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<h1>Source code for pyFTS.benchmarks.BSTS</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 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">scipy.stats</span> <span class="k">as</span> <span class="nn">st</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">SortedCollection</span><span class="p">,</span> <span class="n">fts</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="k">import</span> <span class="n">ProbabilityDistribution</span>
|
||||
|
||||
|
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<div class="viewcode-block" id="ARIMA"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA">[docs]</a><span class="k">class</span> <span class="nc">ARIMA</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Façade for statsmodels.tsa.arima_model</span>
|
||||
<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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">ARIMA</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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"BSTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">"Bayesian Structural Time Series"</span>
|
||||
<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>
|
||||
<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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">trained_data</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">d</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">benchmark_only</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">order</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">"order"</span><span class="p">,</span> <span class="p">(</span><span class="mi">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="bp">self</span><span class="o">.</span><span class="n">_decompose_order</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</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="kc">None</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_decompose_order</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">order</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">order</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">set</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="n">order</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">d</span> <span class="o">=</span> <span class="n">order</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">=</span> <span class="n">order</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">+</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">d</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">"BSTS(</span><span class="si">{}</span><span class="s2">,</span><span class="si">{}</span><span class="s2">,</span><span class="si">{}</span><span class="s2">)-</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">d</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="ARIMA.train"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.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>
|
||||
<span class="kn">import</span> <span class="nn">pyflux</span> <span class="k">as</span> <span class="nn">pf</span>
|
||||
|
||||
<span class="k">if</span> <span class="s1">'order'</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
|
||||
<span class="n">order</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'order'</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_decompose_order</span><span class="p">(</span><span class="n">order</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
|
||||
<span class="k">try</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">pf</span><span class="o">.</span><span class="n">ARIMA</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="n">ar</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="n">ma</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="p">,</span> <span class="n">integ</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">d</span><span class="p">,</span> <span class="n">family</span><span class="o">=</span><span class="n">pf</span><span class="o">.</span><span class="n">Normal</span><span class="p">())</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span> <span class="o">=</span> <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="s1">'M-H'</span><span class="p">,</span> <span class="n">nsims</span><span class="o">=</span><span class="mi">20000</span><span class="p">)</span>
|
||||
<span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">ex</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">ex</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span> <span class="o">=</span> <span class="kc">None</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ARIMA.inference"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.inference">[docs]</a> <span class="k">def</span> <span class="nf">inference</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">steps</span><span class="p">):</span>
|
||||
<span class="n">t_z</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">transform_z</span><span class="p">()</span>
|
||||
<span class="n">mu</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">_model</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">latent_variables</span><span class="o">.</span><span class="n">get_z_values</span><span class="p">())</span>
|
||||
<span class="n">date_index</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">shift_dates</span><span class="p">(</span><span class="n">steps</span><span class="p">)</span>
|
||||
<span class="n">sim_vector</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">_sim_prediction</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="n">t_z</span><span class="p">,</span> <span class="mi">1000</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">sim_vector</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ARIMA.forecast"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.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">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ARIMA.forecast_ahead"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead</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="n">steps</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="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">steps</span><span class="p">,</span> <span class="n">intervals</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ARIMA.forecast_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.forecast_interval">[docs]</a> <span class="k">def</span> <span class="nf">forecast_interval</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>
|
||||
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ARIMA.forecast_ahead_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead_interval">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="n">sim_vector</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">inference</span><span class="p">(</span><span class="n">steps</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="s1">'alpha'</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</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="s1">'alpha'</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">alpha</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">sample</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sim_vector</span><span class="p">):</span>
|
||||
<span class="n">i</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="p">[</span><span class="n">alpha</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">alpha</span><span class="p">)</span><span class="o">*</span><span class="mi">100</span><span class="p">])</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ARIMA.forecast_distribution"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.forecast_distribution">[docs]</a> <span class="k">def</span> <span class="nf">forecast_distribution</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>
|
||||
<span class="kn">import</span> <span class="nn">pyflux</span> <span class="k">as</span> <span class="nn">pf</span>
|
||||
|
||||
<span class="n">sim_vector</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">inference</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">sample</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sim_vector</span><span class="p">):</span>
|
||||
<span class="n">pd</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="s1">'histogram'</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">sample</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pd</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ARIMA.forecast_ahead_distribution"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead_distribution">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead_distribution</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="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
|
||||
<span class="n">sim_vector</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">inference</span><span class="p">(</span><span class="n">steps</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">sample</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sim_vector</span><span class="p">):</span>
|
||||
<span class="n">pd</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="s1">'histogram'</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">sample</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pd</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div></div>
|
||||
</pre></div>
|
||||
|
||||
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<h1>Source code for pyFTS.benchmarks.Measures</h1><div class="highlight"><pre>
|
||||
<span></span><span class="c1"># -*- coding: utf8 -*-</span>
|
||||
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd">pyFTS module for common benchmark metrics</span>
|
||||
<span class="sd">"""</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">time</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">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">SortedCollection</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="k">import</span> <span class="n">ProbabilityDistribution</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="acf"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.acf">[docs]</a><span class="k">def</span> <span class="nf">acf</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Autocorrelation function estimative</span>
|
||||
|
||||
<span class="sd"> :param data: </span>
|
||||
<span class="sd"> :param k: </span>
|
||||
<span class="sd"> :return: </span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">mu</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
<span class="n">sigma</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
<span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
<span class="n">s</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="k">for</span> <span class="n">t</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">0</span><span class="p">,</span> <span class="n">n</span> <span class="o">-</span> <span class="n">k</span><span class="p">):</span>
|
||||
<span class="n">s</span> <span class="o">+=</span> <span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">-</span> <span class="n">mu</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">t</span> <span class="o">+</span> <span class="n">k</span><span class="p">]</span> <span class="o">-</span> <span class="n">mu</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">((</span><span class="n">n</span> <span class="o">-</span> <span class="n">k</span><span class="p">)</span> <span class="o">*</span> <span class="n">sigma</span><span class="p">)</span> <span class="o">*</span> <span class="n">s</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="rmse"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.rmse">[docs]</a><span class="k">def</span> <span class="nf">rmse</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Root Mean Squared Error</span>
|
||||
|
||||
<span class="sd"> :param targets: array of targets</span>
|
||||
<span class="sd"> :param forecasts: array of forecasts</span>
|
||||
<span class="sd"> :param order: model order</span>
|
||||
<span class="sd"> :param offset: forecast offset related to target. </span>
|
||||
<span class="sd"> :return: </span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
|
||||
<span class="n">targets</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">targets</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
|
||||
<span class="n">forecasts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">offset</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">((</span><span class="n">targets</span><span class="p">[</span><span class="n">order</span><span class="p">:]</span> <span class="o">-</span> <span class="n">forecasts</span><span class="p">[:])</span> <span class="o">**</span> <span class="mi">2</span><span class="p">))</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">((</span><span class="n">targets</span><span class="p">[</span><span class="n">order</span><span class="o">+</span><span class="n">offset</span><span class="p">:]</span> <span class="o">-</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="n">offset</span><span class="p">])</span> <span class="o">**</span> <span class="mi">2</span><span class="p">))</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="nmrse"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.nmrse">[docs]</a><span class="k">def</span> <span class="nf">nmrse</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
|
||||
<span class="sd">""" Normalized Root Mean Squared Error """</span>
|
||||
<span class="k">return</span> <span class="n">rmse</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">offset</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">targets</span><span class="p">)</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">targets</span><span class="p">))</span> <span class="c1">## normalizing in targets because on forecasts might explode to inf (when model predict a line)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="rmse_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.rmse_interval">[docs]</a><span class="k">def</span> <span class="nf">rmse_interval</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Root Mean Squared Error</span>
|
||||
|
||||
<span class="sd"> :param targets: </span>
|
||||
<span class="sd"> :param forecasts: </span>
|
||||
<span class="sd"> :return: </span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">fmean</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">forecasts</span><span class="p">]</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">((</span><span class="n">fmean</span> <span class="o">-</span> <span class="n">targets</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">))</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="mape"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.mape">[docs]</a><span class="k">def</span> <span class="nf">mape</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Mean Average Percentual Error</span>
|
||||
|
||||
<span class="sd"> :param targets: </span>
|
||||
<span class="sd"> :param forecasts: </span>
|
||||
<span class="sd"> :return: </span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
|
||||
<span class="n">targets</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">targets</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
|
||||
<span class="n">forecasts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">divide</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">),</span> <span class="n">targets</span><span class="p">)))</span> <span class="o">*</span> <span class="mi">100</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="smape"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.smape">[docs]</a><span class="k">def</span> <span class="nf">smape</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Symmetric Mean Average Percentual Error</span>
|
||||
|
||||
<span class="sd"> :param targets: </span>
|
||||
<span class="sd"> :param forecasts: </span>
|
||||
<span class="sd"> :param type: </span>
|
||||
<span class="sd"> :return: </span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
|
||||
<span class="n">targets</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">targets</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
|
||||
<span class="n">forecasts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="nb">type</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">forecasts</span> <span class="o">-</span> <span class="n">targets</span><span class="p">)</span> <span class="o">/</span> <span class="p">((</span><span class="n">forecasts</span> <span class="o">+</span> <span class="n">targets</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">))</span>
|
||||
<span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">forecasts</span> <span class="o">-</span> <span class="n">targets</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span> <span class="o">+</span> <span class="nb">abs</span><span class="p">(</span><span class="n">targets</span><span class="p">)))</span> <span class="o">*</span> <span class="mi">100</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">forecasts</span> <span class="o">-</span> <span class="n">targets</span><span class="p">))</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">forecasts</span> <span class="o">+</span> <span class="n">targets</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="mape_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.mape_interval">[docs]</a><span class="k">def</span> <span class="nf">mape_interval</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">):</span>
|
||||
<span class="n">fmean</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">forecasts</span><span class="p">]</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">fmean</span> <span class="o">-</span> <span class="n">targets</span><span class="p">)</span> <span class="o">/</span> <span class="n">fmean</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="UStatistic"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.UStatistic">[docs]</a><span class="k">def</span> <span class="nf">UStatistic</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Theil's U Statistic</span>
|
||||
|
||||
<span class="sd"> :param targets: </span>
|
||||
<span class="sd"> :param forecasts: </span>
|
||||
<span class="sd"> :return: </span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
|
||||
<span class="n">forecasts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">forecasts</span><span class="p">])</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">forecasts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
|
||||
<span class="n">targets</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">targets</span><span class="p">])</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">targets</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">targets</span><span class="p">)</span>
|
||||
|
||||
<span class="n">l</span> <span class="o">=</span> <span class="n">forecasts</span><span class="o">.</span><span class="n">size</span>
|
||||
<span class="n">l</span> <span class="o">=</span> <span class="mi">2</span> <span class="k">if</span> <span class="n">l</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">l</span>
|
||||
|
||||
<span class="n">naive</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">y</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="mi">0</span><span class="p">,</span> <span class="n">l</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
|
||||
<span class="n">y</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">subtract</span><span class="p">(</span><span class="n">forecasts</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">targets</span><span class="p">[</span><span class="n">k</span><span class="p">])</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
|
||||
<span class="n">naive</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">subtract</span><span class="p">(</span><span class="n">targets</span><span class="p">[</span><span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">],</span> <span class="n">targets</span><span class="p">[</span><span class="n">k</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="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">divide</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">y</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">naive</span><span class="p">)))</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="TheilsInequality"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.TheilsInequality">[docs]</a><span class="k">def</span> <span class="nf">TheilsInequality</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Theil’s Inequality Coefficient</span>
|
||||
|
||||
<span class="sd"> :param targets: </span>
|
||||
<span class="sd"> :param forecasts: </span>
|
||||
<span class="sd"> :return: </span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">res</span> <span class="o">=</span> <span class="n">targets</span> <span class="o">-</span> <span class="n">forecasts</span>
|
||||
<span class="n">t</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
|
||||
<span class="n">us</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="n">u</span> <span class="o">**</span> <span class="mi">2</span> <span class="k">for</span> <span class="n">u</span> <span class="ow">in</span> <span class="n">res</span><span class="p">]))</span>
|
||||
<span class="n">ys</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="n">y</span> <span class="o">**</span> <span class="mi">2</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">targets</span><span class="p">]))</span>
|
||||
<span class="n">fs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="n">f</span> <span class="o">**</span> <span class="mi">2</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">forecasts</span><span class="p">]))</span>
|
||||
<span class="k">return</span> <span class="n">us</span> <span class="o">/</span> <span class="p">(</span><span class="n">ys</span> <span class="o">+</span> <span class="n">fs</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="sharpness"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.sharpness">[docs]</a><span class="k">def</span> <span class="nf">sharpness</span><span class="p">(</span><span class="n">forecasts</span><span class="p">):</span>
|
||||
<span class="sd">"""Sharpness - Mean size of the intervals"""</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">i</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">forecasts</span><span class="p">]</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="resolution"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.resolution">[docs]</a><span class="k">def</span> <span class="nf">resolution</span><span class="p">(</span><span class="n">forecasts</span><span class="p">):</span>
|
||||
<span class="sd">"""Resolution - Standard deviation of the intervals"""</span>
|
||||
<span class="n">shp</span> <span class="o">=</span> <span class="n">sharpness</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="nb">abs</span><span class="p">((</span><span class="n">i</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">i</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">-</span> <span class="n">shp</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">forecasts</span><span class="p">]</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="coverage"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.coverage">[docs]</a><span class="k">def</span> <span class="nf">coverage</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">):</span>
|
||||
<span class="sd">"""Percent of target values that fall inside forecasted interval"""</span>
|
||||
|
||||
<span class="n">preds</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">i</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)):</span>
|
||||
<span class="k">if</span> <span class="n">targets</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">>=</span> <span class="n">forecasts</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="ow">and</span> <span class="n">targets</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o"><=</span> <span class="n">forecasts</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]:</span>
|
||||
<span class="n">preds</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">preds</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">preds</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="pinball"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.pinball">[docs]</a><span class="k">def</span> <span class="nf">pinball</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">forecast</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Pinball loss function. Measure the distance of forecast to the tau-quantile of the target</span>
|
||||
|
||||
<span class="sd"> :param tau: quantile value in the range (0,1)</span>
|
||||
<span class="sd"> :param target: </span>
|
||||
<span class="sd"> :param forecast: </span>
|
||||
<span class="sd"> :return: float, distance of forecast to the tau-quantile of the target</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">target</span> <span class="o">>=</span> <span class="n">forecast</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">forecast</span><span class="p">)</span> <span class="o">*</span> <span class="n">tau</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">forecast</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">tau</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="pinball_mean"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.pinball_mean">[docs]</a><span class="k">def</span> <span class="nf">pinball_mean</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Mean pinball loss value of the forecast for a given tau-quantile of the targets</span>
|
||||
|
||||
<span class="sd"> :param tau: quantile value in the range (0,1)</span>
|
||||
<span class="sd"> :param targets: list of target values</span>
|
||||
<span class="sd"> :param forecasts: list of prediction intervals</span>
|
||||
<span class="sd"> :return: float, the pinball loss mean for tau quantile</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">tau</span> <span class="o"><=</span> <span class="mf">0.5</span><span class="p">:</span>
|
||||
<span class="n">preds</span> <span class="o">=</span> <span class="p">[</span><span class="n">pinball</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">targets</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">forecasts</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">forecasts</span><span class="p">))]</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">preds</span> <span class="o">=</span> <span class="p">[</span><span class="n">pinball</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">targets</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">forecasts</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">forecasts</span><span class="p">))]</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">preds</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="winkler_score"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.winkler_score">[docs]</a><span class="k">def</span> <span class="nf">winkler_score</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">forecast</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> R. L. Winkler, A Decision-Theoretic Approach to Interval Estimation, J. Am. Stat. Assoc. 67 (337) (1972) 187–191. doi:10.2307/2284720.</span>
|
||||
|
||||
<span class="sd"> :param tau:</span>
