attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.interval_quantile">interval_quantile() (pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
|
||||
</li>
|
||||
@ -1163,6 +1179,8 @@
|
||||
<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.minute_of_week">minute_of_week (pyFTS.models.seasonal.common.DateTime attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.minute_of_year">minute_of_year (pyFTS.models.seasonal.common.DateTime attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.Retrainer.Retrainer.model">model (pyFTS.models.incremental.Retrainer.Retrainer attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.models">models (pyFTS.models.ensemble.ensemble.EnsembleFTS attribute)</a>
|
||||
</li>
|
||||
@ -1251,6 +1269,14 @@
|
||||
<li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner">Partitioner (class in pyFTS.partitioners.partitioner)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.partitioner">partitioner (pyFTS.common.fts.FTS attribute)</a>
|
||||
|
||||
<ul>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.Retrainer.Retrainer.partitioner">(pyFTS.models.incremental.Retrainer.Retrainer attribute)</a>
|
||||
</li>
|
||||
</ul></li>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.Retrainer.Retrainer.partitioner_method">partitioner_method (pyFTS.models.incremental.Retrainer.Retrainer attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.Retrainer.Retrainer.partitioner_params">partitioner_params (pyFTS.models.incremental.Retrainer.Retrainer attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.partitions">partitions (pyFTS.partitioners.partitioner.Partitioner attribute)</a>
|
||||
</li>
|
||||
@ -1339,6 +1365,8 @@
|
||||
<li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.point_expected_value">point_expected_value() (pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.point_heuristic">point_heuristic() (pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.point_method">point_method (pyFTS.models.ensemble.ensemble.EnsembleFTS attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.point_to_interval">point_to_interval() (pyFTS.benchmarks.quantreg.QuantileRegression method)</a>
|
||||
</li>
|
||||
@ -1397,11 +1425,11 @@
|
||||
<li><a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.quantreg">pyFTS.benchmarks.quantreg (module)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.ResidualAnalysis">pyFTS.benchmarks.ResidualAnalysis (module)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.Util">pyFTS.benchmarks.Util (module)</a>
|
||||
</li>
|
||||
</ul></td>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.Util">pyFTS.benchmarks.Util (module)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#module-pyFTS.common">pyFTS.common (module)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#module-pyFTS.common.Composite">pyFTS.common.Composite (module)</a>
|
||||
@ -1487,6 +1515,10 @@
|
||||
<li><a href="pyFTS.models.html#module-pyFTS.models.hwang">pyFTS.models.hwang (module)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.html#module-pyFTS.models.ifts">pyFTS.models.ifts (module)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.incremental.html#module-pyFTS.models.incremental">pyFTS.models.incremental (module)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.incremental.html#module-pyFTS.models.incremental.Retrainer">pyFTS.models.incremental.Retrainer (module)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.html#module-pyFTS.models.ismailefendi">pyFTS.models.ismailefendi (module)</a>
|
||||
</li>
|
||||
@ -1593,6 +1625,8 @@
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.ResidualAnalysis.residuals">residuals() (in module pyFTS.benchmarks.ResidualAnalysis)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.resolution">resolution() (in module pyFTS.benchmarks.Measures)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.Retrainer.Retrainer">Retrainer (class in pyFTS.models.incremental.Retrainer)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.FLR.FLR.RHS">RHS (pyFTS.common.FLR.FLR attribute)</a>
|
||||
|
||||
@ -1600,10 +1634,10 @@
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.flrg.FLRG.RHS">(pyFTS.common.flrg.FLRG attribute)</a>
|
||||
</li>
|
||||
</ul></li>
|
||||
<li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.rhs_conditional_probability">rhs_conditional_probability() (pyFTS.models.pwfts.ProbabilisticWeightedFLRG method)</a>
|
||||
</li>
|
||||
</ul></td>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.rhs_conditional_probability">rhs_conditional_probability() (pyFTS.models.pwfts.ProbabilisticWeightedFLRG method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.rhs_unconditional_probability">rhs_unconditional_probability() (pyFTS.models.pwfts.ProbabilisticWeightedFLRG method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.rmse">rmse() (in module pyFTS.benchmarks.Measures)</a>
|
||||
@ -1764,6 +1798,8 @@
|
||||
<li><a href="pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.train">(pyFTS.models.hofts.HighOrderFTS method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.html#pyFTS.models.hwang.HighOrderFTS.train">(pyFTS.models.hwang.HighOrderFTS method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.Retrainer.Retrainer.train">(pyFTS.models.incremental.Retrainer.Retrainer method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFTS.train">(pyFTS.models.ismailefendi.ImprovedWeightedFTS method)</a>