|
||||
<span class="sd"> :param target:</span>
|
||||
<span class="sd"> :param forecast:</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">delta</span> <span class="o">=</span> <span class="n">forecast</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">forecast</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
|
||||
<span class="k">if</span> <span class="n">forecast</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o"><=</span> <span class="n">target</span> <span class="o"><=</span> <span class="n">forecast</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
|
||||
<span class="k">return</span> <span class="n">delta</span>
|
||||
<span class="k">elif</span> <span class="n">forecast</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">></span> <span class="n">target</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">delta</span> <span class="o">+</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">forecast</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">target</span><span class="p">))</span> <span class="o">/</span> <span class="n">tau</span>
|
||||
<span class="k">elif</span> <span class="n">forecast</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o"><</span> <span class="n">target</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">delta</span> <span class="o">+</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">target</span> <span class="o">-</span> <span class="n">forecast</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span> <span class="o">/</span> <span class="n">tau</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="winkler_mean"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.winkler_mean">[docs]</a><span class="k">def</span> <span class="nf">winkler_mean</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Mean Winkler score value of the forecast for a given tau-quantile of the targets</span>
|
||||
|
||||
<span class="sd"> :param tau: quantile value in the range (0,1)</span>
|
||||
<span class="sd"> :param targets: list of target values</span>
|
||||
<span class="sd"> :param forecasts: list of prediction intervals</span>
|
||||
<span class="sd"> :return: float, the Winkler score mean for tau quantile</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">preds</span> <span class="o">=</span> <span class="p">[</span><span class="n">winkler_score</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">targets</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">forecasts</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">forecasts</span><span class="p">))]</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">preds</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="brier_score"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.brier_score">[docs]</a><span class="k">def</span> <span class="nf">brier_score</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">densities</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Brier Score for probabilistic forecasts.</span>
|
||||
<span class="sd"> Brier (1950). "Verification of Forecasts Expressed in Terms of Probability". Monthly Weather Review. 78: 1–3.</span>
|
||||
|
||||
<span class="sd"> :param targets: a list with the target values</span>
|
||||
<span class="sd"> :param densities: a list with pyFTS.probabil objectsistic.ProbabilityDistribution</span>
|
||||
<span class="sd"> :return: float</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">densities</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
|
||||
<span class="n">densities</span> <span class="o">=</span> <span class="p">[</span><span class="n">densities</span><span class="p">]</span>
|
||||
<span class="n">targets</span> <span class="o">=</span> <span class="p">[</span><span class="n">targets</span><span class="p">]</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">d</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">densities</span><span class="p">):</span>
|
||||
<span class="k">try</span><span class="p">:</span>
|
||||
<span class="n">v</span> <span class="o">=</span> <span class="n">d</span><span class="o">.</span><span class="n">bin_index</span><span class="o">.</span><span class="n">find_le</span><span class="p">(</span><span class="n">targets</span><span class="p">[</span><span class="n">ct</span><span class="p">])</span>
|
||||
|
||||
<span class="n">score</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="n">d</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">d</span><span class="o">.</span><span class="n">bins</span> <span class="k">if</span> <span class="n">k</span> <span class="o">!=</span> <span class="n">v</span><span class="p">])</span>
|
||||
<span class="n">score</span> <span class="o">+=</span> <span class="p">(</span><span class="n">d</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">score</span><span class="p">)</span>
|
||||
<span class="k">except</span> <span class="ne">ValueError</span> <span class="k">as</span> <span class="n">ex</span><span class="p">:</span>
|
||||
<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">nansum</span><span class="p">([</span><span class="n">d</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">d</span><span class="o">.</span><span class="n">bins</span><span class="p">]))</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">ret</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">ret</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="logarithm_score"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.logarithm_score">[docs]</a><span class="k">def</span> <span class="nf">logarithm_score</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">densities</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Logarithm Score for probabilistic forecasts.</span>
|
||||
<span class="sd"> Good IJ (1952). “Rational Decisions.”Journal of the Royal Statistical Society B,14(1),107–114. URLhttps://www.jstor.org/stable/2984087.</span>
|
||||
|
||||
<span class="sd"> :param targets: a list with the target values</span>
|
||||
<span class="sd"> :param densities: a list with pyFTS.probabil objectsistic.ProbabilityDistribution</span>
|
||||
<span class="sd"> :return: float</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">_ls</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="mf">0.0</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">densities</span><span class="p">,</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">):</span>
|
||||
<span class="n">densities</span> <span class="o">=</span> <span class="p">[</span><span class="n">densities</span><span class="p">]</span>
|
||||
<span class="n">targets</span> <span class="o">=</span> <span class="p">[</span><span class="n">targets</span><span class="p">]</span>
|
||||
|
||||
<span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">densities</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">df</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">densities</span><span class="p">):</span>
|
||||
<span class="n">_ls</span> <span class="o">+=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="n">targets</span><span class="p">[</span><span class="n">ct</span><span class="p">]))</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">_ls</span> <span class="o">/</span> <span class="n">n</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="crps"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.crps">[docs]</a><span class="k">def</span> <span class="nf">crps</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">densities</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Continuous Ranked Probability Score</span>
|
||||
|
||||
<span class="sd"> :param targets: a list with the target values</span>
|
||||
<span class="sd"> :param densities: a list with pyFTS.probabil objectsistic.ProbabilityDistribution</span>
|
||||
<span class="sd"> :return: float</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">_crps</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="mf">0.0</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">densities</span><span class="p">,</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">):</span>
|
||||
<span class="n">densities</span> <span class="o">=</span> <span class="p">[</span><span class="n">densities</span><span class="p">]</span>
|
||||
<span class="n">targets</span> <span class="o">=</span> <span class="p">[</span><span class="n">targets</span><span class="p">]</span>
|
||||
|
||||
<span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">densities</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">n</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">df</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">densities</span><span class="p">):</span>
|
||||
<span class="n">_crps</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([(</span><span class="n">df</span><span class="o">.</span><span class="n">cumulative</span><span class="p">(</span><span class="nb">bin</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="mi">1</span> <span class="k">if</span> <span class="nb">bin</span> <span class="o">>=</span> <span class="n">targets</span><span class="p">[</span><span class="n">ct</span><span class="p">]</span> <span class="k">else</span> <span class="mi">0</span><span class="p">))</span> <span class="o">**</span> <span class="mi">2</span> <span class="k">for</span> <span class="nb">bin</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">bins</span><span class="p">])</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">_crps</span> <span class="o">/</span> <span class="n">n</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="get_point_statistics"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.get_point_statistics">[docs]</a><span class="k">def</span> <span class="nf">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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Condensate all measures for point forecasters</span>
|
||||
|
||||
<span class="sd"> :param data: test data</span>
|
||||
<span class="sd"> :param model: FTS model with point forecasting capability</span>
|
||||
<span class="sd"> :param kwargs:</span>
|
||||
<span class="sd"> :return: a list with the RMSE, SMAPE and U Statistic</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">steps_ahead</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">'steps_ahead'</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">kwargs</span><span class="p">[</span><span class="s1">'type'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'point'</span>
|
||||
|
||||
<span class="n">indexer</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">'indexer'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">indexer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
|
||||
<span class="k">elif</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">):</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Multivariate data must be a Pandas DataFrame!"</span><span class="p">)</span>
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="n">data</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ndata</span> <span class="o">=</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="n">ret</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">steps_ahead</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">forecasts</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">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="ow">and</span> <span class="n">model</span><span class="o">.</span><span class="n">has_seasonality</span><span class="p">:</span>
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
|
||||
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
|
||||
<span class="n">forecasts</span> <span class="o">=</span> <span class="p">[</span><span class="n">forecasts</span><span class="p">]</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</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="o">-</span> <span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span>
|
||||
<span class="n">forecasts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">forecasts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
|
||||
|
||||
<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">round</span><span class="p">(</span><span class="n">rmse</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
|
||||
<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">round</span><span class="p">(</span><span class="n">mape</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
|
||||
<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">round</span><span class="p">(</span><span class="n">UStatistic</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">steps_ahead_sampler</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">'steps_ahead_sampler'</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">nforecasts</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">model</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span> <span class="o">-</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="n">steps_ahead_sampler</span><span class="p">):</span>
|
||||
<span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="n">model</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="n">tmp</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">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="n">nforecasts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
|
||||
|
||||
<span class="n">start</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">+</span> <span class="n">steps_ahead</span> <span class="o">-</span> <span class="mi">1</span>
|
||||
<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">round</span><span class="p">(</span><span class="n">rmse</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span><span class="n">steps_ahead_sampler</span><span class="p">],</span> <span class="n">nforecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
|
||||
<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">round</span><span class="p">(</span><span class="n">mape</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span><span class="n">steps_ahead_sampler</span><span class="p">],</span> <span class="n">nforecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
|
||||
<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">round</span><span class="p">(</span><span class="n">UStatistic</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span><span class="n">steps_ahead_sampler</span><span class="p">],</span> <span class="n">nforecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="get_point_ahead_statistics"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.get_point_ahead_statistics">[docs]</a><span class="k">def</span> <span class="nf">get_point_ahead_statistics</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Condensate all measures for point forecasters</span>
|
||||
|
||||
<span class="sd"> :param data: test data</span>
|
||||
<span class="sd"> :param model: FTS model with point forecasting capability</span>
|
||||
<span class="sd"> :param kwargs:</span>
|
||||
<span class="sd"> :return: a list with the RMSE, SMAPE and U Statistic</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
|
||||
|
||||
<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="n">l</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">"Data and intervals have different lenghts!"</span><span class="p">)</span>
|
||||
|
||||
<span class="n">lags</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">lag</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">):</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="n">datum</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span>
|
||||
<span class="n">forecast</span> <span class="o">=</span> <span class="n">forecasts</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span>
|
||||
<span class="n">ret</span><span class="p">[</span><span class="s1">'steps'</span><span class="p">]</span> <span class="o">=</span> <span class="n">lag</span>
|
||||
<span class="n">ret</span><span class="p">[</span><span class="s1">'method'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">''</span>
|
||||
<span class="n">ret</span><span class="p">[</span><span class="s1">'rmse'</span><span class="p">]</span> <span class="o">=</span> <span class="n">rmse</span><span class="p">(</span><span class="n">datum</span><span class="p">,</span> <span class="n">forecast</span><span class="p">)</span>
|
||||
<span class="n">ret</span><span class="p">[</span><span class="s1">'mape'</span><span class="p">]</span> <span class="o">=</span> <span class="n">mape</span><span class="p">(</span><span class="n">datum</span><span class="p">,</span> <span class="n">forecast</span><span class="p">)</span>
|
||||
<span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">lag</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span><span class="n">lag</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span> <span class="k">if</span> <span class="n">lag</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="p">[</span><span class="n">datum</span><span class="p">,</span> <span class="n">datum</span><span class="p">]</span>
|
||||
<span class="n">ret</span><span class="p">[</span><span class="s1">'u'</span><span class="p">]</span> <span class="o">=</span> <span class="n">UStatistic</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">forecast</span><span class="p">)</span>
|
||||
<span class="n">lags</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span> <span class="o">=</span> <span class="n">ret</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">lags</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="get_interval_statistics"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.get_interval_statistics">[docs]</a><span class="k">def</span> <span class="nf">get_interval_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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Condensate all measures for point interval forecasters</span>
|
||||
|
||||
<span class="sd"> :param data: test data</span>
|
||||
<span class="sd"> :param model: FTS model with interval forecasting capability</span>
|
||||
<span class="sd"> :param kwargs:</span>
|
||||
<span class="sd"> :return: a list with the sharpness, resolution, coverage, .05 pinball mean,</span>
|
||||
<span class="sd"> .25 pinball mean, .75 pinball mean and .95 pinball mean.</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">steps_ahead</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">'steps_ahead'</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">kwargs</span><span class="p">[</span><span class="s1">'type'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'interval'</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">steps_ahead</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">forecasts</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">data</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">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">sharpness</span><span class="p">(</span><span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">resolution</span><span class="p">(</span><span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
|
||||
<span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</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="n">model</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">coverage</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">pinball_mean</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">pinball_mean</span><span class="p">(</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">pinball_mean</span><span class="p">(</span><span class="mf">0.75</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">pinball_mean</span><span class="p">(</span><span class="mf">0.95</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">winkler_mean</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">winkler_mean</span><span class="p">(</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">forecasts</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">model</span><span class="o">.</span><span class="n">order</span><span class="p">,</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="n">steps_ahead</span><span class="p">):</span>
|
||||
<span class="n">sample</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">model</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="n">tmp</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">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="n">forecasts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
|
||||
|
||||
<span class="n">start</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">+</span> <span class="n">steps_ahead</span> <span class="o">-</span> <span class="mi">1</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">sharpness</span><span class="p">(</span><span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">resolution</span><span class="p">(</span><span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
|
||||
<span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</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="n">model</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">coverage</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">pinball_mean</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">pinball_mean</span><span class="p">(</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">pinball_mean</span><span class="p">(</span><span class="mf">0.75</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">pinball_mean</span><span class="p">(</span><span class="mf">0.95</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">winkler_mean</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">winkler_mean</span><span class="p">(</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="get_interval_ahead_statistics"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.get_interval_ahead_statistics">[docs]</a><span class="k">def</span> <span class="nf">get_interval_ahead_statistics</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">intervals</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Condensate all measures for point interval forecasters</span>
|
||||
|
||||
<span class="sd"> :param data: test data</span>
|
||||
<span class="sd"> :param intervals: predicted intervals for each datapoint</span>
|
||||
<span class="sd"> :param kwargs:</span>
|
||||
<span class="sd"> :return: a list with the sharpness, resolution, coverage, .05 pinball mean,</span>
|
||||
<span class="sd"> .25 pinball mean, .75 pinball mean and .95 pinball mean.</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">intervals</span><span class="p">)</span>
|
||||
|
||||
<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="n">l</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">"Data and intervals have different lenghts!"</span><span class="p">)</span>
|
||||
|
||||
<span class="n">lags</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">lag</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">):</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="n">datum</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span>
|
||||
<span class="n">interval</span> <span class="o">=</span> <span class="n">intervals</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span>
|
||||
<span class="n">ret</span><span class="p">[</span><span class="s1">'steps'</span><span class="p">]</span> <span class="o">=</span> <span class="n">lag</span>
|
||||
<span class="n">ret</span><span class="p">[</span><span class="s1">'method'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">''</span>
|
||||
<span class="n">ret</span><span class="p">[</span><span class="s1">'sharpness'</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">interval</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">interval</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">2</span><span class="p">)</span>
|
||||
<span class="n">ret</span><span class="p">[</span><span class="s1">'coverage'</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">interval</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o"><=</span> <span class="n">datum</span> <span class="o"><=</span> <span class="n">interval</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">else</span> <span class="mi">0</span>
|
||||
<span class="n">ret</span><span class="p">[</span><span class="s1">'pinball05'</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">pinball</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">datum</span><span class="p">,</span> <span class="n">interval</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="mi">2</span><span class="p">)</span>
|
||||
<span class="n">ret</span><span class="p">[</span><span class="s1">'pinball25'</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">pinball</span><span class="p">(</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">datum</span><span class="p">,</span> <span class="n">interval</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="mi">2</span><span class="p">)</span>
|
||||
<span class="n">ret</span><span class="p">[</span><span class="s1">'pinball75'</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">pinball</span><span class="p">(</span><span class="mf">0.75</span><span class="p">,</span> <span class="n">datum</span><span class="p">,</span> <span class="n">interval</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">2</span><span class="p">)</span>
|
||||
<span class="n">ret</span><span class="p">[</span><span class="s1">'pinball95'</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">pinball</span><span class="p">(</span><span class="mf">0.95</span><span class="p">,</span> <span class="n">datum</span><span class="p">,</span> <span class="n">interval</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">2</span><span class="p">)</span>
|
||||
<span class="n">ret</span><span class="p">[</span><span class="s1">'winkler05'</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">winkler_score</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">datum</span><span class="p">,</span> <span class="n">interval</span><span class="p">),</span> <span class="mi">2</span><span class="p">)</span>
|
||||
<span class="n">ret</span><span class="p">[</span><span class="s1">'winkler25'</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">winkler_score</span><span class="p">(</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">datum</span><span class="p">,</span> <span class="n">interval</span><span class="p">),</span> <span class="mi">2</span><span class="p">)</span>
|
||||
<span class="n">lags</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span> <span class="o">=</span> <span class="n">ret</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">lags</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="get_distribution_statistics"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.get_distribution_statistics">[docs]</a><span class="k">def</span> <span class="nf">get_distribution_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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Get CRPS statistic and time for a forecasting model</span>
|
||||
|
||||
<span class="sd"> :param data: test data</span>
|
||||
<span class="sd"> :param model: FTS model with probabilistic forecasting capability</span>
|
||||
<span class="sd"> :param kwargs:</span>
|
||||
<span class="sd"> :return: a list with the CRPS and execution time</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">steps_ahead</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">'steps_ahead'</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">kwargs</span><span class="p">[</span><span class="s1">'type'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'distribution'</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">steps_ahead</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">_s1</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">forecasts</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">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="n">_e1</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="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</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="n">model</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">crps</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">3</span><span class="p">))</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">_e1</span> <span class="o">-</span> <span class="n">_s1</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">brier_score</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">3</span><span class="p">))</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">skip</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">'steps_ahead_sampler'</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">forecasts</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">_s1</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="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">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</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="n">steps_ahead</span><span class="p">,</span> <span class="n">skip</span><span class="p">):</span>
|
||||
<span class="n">sample</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">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
|
||||
<span class="n">tmp</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">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="n">forecasts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="n">_e1</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">start</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">+</span> <span class="n">steps_ahead</span>
|
||||
<span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</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="n">model</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">crps</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span><span class="n">skip</span><span class="p">],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">3</span><span class="p">))</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">_e1</span> <span class="o">-</span> <span class="n">_s1</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">brier_score</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span><span class="n">skip</span><span class="p">],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">3</span><span class="p">))</span>
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="get_distribution_ahead_statistics"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.get_distribution_ahead_statistics">[docs]</a><span class="k">def</span> <span class="nf">get_distribution_ahead_statistics</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">distributions</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Get CRPS statistic and time for a forecasting model</span>
|
||||
|
||||
<span class="sd"> :param data: test data</span>
|
||||
<span class="sd"> :param model: FTS model with probabilistic forecasting capability</span>
|
||||
<span class="sd"> :param kwargs:</span>
|
||||
<span class="sd"> :return: a list with the CRPS and execution time</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">distributions</span><span class="p">)</span>
|
||||
|
||||
<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="n">l</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">"Data and distributions have different lenghts!"</span><span class="p">)</span>
|
||||
|
||||
<span class="n">lags</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">lag</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">):</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="n">datum</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span>
|
||||
<span class="n">dist</span> <span class="o">=</span> <span class="n">distributions</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span>
|
||||
<span class="n">ret</span><span class="p">[</span><span class="s1">'steps'</span><span class="p">]</span> <span class="o">=</span> <span class="n">lag</span>
|
||||
<span class="n">ret</span><span class="p">[</span><span class="s1">'method'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">''</span>
|
||||
<span class="n">ret</span><span class="p">[</span><span class="s1">'crps'</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">crps</span><span class="p">(</span><span class="n">datum</span><span class="p">,</span> <span class="n">dist</span><span class="p">),</span> <span class="mi">3</span><span class="p">)</span>
|
||||
<span class="n">ret</span><span class="p">[</span><span class="s1">'brier'</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">brier_score</span><span class="p">(</span><span class="n">datum</span><span class="p">,</span> <span class="n">dist</span><span class="p">),</span> <span class="mi">3</span><span class="p">)</span>
|
||||
<span class="n">ret</span><span class="p">[</span><span class="s1">'log'</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">logarithm_score</span><span class="p">(</span><span class="n">datum</span><span class="p">,</span> <span class="n">dist</span><span class="p">),</span> <span class="mi">3</span><span class="p">)</span>
|
||||
<span class="n">lags</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span> <span class="o">=</span> <span class="n">ret</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">lags</span></div>
|
||||
</pre></div>
|
||||
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<h1>Source code for pyFTS.benchmarks.ResidualAnalysis</h1><div class="highlight"><pre>
|
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<span></span><span class="ch">#!/usr/bin/python</span>
|
||||
<span class="c1"># -*- coding: utf8 -*-</span>
|
||||
|
||||
<span class="sd">"""Residual Analysis methods"""</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">matplotlib</span> <span class="k">as</span> <span class="nn">plt</span>
|
||||
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">Transformations</span><span class="p">,</span><span class="n">Util</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.benchmarks</span> <span class="k">import</span> <span class="n">Measures</span>
|
||||
<span class="kn">from</span> <span class="nn">scipy</span> <span class="k">import</span> <span class="n">stats</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="residuals"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.ResidualAnalysis.residuals">[docs]</a><span class="k">def</span> <span class="nf">residuals</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
|
||||
<span class="sd">"""First order residuals"""</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">targets</span><span class="p">[</span><span class="n">order</span><span class="p">:])</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ljung_box_test"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.ResidualAnalysis.ljung_box_test">[docs]</a><span class="k">def</span> <span class="nf">ljung_box_test</span><span class="p">(</span><span class="n">residuals</span><span class="p">,</span> <span class="n">lags</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">):</span>
|
||||
<span class="kn">from</span> <span class="nn">statsmodels.stats.diagnostic</span> <span class="k">import</span> <span class="n">acorr_ljungbox</span>
|
||||
<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="k">import</span> <span class="n">chi2</span>
|
||||
|
||||
<span class="n">stat</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="n">acorr_ljungbox</span><span class="p">(</span><span class="n">residuals</span><span class="p">,</span> <span class="n">lags</span><span class="o">=</span><span class="n">lags</span><span class="p">)</span>
|
||||
|
||||
<span class="n">rows</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">Q</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">stat</span><span class="p">):</span>
|
||||
<span class="n">lag</span> <span class="o">=</span> <span class="n">ct</span><span class="o">+</span><span class="mi">1</span>
|
||||
<span class="n">p_value</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">chi2</span><span class="o">.</span><span class="n">cdf</span><span class="p">(</span><span class="n">Q</span><span class="p">,</span> <span class="n">df</span><span class="o">=</span><span class="n">lag</span><span class="p">)</span>
|
||||
<span class="n">critical_value</span> <span class="o">=</span> <span class="n">chi2</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">alpha</span><span class="p">,</span> <span class="n">df</span><span class="o">=</span><span class="n">lag</span><span class="p">)</span>
|
||||
<span class="n">rows</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">lag</span><span class="p">,</span> <span class="n">Q</span><span class="p">,</span> <span class="n">p_value</span><span class="p">,</span> <span class="n">critical_value</span><span class="p">,</span> <span class="s1">'H0 accepted'</span> <span class="k">if</span> <span class="n">Q</span> <span class="o">></span> <span class="n">critical_value</span> <span class="k">else</span> <span class="s1">'H0 rejected'</span><span class="p">])</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'Lag'</span><span class="p">,</span><span class="s1">'Statistic'</span><span class="p">,</span><span class="s1">'p-Value'</span><span class="p">,</span><span class="s1">'Critical Value'</span><span class="p">,</span> <span class="s1">'Result'</span><span class="p">])</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="compare_residuals"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.ResidualAnalysis.compare_residuals">[docs]</a><span class="k">def</span> <span class="nf">compare_residuals</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">alpha</span><span class="o">=.</span><span class="mi">05</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Compare residual's statistics of several models</span>
|
||||
|
||||
<span class="sd"> :param data: test data</span>
|
||||
<span class="sd"> :param models: </span>
|
||||
<span class="sd"> :return: a Pandas dataframe with the Box-Ljung statistic for each model</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="kn">from</span> <span class="nn">statsmodels.stats.diagnostic</span> <span class="k">import</span> <span class="n">acorr_ljungbox</span>
|
||||
<span class="n">rows</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"Model"</span><span class="p">,</span><span class="s2">"Order"</span><span class="p">,</span><span class="s2">"AVG"</span><span class="p">,</span><span class="s2">"STD"</span><span class="p">,</span><span class="s2">"Box-Ljung"</span><span class="p">,</span><span class="s2">"p-value"</span><span class="p">,</span><span class="s2">"Result"</span><span class="p">]</span>
|
||||
<span class="k">for</span> <span class="n">mfts</span> <span class="ow">in</span> <span class="n">models</span><span class="p">:</span>
|
||||
<span class="n">forecasts</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
<span class="n">res</span> <span class="o">=</span> <span class="n">residuals</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">forecasts</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="mi">1</span><span class="p">)</span>
|
||||
<span class="n">mu</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
|
||||
<span class="n">sig</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
|
||||
<span class="n">row</span> <span class="o">=</span> <span class="p">[</span><span class="n">mfts</span><span class="o">.</span><span class="n">shortname</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="n">mu</span><span class="p">,</span> <span class="n">sig</span><span class="p">]</span>
|
||||
<span class="n">stat</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="n">acorr_ljungbox</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
|
||||
<span class="n">test</span> <span class="o">=</span> <span class="s1">'H0 Accepted'</span> <span class="k">if</span> <span class="n">pval</span> <span class="o">></span> <span class="n">alpha</span> <span class="k">else</span> <span class="s1">'H0 Rejected'</span>
|
||||
<span class="n">row</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">stat</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">test</span><span class="p">])</span>
|
||||
<span class="n">rows</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">columns</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="plot_residuals_by_model"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.ResidualAnalysis.plot_residuals_by_model">[docs]</a><span class="k">def</span> <span class="nf">plot_residuals_by_model</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">models</span><span class="p">,</span> <span class="n">tam</span><span class="o">=</span><span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</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="kn">import</span> <span class="nn">scipy</span> <span class="k">as</span> <span class="nn">sp</span>
|
||||
|
||||
<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">models</span><span class="p">),</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">tam</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">c</span><span class="p">,</span> <span class="n">mfts</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">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">models</span><span class="p">)</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">ax</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="n">c</span><span class="p">]</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ax</span> <span class="o">=</span> <span class="n">axes</span>
|
||||
<span class="n">forecasts</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">targets</span><span class="p">)</span>
|
||||
<span class="n">res</span> <span class="o">=</span> <span class="n">residuals</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</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="n">mu</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
|
||||
<span class="n">sig</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">c</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Residuals"</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">'large'</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">'large'</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">' '</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">c</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Autocorrelation"</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">'large'</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'ACS'</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Lag'</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">acorr</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">c</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> <span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Histogram"</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">'large'</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Freq'</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Bins'</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">c</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> <span class="n">ax</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"QQ Plot"</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">'large'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">_</span><span class="p">,</span> <span class="p">(</span><span class="n">__</span><span class="p">,</span> <span class="n">___</span><span class="p">,</span> <span class="n">r</span><span class="p">)</span> <span class="o">=</span> <span class="n">sp</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">probplot</span><span class="p">(</span><span class="n">res</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">fit</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
|
||||
|
||||
<span class="n">Util</span><span class="o">.</span><span class="n">show_and_save_image</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">save</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="single_plot_residuals"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.ResidualAnalysis.single_plot_residuals">[docs]</a><span class="k">def</span> <span class="nf">single_plot_residuals</span><span class="p">(</span><span class="n">res</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">tam</span><span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">7</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="kn">import</span> <span class="nn">scipy</span> <span class="k">as</span> <span class="nn">sp</span>
|
||||
|
||||
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">tam</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ax</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="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Residuals"</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">'large'</span><span class="p">)</span>
|
||||
<span class="n">ax</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="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Autocorrelation"</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">'large'</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'ACF'</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Lag'</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">acorr</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"Histogram"</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">'large'</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Freq'</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Bins'</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
|
||||
|
||||
<span class="n">_</span><span class="p">,</span> <span class="p">(</span><span class="n">__</span><span class="p">,</span> <span class="n">___</span><span class="p">,</span> <span class="n">r</span><span class="p">)</span> <span class="o">=</span> <span class="n">sp</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">probplot</span><span class="p">(</span><span class="n">res</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="n">ax</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">fit</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
|
||||
|
||||
<span class="n">Util</span><span class="o">.</span><span class="n">show_and_save_image</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">save</span><span class="p">)</span></div>
|
||||
</pre></div>
|
||||
|
||||
<div class="clearer"></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="sphinxsidebar" role="navigation" aria-label="main navigation">
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<div class="sphinxsidebarwrapper">
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<div id="searchbox" style="display: none" role="search">
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<h3 id="searchlabel">Quick search</h3>
|
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<div class="searchformwrapper">
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<form class="search" action="../../../search.html" method="get">
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<input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
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<h3>Navigation</h3>
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<li class="right" style="margin-right: 10px">
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<a href="../../../genindex.html" title="General Index"
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>index</a></li>
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<li class="right" >
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<a href="../../../py-modindex.html" title="Python Module Index"
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>modules</a> |</li>
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<li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.7 documentation</a> »</li>
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<li class="nav-item nav-item-1"><a href="../../index.html" >Module code</a> »</li>
|
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© Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
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Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
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254
docs/build/html/_modules/pyFTS/benchmarks/Tests.html
vendored
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vendored
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|
||||
<h1>Source code for pyFTS.benchmarks.Tests</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 class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.benchmarks.Measures</span> <span class="k">import</span> <span class="n">acf</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="BoxPierceStatistic"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Tests.BoxPierceStatistic">[docs]</a><span class="k">def</span> <span class="nf">BoxPierceStatistic</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">h</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Q Statistic for Box-Pierce test</span>
|
||||
|
||||
<span class="sd"> :param data:</span>
|
||||
<span class="sd"> :param h:</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
<span class="n">s</span> <span class="o">=</span> <span class="mi">0</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="mi">1</span><span class="p">,</span> <span class="n">h</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
|
||||
<span class="n">r</span> <span class="o">=</span> <span class="n">acf</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span>
|
||||
<span class="n">s</span> <span class="o">+=</span> <span class="n">r</span> <span class="o">**</span> <span class="mi">2</span>
|
||||
<span class="k">return</span> <span class="n">n</span> <span class="o">*</span> <span class="n">s</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="BoxLjungStatistic"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Tests.BoxLjungStatistic">[docs]</a><span class="k">def</span> <span class="nf">BoxLjungStatistic</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">h</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Q Statistic for Ljung–Box test</span>
|
||||
|
||||
<span class="sd"> :param data:</span>
|
||||
<span class="sd"> :param h:</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
<span class="n">s</span> <span class="o">=</span> <span class="mi">0</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="mi">1</span><span class="p">,</span> <span class="n">h</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
|
||||
<span class="n">r</span> <span class="o">=</span> <span class="n">acf</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span>
|
||||
<span class="n">s</span> <span class="o">+=</span> <span class="n">r</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">/</span> <span class="p">(</span><span class="n">n</span> <span class="o">-</span> <span class="n">k</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">n</span> <span class="o">*</span> <span class="p">(</span><span class="n">n</span> <span class="o">-</span> <span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="n">s</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="format_experiment_table"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Tests.format_experiment_table">[docs]</a><span class="k">def</span> <span class="nf">format_experiment_table</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">exclude</span><span class="o">=</span><span class="p">[],</span> <span class="n">replace</span><span class="o">=</span><span class="p">{},</span> <span class="n">csv</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">std</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="n">rows</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">columns</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">datasets</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">Dataset</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
|
||||
<span class="n">models</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">Model</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
|
||||
<span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">models</span><span class="p">:</span>
|
||||
<span class="n">test</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">([</span><span class="n">model</span><span class="o">.</span><span class="n">rfind</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">exclude</span><span class="p">])</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">exclude</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="kc">False</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">test</span><span class="p">:</span>
|
||||
<span class="n">columns</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">dataset</span> <span class="ow">in</span> <span class="n">datasets</span><span class="p">:</span>
|
||||
<span class="n">row</span> <span class="o">=</span> <span class="p">[</span><span class="n">dataset</span><span class="p">]</span>
|
||||
<span class="k">if</span> <span class="n">std</span><span class="p">:</span>
|
||||
<span class="n">row_std</span> <span class="o">=</span> <span class="p">[</span><span class="n">dataset</span><span class="p">]</span>
|
||||
<span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">columns</span><span class="p">:</span>
|
||||
<span class="n">avg</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmin</span><span class="p">(</span><span class="n">df</span><span class="p">[(</span><span class="n">df</span><span class="o">.</span><span class="n">Dataset</span> <span class="o">==</span> <span class="n">dataset</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">Model</span> <span class="o">==</span> <span class="n">model</span><span class="p">)][</span><span class="s2">"AVG"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
|
||||
<span class="n">row</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">avg</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
|
||||
<span class="k">if</span> <span class="n">std</span><span class="p">:</span>
|
||||
<span class="n">_std</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmin</span><span class="p">(</span><span class="n">df</span><span class="p">[(</span><span class="n">df</span><span class="o">.</span><span class="n">Dataset</span> <span class="o">==</span> <span class="n">dataset</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">Model</span> <span class="o">==</span> <span class="n">model</span><span class="p">)][</span><span class="s2">"STD"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
|
||||
<span class="n">row_std</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">"("</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">_std</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="o">+</span> <span class="s2">")"</span><span class="p">)</span>
|
||||
|
||||
<span class="n">rows</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">std</span><span class="p">:</span>