|
||||
</li>
|
||||
@ -1891,6 +1927,8 @@
|
||||
<li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.common.FuzzySet.width_params">width_params (pyFTS.models.nonstationary.common.FuzzySet attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.common.window_index">window_index() (in module pyFTS.models.nonstationary.common)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.Retrainer.Retrainer.window_length">window_length (pyFTS.models.incremental.Retrainer.Retrainer attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.winkler_mean">winkler_mean() (in module pyFTS.benchmarks.Measures)</a>
|
||||
</li>
|
||||
|
||||
BIN
docs/build/html/objects.inv
vendored
BIN
docs/build/html/objects.inv
vendored
Binary file not shown.
10
docs/build/html/py-modindex.html
vendored
10
docs/build/html/py-modindex.html
vendored
@ -350,6 +350,16 @@
|
||||
<td>   
|
||||
<a href="pyFTS.models.html#module-pyFTS.models.ifts"><code class="xref">pyFTS.models.ifts</code></a></td><td>
|
||||
<em></em></td></tr>
|
||||
<tr class="cg-1">
|
||||
<td></td>
|
||||
<td>   
|
||||
<a href="pyFTS.models.incremental.html#module-pyFTS.models.incremental"><code class="xref">pyFTS.models.incremental</code></a></td><td>
|
||||
<em></em></td></tr>
|
||||
<tr class="cg-1">
|
||||
<td></td>
|
||||
<td>   
|
||||
<a href="pyFTS.models.incremental.html#module-pyFTS.models.incremental.Retrainer"><code class="xref">pyFTS.models.incremental.Retrainer</code></a></td><td>
|
||||
<em></em></td></tr>
|
||||
<tr class="cg-1">
|
||||
<td></td>
|
||||
<td>   
|
||||
|
||||
6
docs/build/html/pyFTS.html
vendored
6
docs/build/html/pyFTS.html
vendored
@ -174,6 +174,12 @@
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.ensemble.html#module-pyFTS.models.ensemble">Module contents</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="pyFTS.models.incremental.html">pyFTS.models.incremental package</a><ul>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.incremental.html#module-pyFTS.models.incremental">Module contents</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.incremental.html#submodules">Submodules</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.incremental.html#pyfts-models-incremental-retrainer-module">pyFTS.models.incremental.Retrainer module</a></li>
|
||||
</ul>
|
||||
</li>
|
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<li class="toctree-l3"><a class="reference internal" href="pyFTS.models.multivariate.html">pyFTS.models.multivariate package</a><ul>
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|
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<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.multivariate.html#module-pyFTS.models.multivariate.FLR">pyFTS.models.multivariate.FLR module</a></li>
|
||||
|
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title="previous chapter">pyFTS.models package</a></p>
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<div class="section" id="module-pyFTS.models.ensemble.ensemble">
|
||||
<span id="pyfts-models-ensemble-ensemble-module"></span><h2>pyFTS.models.ensemble.ensemble module<a class="headerlink" href="#module-pyFTS.models.ensemble.ensemble" title="Permalink to this headline">¶</a></h2>
|
||||
<p>EnsembleFTS wraps several FTS methods to ensemble their forecasts, providing point,
|
||||
interval and probabilistic forecasting.</p>
|
||||
<p>Silva, P. C. L et al. Probabilistic Forecasting with Seasonal Ensemble Fuzzy Time-Series
|
||||
XIII Brazilian Congress on Computational Intelligence, 2017. Rio de Janeiro, Brazil.</p>
|
||||
<dl class="class">
|
||||
<dt id="pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS">
|
||||
<em class="property">class </em><code class="descclassname">pyFTS.models.ensemble.ensemble.</code><code class="descname">AllMethodEnsembleFTS</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/ensemble.html#AllMethodEnsembleFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS" title="pyFTS.models.ensemble.ensemble.EnsembleFTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.ensemble.ensemble.EnsembleFTS</span></code></a></p>
|
||||
<p>Creates an EnsembleFTS with all point forecast methods, sharing the same partitioner</p>
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS.set_transformations">
|
||||
<code class="descname">set_transformations</code><span class="sig-paren">(</span><em>model</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/ensemble.html#AllMethodEnsembleFTS.set_transformations"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS.set_transformations" title="Permalink to this definition">¶</a></dt>
|
||||
@ -146,10 +151,16 @@
|
||||
<em class="property">class </em><code class="descclassname">pyFTS.models.ensemble.ensemble.</code><code class="descname">EnsembleFTS</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/ensemble.html#EnsembleFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.fts.FTS" title="pyFTS.common.fts.FTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.fts.FTS</span></code></a></p>
|
||||
<p>Ensemble FTS</p>
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.alpha">
|
||||
<code class="descname">alpha</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.alpha" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The quantiles</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.append_model">
|
||||
<code class="descname">append_model</code><span class="sig-paren">(</span><em>model</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/ensemble.html#EnsembleFTS.append_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.append_model" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Append a new model to the ensemble</p>
|
||||
<dd><p>Append a new trained model to the ensemble</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
@ -287,6 +298,12 @@
|
||||
<code class="descname">get_point</code><span class="sig-paren">(</span><em>forecasts</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/ensemble.html#EnsembleFTS.get_point"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_point" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.interval_method">
|
||||
<code class="descname">interval_method</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.interval_method" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The method used to mix the several model’s forecasts into a interval forecast. Options: quantile, extremum, normal</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.models">
|
||||
<code class="descname">models</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.models" title="Permalink to this definition">¶</a></dt>
|
||||
@ -299,6 +316,12 @@
|
||||
<dd><p>A list with the parameters for each component model</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.point_method">
|
||||
<code class="descname">point_method</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.point_method" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The method used to mix the several model’s forecasts into a unique point forecast. Options: mean, median, quantile</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.ensemble.ensemble.EnsembleFTS.train">
|
||||
<code class="descname">train</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/ensemble.html#EnsembleFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.ensemble.EnsembleFTS.train" title="Permalink to this definition">¶</a></dt>
|
||||
@ -327,6 +350,8 @@
|
||||
</div>
|
||||
<div class="section" id="module-pyFTS.models.ensemble.multiseasonal">
|
||||
<span id="pyfts-models-ensemble-multiseasonal-module"></span><h2>pyFTS.models.ensemble.multiseasonal module<a class="headerlink" href="#module-pyFTS.models.ensemble.multiseasonal" title="Permalink to this headline">¶</a></h2>
|
||||
<p>Silva, P. C. L et al. Probabilistic Forecasting with Seasonal Ensemble Fuzzy Time-Series
|
||||
XIII Brazilian Congress on Computational Intelligence, 2017. Rio de Janeiro, Brazil.</p>
|
||||
<dl class="class">
|
||||
<dt id="pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS">
|
||||
<em class="property">class </em><code class="descclassname">pyFTS.models.ensemble.multiseasonal.</code><code class="descname">SeasonalEnsembleFTS</code><span class="sig-paren">(</span><em>name</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/ensemble/multiseasonal.html#SeasonalEnsembleFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS" title="Permalink to this definition">¶</a></dt>
|
||||
@ -405,7 +430,7 @@
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<li><a class="reference internal" href="#pyfts-models-incremental-retrainer-module">pyFTS.models.incremental.Retrainer module</a></li>
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<div class="section" id="pyfts-models-incremental-package">
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<h1>pyFTS.models.incremental package<a class="headerlink" href="#pyfts-models-incremental-package" title="Permalink to this headline">¶</a></h1>
|
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<div class="section" id="module-pyFTS.models.incremental">
|
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<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-pyFTS.models.incremental" title="Permalink to this headline">¶</a></h2>
|
||||
<p>FTS methods with incremental/online learning</p>
|
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</div>
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<div class="section" id="submodules">
|
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<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
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</div>
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<div class="section" id="pyfts-models-incremental-retrainer-module">
|
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<h2>pyFTS.models.incremental.Retrainer module<a class="headerlink" href="#pyfts-models-incremental-retrainer-module" title="Permalink to this headline">¶</a></h2>
|
||||
<span class="target" id="module-pyFTS.models.incremental.Retrainer"></span><p>Meta model that wraps another FTS method and continously retrain it using a data window with the most recent data</p>
|
||||