|
||||
<span class="n">rows</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">row_std</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">columns</span><span class="p">)):</span>
|
||||
<span class="k">if</span> <span class="n">columns</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="ow">in</span> <span class="n">replace</span><span class="p">:</span>
|
||||
<span class="n">columns</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">replace</span><span class="p">[</span><span class="n">columns</span><span class="p">[</span><span class="n">k</span><span class="p">]]</span>
|
||||
|
||||
<span class="n">columns</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="s2">"dataset"</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">csv</span><span class="p">:</span>
|
||||
<span class="n">header</span> <span class="o">=</span> <span class="s2">""</span>
|
||||
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">columns</span><span class="p">)):</span>
|
||||
<span class="k">if</span> <span class="n">k</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">header</span> <span class="o">+=</span> <span class="s2">","</span>
|
||||
<span class="n">header</span> <span class="o">+=</span> <span class="n">columns</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
|
||||
|
||||
<span class="n">body</span> <span class="o">=</span> <span class="s2">""</span>
|
||||
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">rows</span><span class="p">)):</span>
|
||||
<span class="n">row</span> <span class="o">=</span> <span class="s2">""</span>
|
||||
<span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">rows</span><span class="p">[</span><span class="n">k</span><span class="p">])):</span>
|
||||
<span class="k">if</span> <span class="n">w</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">row</span> <span class="o">+=</span> <span class="s2">","</span>
|
||||
<span class="n">row</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">rows</span><span class="p">[</span><span class="n">k</span><span class="p">][</span><span class="n">w</span><span class="p">])</span>
|
||||
<span class="n">body</span> <span class="o">+=</span> <span class="s1">'</span><span class="se">\n</span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">header</span> <span class="o">+</span> <span class="n">body</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">columns</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="test_mean_equality"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Tests.test_mean_equality">[docs]</a><span class="k">def</span> <span class="nf">test_mean_equality</span><span class="p">(</span><span class="n">tests</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=.</span><span class="mi">05</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">'friedman'</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Test for the equality of the means, with alpha confidence level.</span>
|
||||
|
||||
<span class="sd"> H_0: There's no significant difference between the means</span>
|
||||
<span class="sd"> H_1: There is at least one significant difference between the means</span>
|
||||
|
||||
<span class="sd"> :param tests:</span>
|
||||
<span class="sd"> :param alpha:</span>
|
||||
<span class="sd"> :param method:</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="kn">from</span> <span class="nn">stac.stac</span> <span class="k">import</span> <span class="n">nonparametric_tests</span> <span class="k">as</span> <span class="n">npt</span>
|
||||
|
||||
<span class="n">methods</span> <span class="o">=</span> <span class="n">tests</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
|
||||
|
||||
<span class="n">values</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">methods</span><span class="p">:</span>
|
||||
<span class="n">values</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tests</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">method</span><span class="o">==</span><span class="s1">'quade'</span><span class="p">:</span>
|
||||
<span class="n">f_value</span><span class="p">,</span> <span class="n">p_value</span><span class="p">,</span> <span class="n">rankings</span><span class="p">,</span> <span class="n">pivots</span> <span class="o">=</span> <span class="n">npt</span><span class="o">.</span><span class="n">quade_test</span><span class="p">(</span><span class="o">*</span><span class="n">values</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">method</span><span class="o">==</span><span class="s1">'friedman'</span><span class="p">:</span>
|
||||
<span class="n">f_value</span><span class="p">,</span> <span class="n">p_value</span><span class="p">,</span> <span class="n">rankings</span><span class="p">,</span> <span class="n">pivots</span> <span class="o">=</span> <span class="n">npt</span><span class="o">.</span><span class="n">friedman_aligned_ranks_test</span><span class="p">(</span><span class="o">*</span><span class="n">values</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">'Unknown test method!'</span><span class="p">)</span>
|
||||
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"F-Value: </span><span class="si">{}</span><span class="s2"> </span><span class="se">\t</span><span class="s2">p-Value: </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">f_value</span><span class="p">,</span> <span class="n">p_value</span><span class="p">))</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">p_value</span> <span class="o"><</span> <span class="n">alpha</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">H0 is rejected!</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">H0 is accepted!</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
|
||||
|
||||
<span class="n">post_hoc</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="n">rows</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="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">methods</span><span class="p">)):</span>
|
||||
<span class="n">rows</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">methods</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">rankings</span><span class="p">[</span><span class="n">k</span><span class="p">]])</span>
|
||||
<span class="n">post_hoc</span><span class="p">[</span><span class="n">methods</span><span class="p">[</span><span class="n">k</span><span class="p">]]</span> <span class="o">=</span> <span class="n">pivots</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
|
||||
|
||||
<span class="k">return</span> <span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'METHOD'</span><span class="p">,</span> <span class="s1">'RANK'</span><span class="p">])</span><span class="o">.</span><span class="n">sort_values</span><span class="p">([</span><span class="s1">'RANK'</span><span class="p">]),</span> <span class="n">post_hoc</span><span class="p">]</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="post_hoc_tests"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Tests.post_hoc_tests">[docs]</a><span class="k">def</span> <span class="nf">post_hoc_tests</span><span class="p">(</span><span class="n">post_hoc</span><span class="p">,</span> <span class="n">control_method</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=.</span><span class="mi">05</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">'finner'</span><span class="p">):</span>
|
||||
<span class="sd">'''</span>
|
||||
<span class="sd"> Finner paired post-hoc test with NSFTS as control method.</span>
|
||||
|
||||
<span class="sd"> $H_0$: There is no significant difference between the means</span>
|
||||
|
||||
<span class="sd"> $H_1$: There is a significant difference between the means</span>
|
||||
|
||||
<span class="sd"> :param post_hoc:</span>
|
||||
<span class="sd"> :param control_method:</span>
|
||||
<span class="sd"> :param alpha:</span>
|
||||
<span class="sd"> :param method:</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> '''</span>
|
||||
<span class="kn">from</span> <span class="nn">stac.stac</span> <span class="k">import</span> <span class="n">nonparametric_tests</span> <span class="k">as</span> <span class="n">npt</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">'bonferroni_dunn'</span><span class="p">:</span>
|
||||
<span class="n">comparisons</span><span class="p">,</span> <span class="n">z_values</span><span class="p">,</span> <span class="n">p_values</span><span class="p">,</span> <span class="n">adj_p_values</span> <span class="o">=</span> <span class="n">npt</span><span class="o">.</span><span class="n">bonferroni_dunn_test</span><span class="p">(</span><span class="n">post_hoc</span><span class="p">,</span><span class="n">control_method</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">'holm'</span><span class="p">:</span>
|
||||
<span class="n">comparisons</span><span class="p">,</span> <span class="n">z_values</span><span class="p">,</span> <span class="n">p_values</span><span class="p">,</span> <span class="n">adj_p_values</span> <span class="o">=</span> <span class="n">npt</span><span class="o">.</span><span class="n">holm_test</span><span class="p">(</span><span class="n">post_hoc</span><span class="p">,</span><span class="n">control_method</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">'finner'</span><span class="p">:</span>
|
||||
<span class="n">comparisons</span><span class="p">,</span> <span class="n">z_values</span><span class="p">,</span> <span class="n">p_values</span><span class="p">,</span> <span class="n">adj_p_values</span> <span class="o">=</span> <span class="n">npt</span><span class="o">.</span><span class="n">finner_test</span><span class="p">(</span><span class="n">post_hoc</span><span class="p">,</span> <span class="n">control_method</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">'Unknown test method!'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">rows</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="nb">len</span><span class="p">(</span><span class="n">comparisons</span><span class="p">)):</span>
|
||||
<span class="n">test</span> <span class="o">=</span> <span class="s1">'H0 Accepted'</span> <span class="k">if</span> <span class="n">adj_p_values</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">></span> <span class="n">alpha</span> <span class="k">else</span> <span class="s1">'H0 Rejected'</span>
|
||||
<span class="n">rows</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">comparisons</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">z_values</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">p_values</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">adj_p_values</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">test</span><span class="p">])</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'COMPARISON'</span><span class="p">,</span> <span class="s1">'Z-VALUE'</span><span class="p">,</span> <span class="s1">'P-VALUE'</span><span class="p">,</span> <span class="s1">'ADJUSTED P-VALUE'</span><span class="p">,</span> <span class="s1">'Result'</span><span class="p">])</span></div>
|
||||
|
||||
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|
||||
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<h1>Source code for pyFTS.benchmarks.arima</h1><div class="highlight"><pre>
|
||||
<span></span><span class="ch">#!/usr/bin/python</span>
|
||||
<span class="c1"># -*- coding: utf8 -*-</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">from</span> <span class="nn">statsmodels.tsa.arima_model</span> <span class="k">import</span> <span class="n">ARIMA</span> <span class="k">as</span> <span class="n">stats_arima</span>
|
||||
<span class="kn">import</span> <span class="nn">scipy.stats</span> <span class="k">as</span> <span class="nn">st</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">SortedCollection</span><span class="p">,</span> <span class="n">fts</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="k">import</span> <span class="n">ProbabilityDistribution</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ARIMA"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA">[docs]</a><span class="k">class</span> <span class="nc">ARIMA</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Façade for statsmodels.tsa.arima_model</span>
|
||||
<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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">ARIMA</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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"ARIMA"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">"Auto Regressive Integrated Moving Average"</span>
|
||||
<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>
|
||||
<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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">trained_data</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">d</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">benchmark_only</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">order</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">"order"</span><span class="p">,</span> <span class="p">(</span><span class="mi">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="bp">self</span><span class="o">.</span><span class="n">_decompose_order</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_decompose_order</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">order</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">order</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">set</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="n">order</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">d</span> <span class="o">=</span> <span class="n">order</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">=</span> <span class="n">order</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">+</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">d</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">"ARIMA(</span><span class="si">{}</span><span class="s2">,</span><span class="si">{}</span><span class="s2">,</span><span class="si">{}</span><span class="s2">)-</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">d</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="ARIMA.train"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.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>
|
||||
|
||||
<span class="k">if</span> <span class="s1">'order'</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
|
||||
<span class="n">order</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'order'</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_decompose_order</span><span class="p">(</span><span class="n">order</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
|
||||
<span class="k">try</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">stats_arima</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">d</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="p">))</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span> <span class="o">=</span> <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">disp</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">ex</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">ex</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span> <span class="o">=</span> <span class="kc">None</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ARIMA.ar"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.ar">[docs]</a> <span class="k">def</span> <span class="nf">ar</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="k">return</span> <span class="n">data</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span><span class="o">.</span><span class="n">arparams</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ARIMA.ma"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.ma">[docs]</a> <span class="k">def</span> <span class="nf">ma</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="k">return</span> <span class="n">data</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span><span class="o">.</span><span class="n">maparams</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ARIMA.forecast"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.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">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
|
||||
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ndata</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">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">ar</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">ar</span><span class="p">(</span><span class="n">ndata</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">p</span><span class="p">:</span> <span class="n">k</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="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">)])</span> <span class="c1">#+1 to forecast one step ahead given all available lags</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">residuals</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="o">-</span><span class="mi">1</span><span class="p">:]</span> <span class="o">-</span> <span class="n">ar</span>
|
||||
|
||||
<span class="n">ma</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">ma</span><span class="p">(</span><span class="n">residuals</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">q</span><span class="p">:</span> <span class="n">k</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="bp">self</span><span class="o">.</span><span class="n">q</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">residuals</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)])</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">ar</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:]</span> <span class="o">+</span> <span class="n">ma</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">ret</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="p">:]</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">ar</span>
|
||||
|
||||
<span class="c1">#ret = self.apply_inverse_transformations(ret, params=[data[self.order - 1:]]) nforecasts = np.array(forecasts)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ARIMA.forecast_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.forecast_interval">[docs]</a> <span class="k">def</span> <span class="nf">forecast_interval</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>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
|
||||
|
||||
<span class="k">if</span> <span class="s1">'alpha'</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</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="s1">'alpha'</span><span class="p">,</span><span class="mf">0.05</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">alpha</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span>
|
||||
|
||||
<span class="n">sigma</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span><span class="o">.</span><span class="n">sigma2</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">data</span><span class="p">)</span>
|
||||
|
||||
<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="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">sample</span> <span class="o">=</span> <span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</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">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="n">mean</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">mean</span><span class="p">,(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
|
||||
<span class="n">mean</span> <span class="o">=</span> <span class="n">mean</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
|
||||
|
||||
<span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mean</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">sigma</span><span class="p">)</span>
|
||||
<span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mean</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">sigma</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ARIMA.forecast_ahead_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.forecast_ahead_interval">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
|
||||
|
||||
<span class="k">if</span> <span class="s1">'alpha'</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</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="s1">'alpha'</span><span class="p">,</span><span class="mf">0.05</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">alpha</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span>
|
||||
|
||||
<span class="n">smoothing</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">"smoothing"</span><span class="p">,</span><span class="mf">0.5</span><span class="p">)</span>
|
||||
|
||||
<span class="n">sigma</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span><span class="o">.</span><span class="n">sigma2</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">nmeans</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">steps</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="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="mi">0</span><span class="p">,</span> <span class="n">steps</span><span class="p">):</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">hsigma</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">k</span><span class="o">*</span><span class="n">smoothing</span><span class="p">)</span><span class="o">*</span><span class="n">sigma</span>
|
||||
|
||||
<span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">nmeans</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">hsigma</span><span class="p">)</span>
|
||||
<span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">nmeans</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">hsigma</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ARIMA.forecast_distribution"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.forecast_distribution">[docs]</a> <span class="k">def</span> <span class="nf">forecast_distribution</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>
|
||||
|
||||
<span class="n">sigma</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span><span class="o">.</span><span class="n">sigma2</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">data</span><span class="p">)</span>
|
||||
|
||||
<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="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
|
||||
<span class="n">sample</span> <span class="o">=</span> <span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</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">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="n">mean</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">mean</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
|
||||
<span class="n">mean</span> <span class="o">=</span> <span class="n">mean</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
|
||||
|
||||
<span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="s2">"histogram"</span><span class="p">,</span> <span class="n">uod</span><span class="o">=</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">original_min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span><span class="p">])</span>
|
||||
<span class="n">intervals</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">alpha</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="mf">0.05</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">):</span>
|
||||
|
||||
<span class="n">qt1</span> <span class="o">=</span> <span class="n">mean</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">sigma</span>
|
||||
<span class="n">qt2</span> <span class="o">=</span> <span class="n">mean</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">sigma</span>
|
||||
|
||||
<span class="n">intervals</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">qt1</span><span class="p">,</span> <span class="n">qt2</span><span class="p">])</span>
|
||||
|
||||
<span class="n">dist</span><span class="o">.</span><span class="n">append_interval</span><span class="p">(</span><span class="n">intervals</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ARIMA.forecast_ahead_distribution"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.forecast_ahead_distribution">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead_distribution</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="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="n">smoothing</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">"smoothing"</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span>
|
||||
|
||||
<span class="n">sigma</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span><span class="o">.</span><span class="n">sigma2</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">data</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">nmeans</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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="mi">0</span><span class="p">,</span> <span class="n">steps</span><span class="p">):</span>
|
||||
<span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="s2">"histogram"</span><span class="p">,</span>
|
||||
<span class="n">uod</span><span class="o">=</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">original_min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span><span class="p">])</span>
|
||||
<span class="n">intervals</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">alpha</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="mf">0.05</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">):</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">hsigma</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">k</span> <span class="o">*</span> <span class="n">smoothing</span><span class="p">)</span> <span class="o">*</span> <span class="n">sigma</span>
|
||||
|
||||
<span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">nmeans</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">hsigma</span><span class="p">)</span>
|
||||
<span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">nmeans</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">hsigma</span><span class="p">)</span>
|
||||
|
||||
<span class="n">intervals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
|
||||
|
||||
<span class="n">dist</span><span class="o">.</span><span class="n">append_interval</span><span class="p">(</span><span class="n">intervals</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div></div>
|
||||
|
||||
</pre></div>
|
||||
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<h1>Source code for pyFTS.benchmarks.knn</h1><div class="highlight"><pre>
|
||||
<span></span><span class="ch">#!/usr/bin/python</span>
|
||||
<span class="c1"># -*- coding: utf8 -*-</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">statsmodels.tsa.tsatools</span> <span class="k">import</span> <span class="n">lagmat</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">fts</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="k">import</span> <span class="n">ProbabilityDistribution</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="k">import</span> <span class="n">KDTree</span>
|
||||
<span class="kn">from</span> <span class="nn">itertools</span> <span class="k">import</span> <span class="n">product</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.models.ensemble.ensemble</span> <span class="k">import</span> <span class="n">sampler</span>
|
||||
|
||||
<div class="viewcode-block" id="KNearestNeighbors"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors">[docs]</a><span class="k">class</span> <span class="nc">KNearestNeighbors</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> A façade for sklearn.neighbors</span>