<dl class="class">
|
||||
<dt id="pyFTS.models.incremental.Retrainer.Retrainer">
|
||||
<em class="property">class </em><code class="descclassname">pyFTS.models.incremental.Retrainer.</code><code class="descname">Retrainer</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/incremental/Retrainer.html#Retrainer"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.fts.FTS" title="pyFTS.common.fts.FTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.fts.FTS</span></code></a></p>
|
||||
<p>Meta model for incremental/online learning</p>
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.auto_update">
|
||||
<code class="descname">auto_update</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer.auto_update" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>If true the model is updated at each time and not recreated</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.forecast">
|
||||
<code class="descname">forecast</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/incremental/Retrainer.html#Retrainer.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer.forecast" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Point forecast one step ahead</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
|
||||
<li><strong>kwargs</strong> – model specific parameters</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the forecasted values</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.fts_method">
|
||||
<code class="descname">fts_method</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer.fts_method" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The FTS method to be called when a new model is build</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.fts_params">
|
||||
<code class="descname">fts_params</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer.fts_params" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The FTS method specific parameters</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.model">
|
||||
<code class="descname">model</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer.model" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The most recent trained model</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.partitioner">
|
||||
<code class="descname">partitioner</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer.partitioner" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The most recent trained partitioner</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.partitioner_method">
|
||||
<code class="descname">partitioner_method</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer.partitioner_method" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The partitioner method to be called when a new model is build</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.partitioner_params">
|
||||
<code class="descname">partitioner_params</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer.partitioner_params" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The partitioner method parameters</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.train">
|
||||
<code class="descname">train</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/incremental/Retrainer.html#Retrainer.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer.train" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Method specific parameter fitting</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
|
||||
<li><strong>data</strong> – training time series data</li>
|
||||
<li><strong>kwargs</strong> – Method specific parameters</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.window_length">
|
||||
<code class="descname">window_length</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer.window_length" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The memory window length</p>
|
||||
</dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
|
||||
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="clearer"></div>
|
||||
</div>
|
||||
<div class="related" role="navigation" aria-label="related navigation">
|
||||
<h3>Navigation</h3>
|
||||
<ul>
|
||||
<li class="right" style="margin-right: 10px">
|
||||
<a href="genindex.html" title="General Index"
|
||||
>index</a></li>
|
||||
<li class="right" >
|
||||
<a href="py-modindex.html" title="Python Module Index"
|
||||
>modules</a> |</li>
|
||||
<li class="right" >
|
||||
<a href="pyFTS.models.multivariate.html" title="pyFTS.models.multivariate package"
|
||||
>next</a> |</li>
|
||||
<li class="right" >
|
||||
<a href="pyFTS.models.ensemble.html" title="pyFTS.models.ensemble package"
|
||||
>previous</a> |</li>
|
||||
<li class="nav-item nav-item-0"><a href="index.html">pyFTS 1.2.3 documentation</a> »</li>
|
||||
<li class="nav-item nav-item-1"><a href="modules.html" >pyFTS</a> »</li>
|
||||
<li class="nav-item nav-item-2"><a href="pyFTS.html" >pyFTS package</a> »</li>
|
||||
<li class="nav-item nav-item-3"><a href="pyFTS.models.html" >pyFTS.models package</a> »</li>
|
||||
</ul>
|
||||
</div>
|
||||
<div class="footer" role="contentinfo">
|
||||
© Copyright 2018, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
|
||||
Created using <a href="http://sphinx-doc.org/">Sphinx</a> 1.7.2.
|
||||
</div>
|
||||
</body>
|
||||
</html>
|
||||
10
docs/build/html/pyFTS.models.multivariate.html
vendored
10
docs/build/html/pyFTS.models.multivariate.html
vendored
@ -29,7 +29,7 @@
|
||||
<link rel="index" title="Index" href="genindex.html" />
|
||||
<link rel="search" title="Search" href="search.html" />
|
||||
<link rel="next" title="pyFTS.models.nonstationary package" href="pyFTS.models.nonstationary.html" />
|
||||
<link rel="prev" title="pyFTS.models.ensemble package" href="pyFTS.models.ensemble.html" />
|
||||
<link rel="prev" title="pyFTS.models.incremental package" href="pyFTS.models.incremental.html" />
|
||||
<meta name="viewport" content="width=device-width,initial-scale=1.0">
|
||||
<!--[if lt IE 9]>
|
||||
<script type="text/javascript" src="_static/css3-mediaqueries.js"></script>
|
||||
@ -48,7 +48,7 @@
|
||||
<a href="pyFTS.models.nonstationary.html" title="pyFTS.models.nonstationary package"
|
||||
accesskey="N">next</a> |</li>
|
||||
<li class="right" >
|
||||
<a href="pyFTS.models.ensemble.html" title="pyFTS.models.ensemble package"
|
||||
<a href="pyFTS.models.incremental.html" title="pyFTS.models.incremental package"
|
||||
accesskey="P">previous</a> |</li>
|
||||
<li class="nav-item nav-item-0"><a href="index.html">pyFTS 1.2.3 documentation</a> »</li>
|
||||
<li class="nav-item nav-item-1"><a href="modules.html" >pyFTS</a> »</li>
|
||||
@ -76,8 +76,8 @@
|
||||
</ul>
|
||||
|
||||
<h4>Previous topic</h4>
|
||||
<p class="topless"><a href="pyFTS.models.ensemble.html"
|
||||
title="previous chapter">pyFTS.models.ensemble package</a></p>
|
||||
<p class="topless"><a href="pyFTS.models.incremental.html"
|
||||
title="previous chapter">pyFTS.models.incremental package</a></p>
|
||||
<h4>Next topic</h4>
|
||||
<p class="topless"><a href="pyFTS.models.nonstationary.html"
|
||||
title="next chapter">pyFTS.models.nonstationary package</a></p>
|
||||
@ -383,7 +383,7 @@ transformations and partitioners.</p>
|
||||
<a href="pyFTS.models.nonstationary.html" title="pyFTS.models.nonstationary package"
|
||||
>next</a> |</li>
|
||||
<li class="right" >
|
||||
<a href="pyFTS.models.ensemble.html" title="pyFTS.models.ensemble package"
|
||||
<a href="pyFTS.models.incremental.html" title="pyFTS.models.incremental package"
|
||||
>previous</a> |</li>
|
||||
<li class="nav-item nav-item-0"><a href="index.html">pyFTS 1.2.3 documentation</a> »</li>
|
||||
<li class="nav-item nav-item-1"><a href="modules.html" >pyFTS</a> »</li>
|
||||
|
||||
16
docs/build/html/pyFTS.probabilistic.html
vendored
16
docs/build/html/pyFTS.probabilistic.html
vendored
@ -173,7 +173,8 @@ If type is KDE the PDF is continuous</p>
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.crossentropy">
|
||||
<code class="descname">crossentropy</code><span class="sig-paren">(</span><em>q</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/probabilistic/ProbabilityDistribution.html#ProbabilityDistribution.crossentropy"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.crossentropy" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Cross entropy between the actual probability distribution and the informed one.</p>
|
||||
<dd><p>Cross entropy between the actual probability distribution and the informed one,
|
||||
H(P,Q) = - ∑ P(x) log ( Q(x) )</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
@ -189,7 +190,8 @@ If type is KDE the PDF is continuous</p>
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.cumulative">
|
||||
<code class="descname">cumulative</code><span class="sig-paren">(</span><em>values</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/probabilistic/ProbabilityDistribution.html#ProbabilityDistribution.cumulative"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.cumulative" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Return the cumulative probability densities for the input values</p>
|
||||
<dd><p>Return the cumulative probability densities for the input values,
|
||||
such that F(x) = P(X <= x)</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