|
||||
<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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">KNearestNeighbors</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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"kNN"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">"kNN"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">"K-Nearest Neighbors"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<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>
|
||||
<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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">benchmark_only</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">lag</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">k</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">"k"</span><span class="p">,</span> <span class="mi">30</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">uod</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">values</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_prepare_x</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="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>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">l</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">l</span> <span class="o">+=</span> <span class="mi">1</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">t</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="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
|
||||
<span class="n">X</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">data</span><span class="p">[</span><span class="n">t</span> <span class="o">-</span> <span class="n">k</span> <span class="o">-</span> <span class="mi">1</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="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)])</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">X</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_prepare_xy</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="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>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">Y</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">t</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="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
|
||||
<span class="n">X</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">data</span><span class="p">[</span><span class="n">t</span> <span class="o">-</span> <span class="n">k</span> <span class="o">-</span> <span class="mi">1</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="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)])</span>
|
||||
<span class="n">Y</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">])</span>
|
||||
|
||||
<span class="k">return</span> <span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">Y</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="KNearestNeighbors.train"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.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>
|
||||
<span class="n">X</span><span class="p">,</span><span class="n">Y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prepare_xy</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span> <span class="o">=</span> <span class="n">KDTree</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">X</span><span class="p">))</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">values</span> <span class="o">=</span> <span class="n">Y</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">"kNN(</span><span class="si">{}</span><span class="s2">)-</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="KNearestNeighbors.knn"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.knn">[docs]</a> <span class="k">def</span> <span class="nf">knn</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">):</span>
|
||||
<span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prepare_x</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
||||
<span class="n">_</span><span class="p">,</span> <span class="n">ix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">query</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">X</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">k</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">ix</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span> <span class="p">]</span></div>
|
||||
|
||||
<div class="viewcode-block" id="KNearestNeighbors.forecast"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.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>
|
||||
<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>
|
||||
<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="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="p">(</span><span class="mi">1</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">==</span> <span class="n">l</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)):</span>
|
||||
|
||||
<span class="n">sample</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="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
||||
|
||||
<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">nanmean</span><span class="p">(</span><span class="n">forecasts</span><span class="p">))</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="KNearestNeighbors.forecast_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_interval">[docs]</a> <span class="k">def</span> <span class="nf">forecast_interval</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>
|
||||
|
||||
<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="s1">'alpha'</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
|
||||
|
||||
<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="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)):</span>
|
||||
|
||||
<span class="n">sample</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="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
||||
|
||||
<span class="n">i</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="p">[</span><span class="n">alpha</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">alpha</span><span class="p">)</span><span class="o">*</span><span class="mi">100</span><span class="p">])</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="KNearestNeighbors.forecast_ahead_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_ahead_interval">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead_interval</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="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</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="s1">'alpha'</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">start</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">'start'</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
|
||||
|
||||
<span class="n">sample</span> <span class="o">=</span> <span class="p">[[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="p">[</span><span class="n">start</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">start</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="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">steps</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">forecasts</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">lags</span> <span class="o">=</span> <span class="p">[</span><span class="n">sample</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</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">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)]</span>
|
||||
|
||||
<span class="c1"># Trace the possible paths</span>
|
||||
<span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">lags</span><span class="p">):</span>
|
||||
<span class="n">forecasts</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">knn</span><span class="p">(</span><span class="n">path</span><span class="p">))</span>
|
||||
|
||||
<span class="n">sample</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sampler</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">.</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">),</span> <span class="n">bounds</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
|
||||
|
||||
<span class="n">interval</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="p">[</span><span class="n">alpha</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">alpha</span><span class="p">)</span><span class="o">*</span><span class="mi">100</span><span class="p">])</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">interval</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="KNearestNeighbors.forecast_distribution"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_distribution">[docs]</a> <span class="k">def</span> <span class="nf">forecast_distribution</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>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">smooth</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">"smooth"</span><span class="p">,</span> <span class="s2">"histogram"</span><span class="p">)</span>
|
||||
|
||||
<span class="n">uod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_UoD</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="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)):</span>
|
||||
|
||||
<span class="n">sample</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="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
||||
|
||||
<span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="n">smooth</span><span class="p">,</span> <span class="n">uod</span><span class="o">=</span><span class="n">uod</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">forecasts</span><span class="p">,</span>
|
||||
<span class="n">name</span><span class="o">=</span><span class="s2">""</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">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="KNearestNeighbors.forecast_ahead_distribution"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_ahead_distribution">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead_distribution</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="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="n">smooth</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">"smooth"</span><span class="p">,</span> <span class="s2">"histogram"</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">start</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">'start'</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
|
||||
|
||||
<span class="n">uod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_UoD</span><span class="p">()</span>
|
||||
|
||||
<span class="n">sample</span> <span class="o">=</span> <span class="p">[[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="p">[</span><span class="n">start</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">start</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="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">steps</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">forecasts</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">lags</span> <span class="o">=</span> <span class="p">[</span><span class="n">sample</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</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">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)]</span>
|
||||
|
||||
<span class="c1"># Trace the possible paths</span>
|
||||
<span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">lags</span><span class="p">):</span>
|
||||
<span class="n">forecasts</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">knn</span><span class="p">(</span><span class="n">path</span><span class="p">))</span>
|
||||
|
||||
<span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="n">smooth</span><span class="p">,</span> <span class="n">uod</span><span class="o">=</span><span class="n">uod</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">forecasts</span><span class="p">,</span>
|
||||
<span class="n">name</span><span class="o">=</span><span class="s2">""</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">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
|
||||
|
||||
<span class="n">sample</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sampler</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">.</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">),</span> <span class="n">bounds</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div></div>
|
||||
|
||||
|
||||
</pre></div>
|
||||
|
||||
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106
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<h1>Source code for pyFTS.benchmarks.naive</h1><div class="highlight"><pre>
|
||||
<span></span><span class="ch">#!/usr/bin/python</span>
|
||||
<span class="c1"># -*- coding: utf8 -*-</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">fts</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="Naive"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.naive.Naive">[docs]</a><span class="k">class</span> <span class="nc">Naive</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
|
||||
<span class="sd">"""Naïve Forecasting method"""</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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">Naive</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">order</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">"Naive"</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">name</span> <span class="o">=</span> <span class="s2">"Naïve Model"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">"Naïve Model"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">benchmark_only</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
|
||||
<div class="viewcode-block" id="Naive.forecast"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.naive.Naive.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>
|
||||
<span class="k">return</span> <span class="n">data</span></div></div>
|
||||
|
||||
</pre></div>
|
||||
|
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<h1>Source code for pyFTS.benchmarks.quantreg</h1><div class="highlight"><pre>
|
||||
<span></span><span class="ch">#!/usr/bin/python</span>
|
||||
<span class="c1"># -*- coding: utf8 -*-</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">from</span> <span class="nn">statsmodels.regression.quantile_regression</span> <span class="k">import</span> <span class="n">QuantReg</span>
|
||||
<span class="kn">from</span> <span class="nn">statsmodels.tsa.tsatools</span> <span class="k">import</span> <span class="n">lagmat</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">SortedCollection</span><span class="p">,</span> <span class="n">fts</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="k">import</span> <span class="n">ProbabilityDistribution</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="QuantileRegression"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression">[docs]</a><span class="k">class</span> <span class="nc">QuantileRegression</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
|
||||
<span class="sd">"""Façade for statsmodels.regression.quantile_regression"""</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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">QuantileRegression</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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"QAR"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">"Quantile Regression"</span>
|
||||
<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>
|
||||
<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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">benchmark_only</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">dist</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">"dist"</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">upper_qt</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">mean_qt</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">lower_qt</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">dist_qt</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
|
||||
<div class="viewcode-block" id="QuantileRegression.train"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.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>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">):</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
|
||||
<span class="n">lagdata</span><span class="p">,</span> <span class="n">ndata</span> <span class="o">=</span> <span class="n">lagmat</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">maxlag</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">trim</span><span class="o">=</span><span class="s2">"both"</span><span class="p">,</span> <span class="n">original</span><span class="o">=</span><span class="s1">'sep'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">mqt</span> <span class="o">=</span> <span class="n">QuantReg</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">lagdata</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="mf">0.5</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">uqt</span> <span class="o">=</span> <span class="n">QuantReg</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">lagdata</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
|
||||
<span class="n">lqt</span> <span class="o">=</span> <span class="n">QuantReg</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">lagdata</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">mean_qt</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">mqt</span><span class="o">.</span><span class="n">params</span><span class="p">]</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">upper_qt</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">uqt</span><span class="o">.</span><span class="n">params</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">lower_qt</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">lqt</span><span class="o">.</span><span class="n">params</span><span class="p">]</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dist</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">dist_qt</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">alpha</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="mf">0.05</span><span class="p">,</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.05</span><span class="p">):</span>
|
||||
<span class="n">lqt</span> <span class="o">=</span> <span class="n">QuantReg</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">lagdata</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">alpha</span><span class="p">)</span>
|
||||
<span class="n">uqt</span> <span class="o">=</span> <span class="n">QuantReg</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">lagdata</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">alpha</span><span class="p">)</span>
|
||||
<span class="n">lo_qt</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">lqt</span><span class="o">.</span><span class="n">params</span><span class="p">]</span>
|
||||
<span class="n">up_qt</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">uqt</span><span class="o">.</span><span class="n">params</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">dist_qt</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">lo_qt</span><span class="p">,</span> <span class="n">up_qt</span><span class="p">])</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">"QAR(</span><span class="si">{}</span><span class="s2">)-</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="QuantileRegression.linearmodel"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.linearmodel">[docs]</a> <span class="k">def</span> <span class="nf">linearmodel</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="n">params</span><span class="p">):</span>
|
||||
<span class="c1">#return params[0] + sum([ data[k] * params[k+1] for k in np.arange(0, self.order) ])</span>
|
||||
<span class="k">return</span> <span class="nb">sum</span><span class="p">([</span><span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">*</span> <span class="n">params</span><span class="p">[</span><span class="n">k</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="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)])</span></div>
|
||||
|
||||
<div class="viewcode-block" id="QuantileRegression.point_to_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.point_to_interval">[docs]</a> <span class="k">def</span> <span class="nf">point_to_interval</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="n">lo_params</span><span class="p">,</span> <span class="n">up_params</span><span class="p">):</span>
|
||||
<span class="n">lo</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linearmodel</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">lo_params</span><span class="p">)</span>
|
||||
<span class="n">up</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linearmodel</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">up_params</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="p">[</span><span class="n">lo</span><span class="p">,</span> <span class="n">up</span><span class="p">]</span></div>
|
||||
|
||||
<div class="viewcode-block" id="QuantileRegression.interval_to_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.interval_to_interval">[docs]</a> <span class="k">def</span> <span class="nf">interval_to_interval</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="n">lo_params</span><span class="p">,</span> <span class="n">up_params</span><span class="p">):</span>
|
||||
<span class="n">lo</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linearmodel</span><span class="p">([</span><span class="n">k</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="p">],</span> <span class="n">lo_params</span><span class="p">)</span>
|
||||
<span class="n">up</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linearmodel</span><span class="p">([</span><span class="n">k</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="p">],</span> <span class="n">up_params</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="p">[</span><span class="n">lo</span><span class="p">,</span> <span class="n">up</span><span class="p">]</span></div>
|
||||
|
||||
<div class="viewcode-block" id="QuantileRegression.forecast"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.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">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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">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="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span> <span class="c1">#+1 to forecast one step ahead given all available lags</span>
|
||||
<span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</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="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">linearmodel</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">mean_qt</span><span class="p">))</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="QuantileRegression.forecast_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_interval">[docs]</a> <span class="k">def</span> <span class="nf">forecast_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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">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="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="p">,</span> <span class="n">l</span><span class="p">):</span>
|
||||
<span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</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="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">point_to_interval</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower_qt</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper_qt</span><span class="p">))</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="QuantileRegression.forecast_ahead_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_ahead_interval">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
|
||||
<span class="n">smoothing</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">"smoothing"</span><span class="p">,</span> <span class="mf">0.01</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">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">nmeans</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
|
||||
<span class="n">nmeans</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">k</span><span class="p">,</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</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="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">steps</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">intl</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">point_to_interval</span><span class="p">(</span><span class="n">nmeans</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="bp">self</span><span class="o">.</span><span class="n">lower_qt</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper_qt</span><span class="p">)</span>
|
||||
|
||||
<span class="n">tmpk</span> <span class="o">=</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="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">intl</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="p">(</span><span class="n">tmpk</span><span class="o">*</span><span class="n">smoothing</span><span class="p">)),</span> <span class="n">intl</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="p">(</span><span class="n">tmpk</span><span class="o">*</span><span class="n">smoothing</span><span class="p">))])</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div>
|
||||
|
||||
<div class="viewcode-block" id="QuantileRegression.forecast_distribution"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_distribution">[docs]</a> <span class="k">def</span> <span class="nf">forecast_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</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="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="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="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
|
||||
<span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="s2">"histogram"</span><span class="p">,</span>
|
||||
<span class="n">uod</span><span class="o">=</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">original_min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span><span class="p">])</span>
|
||||
<span class="n">intervals</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">qt</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">dist_qt</span><span class="p">:</span>
|
||||
<span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</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="n">intl</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">point_to_interval</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">qt</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qt</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="n">intervals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">intl</span><span class="p">)</span>
|
||||
|
||||
<span class="n">dist</span><span class="o">.</span><span class="n">append_interval</span><span class="p">(</span><span class="n">intervals</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="QuantileRegression.forecast_ahead_distribution"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_ahead_distribution">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="n">smoothing</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">"smoothing"</span><span class="p">,</span> <span class="mf">0.01</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">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">nmeans</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