@ -237,7 +239,7 @@ If type is KDE the PDF is continuous</p>
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.empiricalloglikelihood">
|
||||
<code class="descname">empiricalloglikelihood</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/probabilistic/ProbabilityDistribution.html#ProbabilityDistribution.empiricalloglikelihood"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.empiricalloglikelihood" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Empirical Log Likelihood of the probability distribution</p>
|
||||
<dd><p>Empirical Log Likelihood of the probability distribution, L(P) = ∑ log( P(x) )</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
@ -251,7 +253,7 @@ If type is KDE the PDF is continuous</p>
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.entropy">
|
||||
<code class="descname">entropy</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/probabilistic/ProbabilityDistribution.html#ProbabilityDistribution.entropy"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.entropy" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Return the entropy of the probability distribution, H[X] =</p>
|
||||
<dd><p>Return the entropy of the probability distribution, H(P) = E[ -ln P(X) ] = - ∑ P(x) log ( P(x) )</p>
|
||||
<p>:return:the entropy of the probability distribution</p>
|
||||
</dd></dl>
|
||||
|
||||
@ -272,7 +274,8 @@ If type is KDE the PDF is continuous</p>
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.kullbackleiblerdivergence">
|
||||
<code class="descname">kullbackleiblerdivergence</code><span class="sig-paren">(</span><em>q</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/probabilistic/ProbabilityDistribution.html#ProbabilityDistribution.kullbackleiblerdivergence"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.kullbackleiblerdivergence" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Kullback-Leibler divergence between the actual probability distribution and the informed one.</p>
|
||||
<dd><p>Kullback-Leibler divergence between the actual probability distribution and the informed one.
|
||||
DKL(P || Q) = - ∑ P(x) log( P(X) / Q(x) )</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
@ -315,7 +318,8 @@ If type is KDE the PDF is continuous</p>
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.quantile">
|
||||
<code class="descname">quantile</code><span class="sig-paren">(</span><em>values</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/probabilistic/ProbabilityDistribution.html#ProbabilityDistribution.quantile"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.quantile" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Return the quantile values for the input values</p>
|
||||
<dd><p>Return the Universe of Discourse values in relation to the quantile input values,
|
||||
such that Q(tau) = min( {x | F(x) >= tau })</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
|
||||
2
docs/build/html/searchindex.js
vendored
2
docs/build/html/searchindex.js
vendored
File diff suppressed because one or more lines are too long
24
docs/pyFTS.models.incremental.rst
Normal file
24
docs/pyFTS.models.incremental.rst
Normal file
@ -0,0 +1,24 @@
|
||||
pyFTS.models.incremental package
|
||||
=============================
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: pyFTS.models.incremental
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
pyFTS.models.incremental.Retrainer module
|
||||
-------------------------------------
|
||||
|
||||
.. automodule:: pyFTS.models.incremental.Retrainer
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
|
||||
@ -16,6 +16,7 @@ Subpackages
|
||||
.. toctree::
|
||||
|
||||
pyFTS.models.ensemble
|
||||
pyFTS.models.incremental
|
||||
pyFTS.models.multivariate
|
||||
pyFTS.models.nonstationary
|
||||
pyFTS.models.seasonal
|
||||
|
||||
@ -1,5 +1,11 @@
|
||||
#!/usr/bin/python
|
||||
# -*- coding: utf8 -*-
|
||||
"""
|
||||
EnsembleFTS wraps several FTS methods to ensemble their forecasts, providing point,
|
||||
interval and probabilistic forecasting.