|
||||
<span class="n">nmeans</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">ndata</span><span class="p">[</span><span class="n">k</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="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">steps</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">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="s2">"histogram"</span><span class="p">,</span>
|
||||
<span class="n">uod</span><span class="o">=</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">original_min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span><span class="p">])</span>
|
||||
|
||||
<span class="n">intervals</span> <span class="o">=</span> <span class="p">[[</span><span class="n">nmeans</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">],</span> <span class="n">nmeans</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">]]]</span>
|
||||
<span class="k">for</span> <span class="n">qt</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">dist_qt</span><span class="p">:</span>
|
||||
<span class="n">intl1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">point_to_interval</span><span class="p">(</span><span class="n">nmeans</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="n">qt</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qt</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="n">tmpk</span> <span class="o">=</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="n">intl2</span> <span class="o">=</span> <span class="p">[</span><span class="n">intl1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="p">(</span><span class="n">tmpk</span> <span class="o">*</span> <span class="n">smoothing</span><span class="p">)),</span> <span class="n">intl1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="p">(</span><span class="n">tmpk</span> <span class="o">*</span> <span class="n">smoothing</span><span class="p">))]</span>
|
||||
<span class="n">intervals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">intl2</span><span class="p">)</span>
|
||||
<span class="n">dist</span><span class="o">.</span><span class="n">append_interval</span><span class="p">(</span><span class="n">intervals</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div></div>
|
||||
|
||||
</pre></div>
|
||||
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<h1>Source code for pyFTS.common.Util</h1><div class="highlight"><pre>
|
||||
<span></span><span class="sd">"""</span>
|
||||
<span class="sd">Common facilities for pyFTS</span>
|
||||
<span class="sd">"""</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">time</span>
|
||||
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</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">matplotlib.cm</span> <span class="k">as</span> <span class="nn">cmx</span>
|
||||
<span class="kn">import</span> <span class="nn">matplotlib.colors</span> <span class="k">as</span> <span class="nn">pltcolors</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="k">import</span> <span class="n">ProbabilityDistribution</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">Transformations</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="plot_compared_intervals_ahead"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.plot_compared_intervals_ahead">[docs]</a><span class="k">def</span> <span class="nf">plot_compared_intervals_ahead</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">colors</span><span class="p">,</span> <span class="n">distributions</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="n">intervals</span> <span class="o">=</span> <span class="kc">True</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">tam</span><span class="o">=</span><span class="p">[</span><span class="mi">20</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="n">resolution</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
|
||||
<span class="n">cmap</span><span class="o">=</span><span class="s1">'Blues'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">1.5</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Plot the forecasts of several one step ahead models, by point or by interval</span>
|
||||
|
||||
<span class="sd"> :param original: Original time series data (list)</span>
|
||||
<span class="sd"> :param models: List of models to compare</span>
|
||||
<span class="sd"> :param colors: List of models colors</span>
|
||||
<span class="sd"> :param distributions: True to plot a distribution</span>
|
||||
<span class="sd"> :param time_from: index of data poit to start the ahead forecasting</span>
|
||||
<span class="sd"> :param time_to: number of steps ahead to forecast</span>
|
||||
<span class="sd"> :param interpol: Fill space between distribution plots</span>
|
||||
<span class="sd"> :param save: Save the picture on file</span>
|
||||
<span class="sd"> :param file: Filename to save the picture</span>
|
||||
<span class="sd"> :param tam: Size of the picture</span>
|
||||
<span class="sd"> :param resolution:</span>
|
||||
<span class="sd"> :param cmap: Color map to be used on distribution plot</span>
|
||||
<span class="sd"> :param option: Distribution type to be passed for models</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">tam</span><span class="p">)</span>
|
||||
<span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">)</span>
|
||||
|
||||
<span class="n">cm</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="n">cmap</span><span class="p">)</span>
|
||||
<span class="n">cNorm</span> <span class="o">=</span> <span class="n">pltcolors</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">vmin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">scalarMap</span> <span class="o">=</span> <span class="n">cmx</span><span class="o">.</span><span class="n">ScalarMappable</span><span class="p">(</span><span class="n">norm</span><span class="o">=</span><span class="n">cNorm</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cm</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">resolution</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="n">resolution</span> <span class="o">=</span> <span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">original</span><span class="p">)</span> <span class="o">-</span> <span class="nb">min</span><span class="p">(</span><span class="n">original</span><span class="p">))</span> <span class="o">/</span> <span class="mi">100</span>
|
||||
|
||||
<span class="n">mi</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">ma</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">count</span><span class="p">,</span> <span class="n">fts</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">if</span> <span class="n">fts</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="ow">and</span> <span class="n">distributions</span><span class="p">[</span><span class="n">count</span><span class="p">]:</span>
|
||||
<span class="n">density</span> <span class="o">=</span> <span class="n">fts</span><span class="o">.</span><span class="n">forecast_ahead_distribution</span><span class="p">(</span><span class="n">original</span><span class="p">[</span><span class="n">time_from</span> <span class="o">-</span> <span class="n">fts</span><span class="o">.</span><span class="n">order</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="n">resolution</span><span class="o">=</span><span class="n">resolution</span><span class="p">)</span>
|
||||
|
||||
<span class="c1">#plot_density_scatter(ax, cmap, density, fig, resolution, time_from, time_to)</span>
|
||||
<span class="n">plot_density_rectange</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">cm</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="k">if</span> <span class="n">fts</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="ow">and</span> <span class="n">intervals</span><span class="p">:</span>
|
||||
<span class="n">forecasts</span> <span class="o">=</span> <span class="n">fts</span><span class="o">.</span><span class="n">forecast_ahead_interval</span><span class="p">(</span><span class="n">original</span><span class="p">[</span><span class="n">time_from</span> <span class="o">-</span> <span class="n">fts</span><span class="o">.</span><span class="n">order</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="n">lower</span> <span class="o">=</span> <span class="p">[</span><span class="n">kk</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">forecasts</span><span class="p">]</span>
|
||||
<span class="n">upper</span> <span class="o">=</span> <span class="p">[</span><span class="n">kk</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">forecasts</span><span class="p">]</span>
|
||||
<span class="n">mi</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">lower</span><span class="p">))</span>
|
||||
<span class="n">ma</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">upper</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="mi">0</span><span class="p">,</span> <span class="n">time_from</span> <span class="o">-</span> <span class="n">fts</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
|
||||
<span class="n">lower</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
<span class="n">upper</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lower</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">colors</span><span class="p">[</span><span class="n">count</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="n">fts</span><span class="o">.</span><span class="n">shortname</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="n">linewidth</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">upper</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">colors</span><span class="p">[</span><span class="n">count</span><span class="p">],</span> <span class="n">linewidth</span><span class="o">=</span><span class="n">linewidth</span><span class="o">*</span><span class="mf">1.5</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">original</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'black'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"Original"</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="n">linewidth</span><span class="o">*</span><span class="mf">1.5</span><span class="p">)</span>
|
||||
<span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_legend_handles_labels</span><span class="p">()</span>
|
||||
<span class="k">if</span> <span class="kc">True</span> <span class="ow">in</span> <span class="n">distributions</span><span class="p">:</span>
|
||||
<span class="n">lgd</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">lgd</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">bbox_to_anchor</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">_mi</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">mi</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">_mi</span> <span class="o"><</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">_mi</span> <span class="o">*=</span> <span class="mf">1.1</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">_mi</span> <span class="o">*=</span> <span class="mf">0.9</span>
|
||||
<span class="n">_ma</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">ma</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">_ma</span> <span class="o"><</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">_ma</span> <span class="o">*=</span> <span class="mf">0.9</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">_ma</span> <span class="o">*=</span> <span class="mf">1.1</span>
|
||||
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="n">_mi</span><span class="p">,</span> <span class="n">_ma</span><span class="p">])</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'F(T)'</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'T'</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">original</span><span class="p">)])</span>
|
||||
|
||||
<span class="n">show_and_save_image</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">save</span><span class="p">,</span> <span class="n">lgd</span><span class="o">=</span><span class="n">lgd</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="plot_density_rectange"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.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">"""</span>
|
||||
<span class="sd"> Auxiliar function to plot_compared_intervals_ahead</span>
|
||||
<span class="sd"> """</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="n">patches</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">colors</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">density</span><span class="o">.</span><span class="n">index</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">density</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
|
||||
<span class="n">s</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">((</span><span class="n">time_from</span> <span class="o">+</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="n">resolution</span><span class="p">,</span> <span class="n">fill</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">lw</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">patches</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
|
||||
<span class="n">colors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">density</span><span class="p">[</span><span class="n">y</span><span class="p">][</span><span class="n">x</span><span class="p">]</span><span class="o">*</span><span class="mi">5</span><span class="p">)</span>
|
||||
<span class="n">pc</span> <span class="o">=</span> <span class="n">PatchCollection</span><span class="p">(</span><span class="n">patches</span><span class="o">=</span><span class="n">patches</span><span class="p">,</span> <span class="n">match_original</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">pc</span><span class="o">.</span><span class="n">set_clim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
|
||||
<span class="n">pc</span><span class="o">.</span><span class="n">set_cmap</span><span class="p">(</span><span class="n">cmap</span><span class="p">)</span>
|
||||
<span class="n">pc</span><span class="o">.</span><span class="n">set_array</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">colors</span><span class="p">))</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">pc</span><span class="p">)</span>
|
||||
<span class="n">cb</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">pc</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
|
||||
<span class="n">cb</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s1">'Density'</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="plot_probability_distributions"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.plot_probability_distributions">[docs]</a><span class="k">def</span> <span class="nf">plot_probability_distributions</span><span class="p">(</span><span class="n">pmfs</span><span class="p">,</span> <span class="n">lcolors</span><span class="p">,</span> <span class="n">tam</span><span class="o">=</span><span class="p">[</span><span class="mi">15</span><span class="p">,</span> <span class="mi">7</span><span class="p">]):</span>
|
||||
<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">tam</span><span class="p">)</span>
|
||||
<span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">k</span><span class="p">,</span><span class="n">m</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">pmfs</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">m</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">lcolors</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
|
||||
|
||||
<span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_legend_handles_labels</span><span class="p">()</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="plot_distribution"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.plot_distribution">[docs]</a><span class="k">def</span> <span class="nf">plot_distribution</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">probabilitydist</span><span class="p">,</span> <span class="n">fig</span><span class="p">,</span> <span class="n">time_from</span><span class="p">,</span> <span class="n">reference_data</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Plot forecasted ProbabilityDistribution objects on a matplotlib axis</span>
|
||||
|
||||
<span class="sd"> :param ax: matplotlib axis</span>
|
||||
<span class="sd"> :param cmap: matplotlib colormap name</span>
|
||||
<span class="sd"> :param probabilitydist: list of ProbabilityDistribution objects</span>
|
||||
<span class="sd"> :param fig: matplotlib figure</span>
|
||||
<span class="sd"> :param time_from: starting time (on x axis) to begin the plots</span>
|
||||
<span class="sd"> :param reference_data:</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</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="n">patches</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">colors</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">dt</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">probabilitydist</span><span class="p">):</span>
|
||||
<span class="n">disp</span> <span class="o">=</span> <span class="mf">0.0</span>
|
||||
<span class="k">if</span> <span class="n">reference_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">disp</span> <span class="o">=</span> <span class="n">reference_data</span><span class="p">[</span><span class="n">time_from</span><span class="o">+</span><span class="n">ct</span><span class="p">]</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">dt</span><span class="o">.</span><span class="n">bins</span><span class="p">:</span>
|
||||
<span class="n">s</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">((</span><span class="n">time_from</span><span class="o">+</span><span class="n">ct</span><span class="p">,</span> <span class="n">y</span><span class="o">+</span><span class="n">disp</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="n">dt</span><span class="o">.</span><span class="n">resolution</span><span class="p">,</span> <span class="n">fill</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">lw</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">patches</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
|
||||
<span class="n">colors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dt</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
|
||||
<span class="n">scale</span> <span class="o">=</span> <span class="n">Transformations</span><span class="o">.</span><span class="n">Scale</span><span class="p">()</span>
|
||||
<span class="n">colors</span> <span class="o">=</span> <span class="n">scale</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">colors</span><span class="p">)</span>
|
||||
<span class="n">pc</span> <span class="o">=</span> <span class="n">PatchCollection</span><span class="p">(</span><span class="n">patches</span><span class="o">=</span><span class="n">patches</span><span class="p">,</span> <span class="n">match_original</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">pc</span><span class="o">.</span><span class="n">set_clim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
|
||||
<span class="n">pc</span><span class="o">.</span><span class="n">set_cmap</span><span class="p">(</span><span class="n">cmap</span><span class="p">)</span>
|
||||
<span class="n">pc</span><span class="o">.</span><span class="n">set_array</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">colors</span><span class="p">))</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">pc</span><span class="p">)</span>
|
||||
<span class="n">cb</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">pc</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
|
||||
<span class="n">cb</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s1">'Density'</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="plot_distribution2"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.plot_distribution2">[docs]</a><span class="k">def</span> <span class="nf">plot_distribution2</span><span class="p">(</span><span class="n">probabilitydist</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>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Plot distributions in y-axis over the time (x-axis)</span>
|
||||
|
||||
<span class="sd"> :param probabilitydist: the forecasted probability distributions to plot</span>
|
||||
<span class="sd"> :param data: the original test sample</span>
|
||||
<span class="sd"> :keyword start_at: the time index (inside of data) to start to plot the probability distributions</span>
|
||||
<span class="sd"> :keyword ax: a matplotlib axis. If no value was provided a new axis is created.</span>
|
||||
<span class="sd"> :keyword cmap: a matplotlib colormap name, the default value is 'Blues'</span>
|
||||
<span class="sd"> :keyword quantiles: the list of quantiles intervals to plot, e. g. [.05, .25, .75, .95]</span>
|
||||
<span class="sd"> :keyword median: a boolean value indicating if the median value will be plot.</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="kn">import</span> <span class="nn">matplotlib.colorbar</span> <span class="k">as</span> <span class="nn">cbar</span>
|
||||
<span class="kn">import</span> <span class="nn">matplotlib.cm</span> <span class="k">as</span> <span class="nn">cm</span>
|
||||
|
||||
<span class="n">ax</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">'ax'</span><span class="p">,</span><span class="kc">None</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">ax</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">[</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</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">probabilitydist</span><span class="p">)</span>
|
||||
|
||||
<span class="n">cmap</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">'cmap'</span><span class="p">,</span><span class="s1">'Blues'</span><span class="p">)</span>
|
||||
<span class="n">cmap</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="n">cmap</span><span class="p">)</span>
|
||||
|
||||
<span class="n">start_at</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">'start_at'</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
|
||||
|
||||
<span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="o">+</span> <span class="n">start_at</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)]</span>
|
||||
|
||||
<span class="n">qt</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">'quantiles'</span><span class="p">,</span><span class="kc">None</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">qt</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">qt</span> <span class="o">=</span> <span class="p">[</span><span class="nb">round</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">2</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="o">.</span><span class="mi">05</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="o">.</span><span class="mi">05</span><span class="p">)]</span>
|
||||
<span class="n">qt</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="o">.</span><span class="mi">01</span><span class="p">)</span>
|
||||
<span class="n">qt</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="o">.</span><span class="mi">99</span><span class="p">)</span>
|
||||
|
||||
<span class="n">lq</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">qt</span><span class="p">)</span>
|
||||
|
||||
<span class="n">normal</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">qt</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">qt</span><span class="p">))</span>
|
||||
<span class="n">scalarMap</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">ScalarMappable</span><span class="p">(</span><span class="n">norm</span><span class="o">=</span><span class="n">normal</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">ct</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="nb">int</span><span class="p">(</span><span class="n">lq</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
|
||||
<span class="k">try</span><span class="p">:</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="p">[[</span><span class="n">data</span><span class="p">[</span><span class="n">start_at</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="n">start_at</span><span class="p">]]]</span>
|
||||
<span class="k">for</span> <span class="n">pd</span> <span class="ow">in</span> <span class="n">probabilitydist</span><span class="p">:</span>
|
||||
<span class="n">qts</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">quantile</span><span class="p">([</span><span class="n">qt</span><span class="p">[</span><span class="n">ct</span> <span class="o">-</span> <span class="mi">1</span><span class="p">],</span> <span class="n">qt</span><span class="p">[</span><span class="o">-</span><span class="n">ct</span><span class="p">]])</span>
|
||||
<span class="n">y</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">qts</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">[</span><span class="n">k</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">y</span><span class="p">],</span> <span class="p">[</span><span class="n">k</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">y</span><span class="p">],</span>
|
||||
<span class="n">facecolor</span><span class="o">=</span><span class="n">scalarMap</span><span class="o">.</span><span class="n">to_rgba</span><span class="p">(</span><span class="n">ct</span> <span class="o">/</span> <span class="n">lq</span><span class="p">))</span>
|
||||
<span class="k">except</span><span class="p">:</span>
|
||||
<span class="k">pass</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'median'</span><span class="p">,</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="n">start_at</span><span class="p">]]</span>
|
||||
<span class="k">for</span> <span class="n">pd</span> <span class="ow">in</span> <span class="n">probabilitydist</span><span class="p">:</span>
|
||||
<span class="n">qts</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">quantile</span><span class="p">([</span><span class="o">.</span><span class="mi">5</span><span class="p">])</span>
|
||||
<span class="n">y</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">qts</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
|
||||
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'red'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Median'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">cax</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">cbar</span><span class="o">.</span><span class="n">make_axes</span><span class="p">(</span><span class="n">ax</span><span class="p">)</span>
|
||||
<span class="n">cb</span> <span class="o">=</span> <span class="n">cbar</span><span class="o">.</span><span class="n">ColorbarBase</span><span class="p">(</span><span class="n">cax</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="n">normal</span><span class="p">)</span>
|
||||
<span class="n">cb</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s1">'Density'</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="plot_distribution_tiled"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.plot_distribution_tiled">[docs]</a><span class="k">def</span> <span class="nf">plot_distribution_tiled</span><span class="p">(</span><span class="n">distributions</span><span class="p">,</span><span class="n">data</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span><span class="n">rows</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span><span class="n">cols</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span><span class="n">index</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span><span class="n">size</span><span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">,</span><span class="mi">20</span><span class="p">]):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Plot one distribution individually in each axis, with probability in y-axis and UoD on x-axis</span>