|
||||
|
||||
Silva, P. C. L et al. Probabilistic Forecasting with Seasonal Ensemble Fuzzy Time-Series
|
||||
XIII Brazilian Congress on Computational Intelligence, 2017. Rio de Janeiro, Brazil.
|
||||
"""
|
||||
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
@ -22,7 +28,7 @@ class EnsembleFTS(fts.FTS):
|
||||
"""
|
||||
def __init__(self, **kwargs):
|
||||
super(EnsembleFTS, self).__init__(**kwargs)
|
||||
self.shortname = "Ensemble FTS"
|
||||
self.shortname = "EnsembleFTS"
|
||||
self.name = "Ensemble FTS"
|
||||
self.flrgs = {}
|
||||
self.has_point_forecasting = True
|
||||
@ -34,13 +40,16 @@ class EnsembleFTS(fts.FTS):
|
||||
self.parameters = []
|
||||
"""A list with the parameters for each component model"""
|
||||
self.alpha = kwargs.get("alpha", 0.05)
|
||||
"""The quantiles """
|
||||
self.point_method = kwargs.get('point_method', 'mean')
|
||||
"""The method used to mix the several model's forecasts into a unique point forecast. Options: mean, median, quantile"""
|
||||
self.interval_method = kwargs.get('interval_method', 'quantile')
|
||||
"""The method used to mix the several model's forecasts into a interval forecast. Options: quantile, extremum, normal"""
|
||||
self.order = 1
|
||||
|
||||
def append_model(self, model):
|
||||
"""
|
||||
Append a new model to the ensemble
|
||||
Append a new trained model to the ensemble
|
||||
|
||||
:param model: FTS model
|
||||
|
||||
@ -267,6 +276,9 @@ class EnsembleFTS(fts.FTS):
|
||||
|
||||
|
||||
class AllMethodEnsembleFTS(EnsembleFTS):
|
||||
"""
|
||||
Creates an EnsembleFTS with all point forecast methods, sharing the same partitioner
|
||||
"""
|
||||
def __init__(self, **kwargs):
|
||||
super(AllMethodEnsembleFTS, self).__init__(**kwargs)
|
||||
self.min_order = 3
|
||||
|
||||
@ -1,5 +1,7 @@
|
||||
#!/usr/bin/python
|
||||
# -*- coding: utf8 -*-
|
||||
"""
|
||||
Silva, P. C. L et al. Probabilistic Forecasting with Seasonal Ensemble Fuzzy Time-Series
|
||||
XIII Brazilian Congress on Computational Intelligence, 2017. Rio de Janeiro, Brazil.
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
from pyFTS.common import Util as cUtil
|
||||
|
||||
73
pyFTS/models/incremental/Retrainer.py
Normal file
73
pyFTS/models/incremental/Retrainer.py
Normal file
@ -0,0 +1,73 @@
|
||||
"""
|
||||
Meta model that wraps another FTS method and continously retrain it using a data window with the most recent data
|
||||
"""
|
||||
|
||||
import numpy as np
|
||||
from pyFTS.common import FuzzySet, FLR, fts, flrg
|
||||
from pyFTS.partitioners import Grid
|
||||
|
||||
|
||||
class Retrainer(fts.FTS):
|
||||
"""
|
||||
Meta model for incremental/online learning
|
||||
"""
|
||||
def __init__(self, **kwargs):
|
||||
super(Retrainer, self).__init__(**kwargs)
|
||||
|
||||
self.partitioner_method = kwargs.get('partitioner_method', Grid.GridPartitioner)
|
||||
"""The partitioner method to be called when a new model is build"""
|
||||
self.partitioner_params = kwargs.get('partitioner_params', {'npart': 10})
|
||||
"""The partitioner method parameters"""
|
||||
self.partitioner = None
|
||||
"""The most recent trained partitioner"""
|
||||
|
||||
self.fts_method = kwargs.get('fts_method', None)
|
||||
"""The FTS method to be called when a new model is build"""
|
||||
self.fts_params = kwargs.get('fts_params', {})
|
||||
"""The FTS method specific parameters"""
|
||||
self.model = None
|
||||
"""The most recent trained model"""
|
||||
|
||||
self.window_length = kwargs.get('window_length',100)
|
||||
"""The memory window length"""
|
||||
self.auto_update = False
|
||||
"""If true the model is updated at each time and not recreated"""
|
||||
self.is_high_order = True
|
||||
|
||||
def train(self, data, **kwargs):
|
||||
self.partitioner = self.partitioner_method(data=data, **self.partitioner_params)
|
||||
self.model = self.fts_method(partitioner=self.partitioner, order=self.order, **self.fts_params)
|
||||
self.model.fit(data, **kwargs)
|
||||
|
||||
def forecast(self, data, **kwargs):
|
||||
l = len(data)
|
||||
|
||||
horizon = self.window_length + self.order
|
||||
|
||||
ret = []
|
||||
|
||||
for k in np.arange(horizon, l):
|
||||
_train = data[k - horizon: k - self.order]
|
||||
_test = data[k - self.order: k]
|
||||
|
||||
if self.auto_update:
|
||||
self.model.train(_train)
|
||||
else:
|
||||
self.train(_train, **kwargs)
|
||||
|
||||
ret.extend(self.model.predict(_test, **kwargs))
|
||||
|
||||
return ret
|
||||
|
||||
def __str__(self):
|
||||
"""String representation of the model"""
|
||||
|
||||
return str(self.model)
|
||||
|
||||
def __len__(self):
|
||||
"""
|
||||
The length (number of rules) of the model
|
||||
|
||||
:return: number of rules
|
||||
"""
|
||||
return len(self.model)
|
||||
3
pyFTS/models/incremental/__init__.py
Normal file
3
pyFTS/models/incremental/__init__.py
Normal file
@ -0,0 +1,3 @@
|
||||
"""
|
||||
FTS methods with incremental/online learning
|
||||
"""
|
||||
3
setup.py
3
setup.py
@ -4,7 +4,8 @@ setup(
|
||||
name='pyFTS',
|
||||
packages=['pyFTS', 'pyFTS.benchmarks', 'pyFTS.common', 'pyFTS.data', 'pyFTS.models.ensemble',
|
||||
'pyFTS.models', 'pyFTS.models.seasonal', 'pyFTS.partitioners', 'pyFTS.probabilistic',
|
||||
'pyFTS.tests', 'pyFTS.models.nonstationary', 'pyFTS.models.multivariate'],
|
||||
'pyFTS.tests', 'pyFTS.models.nonstationary', 'pyFTS.models.multivariate',
|
||||
'pyFTS.models.incremental'],
|
||||
version='1.2.3',
|
||||
description='Fuzzy Time Series for Python',
|
||||
author='Petronio Candido L. e Silva',
|
||||
|
||||
Loading…
Reference in New Issue
Block a user