|
||||
|
||||
<span class="sd"> :param distributions:</span>
|
||||
<span class="sd"> :param data:</span>
|
||||
<span class="sd"> :param rows:</span>
|
||||
<span class="sd"> :param cols:</span>
|
||||
<span class="sd"> :param index:</span>
|
||||
<span class="sd"> :param axis:</span>
|
||||
<span class="sd"> :param size:</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">axis</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">fig</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="n">rows</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="n">cols</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">size</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">ct</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">rows</span><span class="o">*</span><span class="n">cols</span><span class="p">):</span>
|
||||
<span class="n">col</span> <span class="o">=</span> <span class="n">ct</span> <span class="o">%</span> <span class="n">cols</span>
|
||||
<span class="n">row</span> <span class="o">=</span> <span class="n">ct</span> <span class="o">//</span> <span class="n">cols</span>
|
||||
<span class="k">if</span> <span class="n">index</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">ix</span> <span class="o">=</span> <span class="n">ct</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ix</span> <span class="o">=</span><span class="n">index</span><span class="p">[</span><span class="n">ct</span><span class="p">]</span>
|
||||
<span class="n">forecast</span> <span class="o">=</span> <span class="n">distributions</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
|
||||
<span class="n">forecast</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">[</span><span class="n">row</span><span class="p">][</span><span class="n">col</span><span class="p">])</span>
|
||||
<span class="k">if</span> <span class="n">data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">axis</span><span class="p">[</span><span class="n">row</span><span class="p">][</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">ix</span><span class="p">])</span>
|
||||
<span class="n">axis</span><span class="p">[</span><span class="n">row</span><span class="p">][</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'t+</span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ix</span><span class="p">))</span>
|
||||
<span class="n">axis</span><span class="p">[</span><span class="n">row</span><span class="p">][</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
|
||||
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="plot_interval"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.plot_interval">[docs]</a><span class="k">def</span> <span class="nf">plot_interval</span><span class="p">(</span><span class="n">axis</span><span class="p">,</span> <span class="n">intervals</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'red'</span><span class="p">,</span> <span class="n">typeonlegend</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="s1">'-'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Plot forecasted intervals on matplotlib</span>
|
||||
|
||||
<span class="sd"> :param axis: matplotlib axis</span>
|
||||
<span class="sd"> :param intervals: list of forecasted intervals</span>
|
||||
<span class="sd"> :param order: order of the model that create the forecasts</span>
|
||||
<span class="sd"> :param label: figure label</span>
|
||||
<span class="sd"> :param color: matplotlib color name</span>
|
||||
<span class="sd"> :param typeonlegend:</span>
|
||||
<span class="sd"> :param ls: matplotlib line style</span>
|
||||
<span class="sd"> :param linewidth: matplotlib width</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">lower</span> <span class="o">=</span> <span class="p">[</span><span class="n">kk</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">intervals</span><span class="p">]</span>
|
||||
<span class="n">upper</span> <span class="o">=</span> <span class="p">[</span><span class="n">kk</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">intervals</span><span class="p">]</span>
|
||||
<span class="n">mi</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">lower</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.95</span>
|
||||
<span class="n">ma</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">upper</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1.05</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="mi">0</span><span class="p">,</span> <span class="n">order</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
|
||||
<span class="n">lower</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
<span class="n">upper</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">typeonlegend</span><span class="p">:</span> <span class="n">label</span> <span class="o">+=</span> <span class="s2">" (Interval)"</span>
|
||||
<span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lower</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="n">ls</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="n">linewidth</span><span class="p">)</span>
|
||||
<span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">upper</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="n">ls</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="n">linewidth</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="p">[</span><span class="n">mi</span><span class="p">,</span> <span class="n">ma</span><span class="p">]</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="plot_interval2"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.plot_interval2">[docs]</a><span class="k">def</span> <span class="nf">plot_interval2</span><span class="p">(</span><span class="n">intervals</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>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Plot forecasted intervals on matplotlib</span>
|
||||
|
||||
<span class="sd"> :param intervals: list of forecasted intervals</span>
|
||||
<span class="sd"> :param data: the original test sample</span>
|
||||
<span class="sd"> :keyword start_at: the time index (inside of data) to start to plot the intervals</span>
|
||||
<span class="sd"> :keyword label: figure label</span>
|
||||
<span class="sd"> :keyword color: matplotlib color name</span>
|
||||
<span class="sd"> :keyword typeonlegend:</span>
|
||||
<span class="sd"> :keyword ls: matplotlib line style</span>
|
||||
<span class="sd"> :keyword linewidth: matplotlib width</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">intervals</span><span class="p">)</span>
|
||||
|
||||
<span class="n">nintervals</span> <span class="o">=</span> <span class="n">intervals</span>
|
||||
|
||||
<span class="n">start_at</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">'start_at'</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ax</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">'ax'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">ax</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">[</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</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="mi">0</span><span class="p">,</span> <span class="n">start_at</span><span class="p">):</span>
|
||||
<span class="n">nintervals</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span><span class="kc">None</span><span class="p">])</span>
|
||||
|
||||
<span class="n">nintervals</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">start_at</span><span class="p">,</span> <span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="n">start_at</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="n">start_at</span><span class="p">]])</span>
|
||||
|
||||
<span class="n">lower</span> <span class="o">=</span> <span class="p">[</span><span class="n">kk</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">nintervals</span><span class="p">]</span>
|
||||
<span class="n">upper</span> <span class="o">=</span> <span class="p">[</span><span class="n">kk</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">nintervals</span><span class="p">]</span>
|
||||
|
||||
<span class="n">typeonlegend</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">'typeonlegend'</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
|
||||
<span class="n">color</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">'color'</span><span class="p">,</span> <span class="s1">'red'</span><span class="p">)</span>
|
||||
<span class="n">label</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">'label'</span><span class="p">,</span><span class="s1">''</span><span class="p">)</span>
|
||||
<span class="n">linewidth</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">'linewidth'</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ls</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">'ls'</span><span class="p">,</span><span class="s1">'-'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">typeonlegend</span><span class="p">:</span> <span class="n">label</span> <span class="o">+=</span> <span class="s2">" (Interval)"</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lower</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="n">ls</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="n">linewidth</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">upper</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="n">ls</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="n">linewidth</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="plot_rules"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.plot_rules">[docs]</a><span class="k">def</span> <span class="nf">plot_rules</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">rules_by_axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Plot the FLRG rules of a FTS model on a matplotlib axis</span>
|
||||
|
||||
<span class="sd"> :param model: FTS model</span>
|
||||
<span class="sd"> :param size: figure size</span>
|
||||
<span class="sd"> :param axis: matplotlib axis</span>
|
||||
<span class="sd"> :param rules_by_axis: number of rules plotted by column</span>
|
||||
<span class="sd"> :param columns: number of columns</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="n">axis</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">rules_by_axis</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">rows</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="k">elif</span> <span class="n">axis</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">rules_by_axis</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">rows</span> <span class="o">=</span> <span class="p">(((</span><span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">//</span><span class="n">rules_by_axis</span><span class="p">))</span> <span class="o">//</span> <span class="n">columns</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span>
|
||||
|
||||
<span class="n">fig</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="n">rows</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="n">columns</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">size</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">rules_by_axis</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">draw_sets_on_axis</span><span class="p">(</span><span class="n">axis</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
|
||||
|
||||
<span class="n">_lhs</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">model</span><span class="o">.</span><span class="n">is_high_order</span> <span class="k">else</span> <span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">_lhs</span><span class="p">):</span>
|
||||
|
||||
<span class="n">xticks</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">xtickslabels</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">rules_by_axis</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">ax</span> <span class="o">=</span> <span class="n">axis</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">colcount</span> <span class="o">=</span> <span class="p">(</span><span class="n">ct</span> <span class="o">//</span> <span class="n">rules_by_axis</span><span class="p">)</span> <span class="o">%</span> <span class="n">columns</span>
|
||||
<span class="n">rowcount</span> <span class="o">=</span> <span class="p">(</span><span class="n">ct</span> <span class="o">//</span> <span class="n">rules_by_axis</span><span class="p">)</span> <span class="o">//</span> <span class="n">columns</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">rows</span> <span class="o">></span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">columns</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">ax</span> <span class="o">=</span> <span class="n">axis</span><span class="p">[</span><span class="n">rowcount</span><span class="p">,</span> <span class="n">colcount</span><span class="p">]</span>
|
||||
<span class="k">elif</span> <span class="n">columns</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">ax</span> <span class="o">=</span> <span class="n">axis</span><span class="p">[</span><span class="n">rowcount</span><span class="p">]</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ax</span> <span class="o">=</span> <span class="n">axis</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">ct</span> <span class="o">%</span> <span class="n">rules_by_axis</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">draw_sets_on_axis</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">model</span><span class="o">.</span><span class="n">is_high_order</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
|
||||
<span class="n">x</span> <span class="o">=</span> <span class="p">(</span><span class="n">ct</span> <span class="o">%</span> <span class="n">rules_by_axis</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
|
||||
<span class="n">flrg</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">x</span><span class="p">],[</span><span class="n">y</span><span class="p">],</span><span class="s1">'o'</span><span class="p">)</span>
|
||||
<span class="n">xticks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
||||
<span class="n">xtickslabels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">rhs</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
|
||||
<span class="n">dest</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">rhs</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">arrow</span><span class="p">(</span><span class="n">x</span><span class="o">+.</span><span class="mi">1</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">,</span> <span class="n">dest</span> <span class="o">-</span> <span class="n">y</span><span class="p">,</span> <span class="c1">#length_includes_head=True,</span>
|
||||
<span class="n">head_width</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">head_length</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="s1">'full'</span><span class="p">,</span> <span class="n">overhang</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
|
||||
<span class="n">fc</span><span class="o">=</span><span class="s1">'k'</span><span class="p">,</span> <span class="n">ec</span><span class="o">=</span><span class="s1">'k'</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">flrg</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
|
||||
<span class="n">x</span> <span class="o">=</span> <span class="p">(</span><span class="n">ct</span><span class="o">%</span><span class="n">rules_by_axis</span><span class="p">)</span><span class="o">*</span><span class="n">model</span><span class="o">.</span><span class="n">order</span> <span class="o">+</span> <span class="mi">1</span>
|
||||
<span class="k">for</span> <span class="n">ct2</span><span class="p">,</span> <span class="n">lhs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">):</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">lhs</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">x</span><span class="o">+</span><span class="n">ct2</span><span class="p">],</span> <span class="p">[</span><span class="n">y</span><span class="p">],</span> <span class="s1">'o'</span><span class="p">)</span>
|
||||
<span class="n">xticks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x</span><span class="o">+</span><span class="n">ct2</span><span class="p">)</span>
|
||||
<span class="n">xtickslabels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">lhs</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">ct2</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
|
||||
<span class="n">fs1</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="n">ct2</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="n">fs2</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="n">ct2</span><span class="p">]</span>
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">fs1</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span>
|
||||
<span class="n">dest</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">fs2</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">x</span><span class="o">+</span><span class="n">ct2</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">x</span><span class="o">+</span><span class="n">ct2</span><span class="p">],</span> <span class="p">[</span><span class="n">y</span><span class="p">,</span><span class="n">dest</span><span class="p">],</span><span class="s1">'-'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">y</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span><span class="o">.</span><span class="n">centroid</span>
|
||||
<span class="k">for</span> <span class="n">rhs</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
|
||||
<span class="n">dest</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">rhs</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">arrow</span><span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span> <span class="o">-</span><span class="mi">1</span> <span class="o">+</span> <span class="o">.</span><span class="mi">1</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">,</span> <span class="n">dest</span> <span class="o">-</span> <span class="n">y</span><span class="p">,</span> <span class="c1"># length_includes_head=True,</span>
|
||||
<span class="n">head_width</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">head_length</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="s1">'full'</span><span class="p">,</span> <span class="n">overhang</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
|
||||
<span class="n">fc</span><span class="o">=</span><span class="s1">'k'</span><span class="p">,</span> <span class="n">ec</span><span class="o">=</span><span class="s1">'k'</span><span class="p">)</span>
|
||||
|
||||
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">xticks</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">xtickslabels</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="n">rules_by_axis</span><span class="o">*</span><span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="o">+</span><span class="mi">1</span><span class="p">])</span>
|
||||
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="draw_sets_on_axis"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.draw_sets_on_axis">[docs]</a><span class="k">def</span> <span class="nf">draw_sets_on_axis</span><span class="p">(</span><span class="n">axis</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">size</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">axis</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">fig</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">size</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">):</span>
|
||||
<span class="n">fs</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
|
||||
<span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">fs</span><span class="o">.</span><span class="n">parameters</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">fs</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
|
||||
<span class="n">axis</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">fs</span><span class="o">.</span><span class="n">centroid</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s2">"lightgray"</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
|
||||
<span class="n">axis</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">)])</span>
|
||||
<span class="n">axis</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">)))</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="s1">''</span><span class="p">]</span>
|
||||
<span class="n">tmp</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">)</span>
|
||||
<span class="n">axis</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
|
||||
<span class="n">axis</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">min</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">max</span><span class="p">])</span>
|
||||
<span class="n">axis</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">([</span><span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">])</span>
|
||||
<span class="n">axis</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span> <span class="o">+</span> <span class="s2">" - "</span> <span class="o">+</span> <span class="n">k</span>
|
||||
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">])</span></div>
|
||||
|
||||
|
||||
<span class="n">current_milli_time</span> <span class="o">=</span> <span class="k">lambda</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">*</span> <span class="mi">1000</span><span class="p">))</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="uniquefilename"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.uniquefilename">[docs]</a><span class="k">def</span> <span class="nf">uniquefilename</span><span class="p">(</span><span class="n">name</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="s1">'.'</span> <span class="ow">in</span> <span class="n">name</span><span class="p">:</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="n">name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">'.'</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">tmp</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">current_milli_time</span><span class="p">())</span> <span class="o">+</span> <span class="s1">'.'</span> <span class="o">+</span> <span class="n">tmp</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">name</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">current_milli_time</span><span class="p">())</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="show_and_save_image"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.show_and_save_image">[docs]</a><span class="k">def</span> <span class="nf">show_and_save_image</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">flag</span><span class="p">,</span> <span class="n">lgd</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Show and image and save on file</span>
|
||||
|
||||
<span class="sd"> :param fig: Matplotlib Figure object</span>
|
||||
<span class="sd"> :param file: filename to save the picture</span>
|
||||
<span class="sd"> :param flag: if True the image will be saved</span>
|
||||
<span class="sd"> :param lgd: legend</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
|
||||
<span class="k">if</span> <span class="n">flag</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">lgd</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">additional_artists</span><span class="o">=</span><span class="n">lgd</span><span class="p">,</span><span class="n">bbox_inches</span><span class="o">=</span><span class="s1">'tight'</span><span class="p">)</span> <span class="c1">#bbox_extra_artists=(lgd,), )</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">close</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="enumerate2"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.enumerate2">[docs]</a><span class="k">def</span> <span class="nf">enumerate2</span><span class="p">(</span><span class="n">xs</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">step</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
|
||||
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">xs</span><span class="p">:</span>
|
||||
<span class="k">yield</span> <span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
|
||||
<span class="n">start</span> <span class="o">+=</span> <span class="n">step</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="sliding_window"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.sliding_window">[docs]</a><span class="k">def</span> <span class="nf">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">inc</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Sliding window method of cross validation for time series</span>
|
||||
|
||||
<span class="sd"> :param data: the entire dataset</span>
|
||||
<span class="sd"> :param windowsize: window size</span>
|
||||
<span class="sd"> :param train: percentual of the window size will be used for training the models</span>
|
||||
<span class="sd"> :param inc: percentual of data used for slide the window</span>
|
||||
<span class="sd"> :return: window count, training set, test set</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">multivariate</span> <span class="o">=</span> <span class="kc">True</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="k">else</span> <span class="kc">False</span>
|
||||
|
||||
<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> <span class="k">if</span> <span class="ow">not</span> <span class="n">multivariate</span> <span class="k">else</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>
|
||||
<span class="n">ttrain</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">windowsize</span> <span class="o">*</span> <span class="n">train</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
|
||||
<span class="n">ic</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">windowsize</span> <span class="o">*</span> <span class="n">inc</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
|
||||
|
||||
<span class="n">progressbar</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">'progress'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
|
||||
<span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">l</span><span class="o">-</span><span class="n">windowsize</span><span class="o">+</span><span class="n">ic</span><span class="p">,</span><span class="n">ic</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">progressbar</span><span class="p">:</span>
|
||||
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="k">import</span> <span class="n">tqdm</span>
|
||||
<span class="n">rng</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">rng</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">count</span> <span class="ow">in</span> <span class="n">rng</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">count</span> <span class="o">+</span> <span class="n">windowsize</span> <span class="o">></span> <span class="n">l</span><span class="p">:</span>
|
||||
<span class="n">_end</span> <span class="o">=</span> <span class="n">l</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">_end</span> <span class="o">=</span> <span class="n">count</span> <span class="o">+</span> <span class="n">windowsize</span>
|
||||
<span class="k">if</span> <span class="n">multivariate</span><span class="p">:</span>
|
||||
<span class="k">yield</span> <span class="p">(</span><span class="n">count</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">count</span><span class="p">:</span> <span class="n">count</span> <span class="o">+</span> <span class="n">ttrain</span><span class="p">],</span> <span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">count</span> <span class="o">+</span> <span class="n">ttrain</span><span class="p">:</span> <span class="n">_end</span><span class="p">])</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">yield</span> <span class="p">(</span><span class="n">count</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">count</span> <span class="p">:</span> <span class="n">count</span> <span class="o">+</span> <span class="n">ttrain</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="n">count</span> <span class="o">+</span> <span class="n">ttrain</span> <span class="p">:</span> <span class="n">_end</span><span class="p">]</span> <span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="persist_obj"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.persist_obj">[docs]</a><span class="k">def</span> <span class="nf">persist_obj</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">file</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Persist an object on filesystem. This function depends on Dill package</span>
|
||||
|
||||
<span class="sd"> :param obj: object on memory</span>
|
||||
<span class="sd"> :param file: file name to store the object</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="kn">import</span> <span class="nn">dill</span>
|
||||
<span class="k">try</span><span class="p">:</span>
|
||||
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="s1">'wb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">_file</span><span class="p">:</span>
|
||||
<span class="n">dill</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">_file</span><span class="p">)</span>
|
||||
<span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">ex</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"File </span><span class="si">{}</span><span class="s2"> could not be saved due exception </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">ex</span><span class="p">))</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="load_obj"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.load_obj">[docs]</a><span class="k">def</span> <span class="nf">load_obj</span><span class="p">(</span><span class="n">file</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Load to memory an object stored filesystem. This function depends on Dill package</span>
|
||||
|
||||
<span class="sd"> :param file: file name where the object is stored</span>
|
||||
<span class="sd"> :return: object</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="kn">import</span> <span class="nn">dill</span>
|
||||
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">_file</span><span class="p">:</span>
|
||||
<span class="n">obj</span> <span class="o">=</span> <span class="n">dill</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">_file</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">obj</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="persist_env"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.persist_env">[docs]</a><span class="k">def</span> <span class="nf">persist_env</span><span class="p">(</span><span class="n">file</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Persist an entire environment on file. This function depends on Dill package</span>
|
||||
|
||||
<span class="sd"> :param file: file name to store the environment</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="kn">import</span> <span class="nn">dill</span>
|
||||
<span class="n">dill</span><span class="o">.</span><span class="n">dump_session</span><span class="p">(</span><span class="n">file</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="load_env"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.load_env">[docs]</a><span class="k">def</span> <span class="nf">load_env</span><span class="p">(</span><span class="n">file</span><span class="p">):</span>
|
||||
<span class="kn">import</span> <span class="nn">dill</span>
|
||||
<span class="n">dill</span><span class="o">.</span><span class="n">load_session</span><span class="p">(</span><span class="n">file</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
</pre></div>
|
||||
|
||||
<div class="clearer"></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="sphinxsidebar" role="navigation" aria-label="main navigation">
|
||||
<div class="sphinxsidebarwrapper">
|
||||
<div id="searchbox" style="display: none" role="search">
|
||||
<h3 id="searchlabel">Quick search</h3>
|
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<div class="searchformwrapper">
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<form class="search" action="../../../search.html" method="get">
|
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<input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
|
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<input type="submit" value="Go" />
|
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</form>
|
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|
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</div>
|
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<script>$('#searchbox').show(0);</script>
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<div class="clearer"></div>
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</div>
|
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<div class="related" role="navigation" aria-label="related navigation">
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<h3>Navigation</h3>
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<ul>
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<li class="right" style="margin-right: 10px">
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<a href="../../../genindex.html" title="General Index"
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>index</a></li>
|
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<li class="right" >
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<a href="../../../py-modindex.html" title="Python Module Index"
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>modules</a> |</li>
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<li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.7 documentation</a> »</li>
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<li class="nav-item nav-item-1"><a href="../../index.html" >Module code</a> »</li>
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© Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
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@ -165,6 +165,9 @@
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741
docs/build/html/_modules/pyFTS/common/fts.html
vendored
741
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<h1>Source code for pyFTS.common.fts</h1><div class="highlight"><pre>
|
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<span></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">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">Util</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.common.transformations</span> <span class="k">import</span> <span class="n">transformation</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="k">import</span> <span class="n">partitioner</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="k">import</span> <span class="n">ProbabilityDistribution</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="FTS"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS">[docs]</a><span class="k">class</span> <span class="nc">FTS</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Fuzzy Time Series object model</span>
|
||||
<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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Create a Fuzzy Time Series model</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="sd">"""The list of Fuzzy Logical Relationship Groups - FLRG"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="p">:</span> <span class="nb">int</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">'order'</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="sd">"""A integer with the model order (number of past lags are used on forecasting)"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="p">:</span> <span class="nb">str</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">'name'</span><span class="p">,</span><span class="s2">""</span><span class="p">)</span>
|
||||
<span class="sd">"""A string with a short name or alias for the model"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="p">:</span> <span class="nb">str</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">'name'</span><span class="p">,</span><span class="s2">""</span><span class="p">)</span>
|
||||
<span class="sd">"""A string with the model name"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="p">:</span> <span class="nb">str</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">'name'</span><span class="p">,</span><span class="s2">""</span><span class="p">)</span>
|
||||
<span class="sd">"""A string with the model detailed information"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">is_wrapper</span> <span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<span class="sd">"""Indicates that this model is a wrapper for other(s) method(s)"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<span class="sd">"""A boolean value indicating if the model support orders greater than 1, default: False"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="sd">"""In high order models, this integer value indicates the minimal order supported for the model, default: 1"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">has_seasonality</span> <span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<span class="sd">"""A boolean value indicating if the model supports seasonal indexers, default: False"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">has_point_forecasting</span> <span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<span class="sd">"""A boolean value indicating if the model supports point forecasting, default: True"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<span class="sd">"""A boolean value indicating if the model supports interval forecasting, default: False"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<span class="sd">"""A boolean value indicating if the model support probabilistic forecasting, default: False"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<span class="sd">"""A boolean value indicating if the model support multivariate time series (Pandas DataFrame), default: False"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">is_clustered</span> <span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<span class="sd">"""A boolean value indicating if the model support multivariate time series (Pandas DataFrame), but works like </span>
|
||||
<span class="sd"> a monovariate method, default: False"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">dump</span> <span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">transformations</span> <span class="p">:</span> <span class="nb">list</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="o">=</span> <span class="p">[]</span>
|
||||
<span class="sd">"""A list with the data transformations (common.Transformations) applied on model pre and post processing, default: []"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">transformations_param</span> <span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="sd">"""A list with the specific parameters for each data transformation"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.0</span>
|
||||
<span class="sd">"""A float with the upper limit of the Universe of Discourse, the maximal value found on training data"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.0</span>
|
||||
<span class="sd">"""A float with the lower limit of the Universe of Discourse, the minimal value found on training data"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="p">:</span> <span class="n">partitioner</span><span class="o">.</span><span class="n">Partitioner</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">"partitioner"</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
<span class="sd">"""A pyFTS.partitioners.Partitioner object with the Universe of Discourse partitioner used on the model. This is a mandatory dependecy. """</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">!=</span> <span class="kc">None</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">partitioner</span><span class="o">.</span><span class="n">sets</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">auto_update</span> <span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<span class="sd">"""A boolean value indicating that model is incremental"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">benchmark_only</span> <span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<span class="sd">"""A boolean value indicating a façade for external (non-FTS) model used on benchmarks or ensembles."""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">indexer</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">"indexer"</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
<span class="sd">"""An pyFTS.models.seasonal.Indexer object for indexing the time series data"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="p">:</span> <span class="nb">bool</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">"uod_clip"</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
|
||||
<span class="sd">"""Flag indicating if the test data will be clipped inside the training Universe of Discourse"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">alpha_cut</span> <span class="p">:</span> <span class="nb">float</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_cut"</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)</span>
|
||||
<span class="sd">"""A float with the minimal membership to be considered on fuzzyfication process"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">lags</span> <span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">int</span><span class="p">]</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">"lags"</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
<span class="sd">"""The list of lag indexes for high order models"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
|
||||
<span class="sd">"""A integer indicating the largest lag used by the model. This value also indicates the minimum number of past lags </span>
|
||||
<span class="sd"> needed to forecast a single step ahead"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([],</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">"Datetime"</span><span class="p">,</span><span class="s2">"Operation"</span><span class="p">,</span><span class="s2">"Value"</span><span class="p">])</span>
|
||||
<span class="sd">""""""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">is_time_variant</span> <span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<span class="sd">"""A boolean value indicating if this model is time variant"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">standard_horizon</span> <span class="p">:</span> <span class="nb">int</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">"standard_horizon"</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
||||
<span class="sd">"""Standard forecasting horizon (Default: 1)"""</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="FTS.fuzzy"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.fuzzy">[docs]</a> <span class="k">def</span> <span class="nf">fuzzy</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="nb">dict</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Fuzzify a data point</span>
|
||||
|
||||
<span class="sd"> :param data: data point</span>
|
||||
<span class="sd"> :return: maximum membership fuzzy set</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">best</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"fuzzyset"</span><span class="p">:</span> <span class="s2">""</span><span class="p">,</span> <span class="s2">"membership"</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">}</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">:</span>
|
||||
<span class="n">fset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">f</span><span class="p">]</span>
|
||||
<span class="k">if</span> <span class="n">best</span><span class="p">[</span><span class="s2">"membership"</span><span class="p">]</span> <span class="o"><=</span> <span class="n">fset</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
|
||||
<span class="n">best</span><span class="p">[</span><span class="s2">"fuzzyset"</span><span class="p">]</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">name</span>
|
||||
<span class="n">best</span><span class="p">[</span><span class="s2">"membership"</span><span class="p">]</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">best</span></div>
|
||||
|
||||
<div class="viewcode-block" id="FTS.clip_uod"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.clip_uod">[docs]</a> <span class="k">def</span> <span class="nf">clip_uod</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</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">partitioner</span><span class="o">.</span><span class="n">min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">max</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span><span class="p">:</span>
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</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">original_min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">ndata</span></div>
|
||||
|
||||
<div class="viewcode-block" id="FTS.predict"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.predict">[docs]</a> <span class="k">def</span> <span class="nf">predict</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>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Forecast using trained model</span>
|
||||
|
||||
<span class="sd"> :param data: time series with minimal length to the order of the model</span>
|
||||
|
||||
<span class="sd"> :keyword type: the forecasting type, one of these values: point(default), interval, distribution or multivariate.</span>
|
||||
<span class="sd"> :keyword steps_ahead: The forecasting path H, i. e., tell the model to forecast from t+1 to t+H.</span>
|
||||
<span class="sd"> :keyword step_to: The forecasting step H, i. e., tell the model to forecast to t+H for each input sample </span>
|
||||
<span class="sd"> :keyword start_at: in the multi step forecasting, the index of the data where to start forecasting (default value: 0)</span>
|
||||
<span class="sd"> :keyword distributed: boolean, indicate if the forecasting procedure will be distributed in a dispy cluster (default value: False)</span>
|
||||
<span class="sd"> :keyword nodes: a list with the dispy cluster nodes addresses</span>
|
||||
<span class="sd"> :keyword explain: try to explain, step by step, the one-step-ahead point forecasting result given the input data. (default value: False)</span>
|
||||
<span class="sd"> :keyword generators: for multivariate methods on multi step ahead forecasting, generators is a dict where the keys</span>
|
||||
<span class="sd"> are the dataframe columun names (except the target_variable) and the values are lambda functions that</span>
|
||||
<span class="sd"> accept one value (the actual value of the variable) and return the next value or trained FTS</span>
|
||||
<span class="sd"> models that accept the actual values and forecast new ones.</span>
|
||||
|
||||
<span class="sd"> :return: a numpy array with the forecasted data</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="kn">import</span> <span class="nn">copy</span>
|
||||
|
||||
<span class="n">kw</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="n">data</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</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="bp">self</span><span class="o">.</span><span class="n">clip_uod</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="s1">'distributed'</span> <span class="ow">in</span> <span class="n">kw</span><span class="p">:</span>
|
||||
<span class="n">distributed</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'distributed'</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">distributed</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
|
||||
<span class="k">if</span> <span class="s1">'type'</span> <span class="ow">in</span> <span class="n">kw</span><span class="p">:</span>
|
||||
<span class="nb">type</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">"type"</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="nb">type</span> <span class="o">=</span> <span class="s1">'point'</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">distributed</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">distributed</span> <span class="o">==</span> <span class="kc">False</span><span class="p">:</span>
|
||||
|
||||
<span class="n">steps_ahead</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"steps_ahead"</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
|
||||
<span class="n">step_to</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"step_to"</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="p">(</span><span class="n">steps_ahead</span> <span class="o">==</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">step_to</span> <span class="o">==</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">steps_ahead</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">or</span> <span class="n">step_to</span> <span class="o">==</span><span class="mi">1</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">'point'</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">'interval'</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">'distribution'</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_distribution</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">'multivariate'</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_multivariate</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">step_to</span> <span class="o">==</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">steps_ahead</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">'point'</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">'interval'</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead_interval</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">'distribution'</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead_distribution</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">'multivariate'</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead_multivariate</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">step_to</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">'point'</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_step</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">step_to</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">'This model only perform point step ahead forecasts!'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="p">[</span><span class="s1">'point'</span><span class="p">,</span> <span class="s1">'interval'</span><span class="p">,</span> <span class="s1">'distribution'</span><span class="p">,</span> <span class="s1">'multivariate'</span><span class="p">]</span><span class="o">.</span><span class="fm">__contains__</span><span class="p">(</span><span class="nb">type</span><span class="p">):</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'The argument </span><span class="se">\'</span><span class="s1">type</span><span class="se">\'</span><span class="s1"> has an unknown value.'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">distributed</span> <span class="o">==</span> <span class="s1">'dispy'</span><span class="p">:</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.distributed</span> <span class="k">import</span> <span class="n">dispy</span>
|
||||
|
||||
<span class="n">nodes</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">"nodes"</span><span class="p">,</span> <span class="p">[</span><span class="s1">'127.0.0.1'</span><span class="p">])</span>
|
||||
<span class="n">num_batches</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'num_batches'</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">dispy</span><span class="o">.</span><span class="n">distributed_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">kw</span><span class="p">,</span> <span class="n">nodes</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="n">num_batches</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
|
||||
|
||||
<span class="k">elif</span> <span class="n">distributed</span> <span class="o">==</span> <span class="s1">'spark'</span><span class="p">:</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.distributed</span> <span class="k">import</span> <span class="n">spark</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">distributed_predict</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">ndata</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
|
||||
<span class="n">kw</span><span class="p">[</span><span class="s1">'type'</span><span class="p">]</span> <span class="o">=</span> <span class="nb">type</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_inverse_transformations</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:]],</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="s1">'statistics'</span> <span class="ow">in</span> <span class="n">kw</span><span class="p">:</span>
|
||||
<span class="n">kwargs</span><span class="p">[</span><span class="s1">'statistics'</span><span class="p">]</span> <span class="o">=</span> <span class="n">kw</span><span class="p">[</span><span class="s1">'statistics'</span><span class="p">]</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="FTS.forecast"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.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> <span class="o">-></span> <span class="nb">list</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Point forecast one step ahead</span>
|
||||
|
||||
<span class="sd"> :param data: time series data with the minimal length equal to the max_lag of the model</span>
|
||||
<span class="sd"> :param kwargs: model specific parameters</span>
|
||||
<span class="sd"> :return: a list with the forecasted values</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">'This model do not perform one step ahead point forecasts!'</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="FTS.forecast_interval"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.forecast_interval">[docs]</a> <span class="k">def</span> <span class="nf">forecast_interval</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> <span class="o">-></span> <span class="nb">list</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Interval forecast one step ahead</span>
|
||||
|
||||
<span class="sd"> :param data: time series data with the minimal length equal to the max_lag of the model</span>
|
||||
<span class="sd"> :param kwargs: model specific parameters</span>
|
||||
<span class="sd"> :return: a list with the prediction intervals</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">'This model do not perform one step ahead interval forecasts!'</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="FTS.forecast_distribution"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.forecast_distribution">[docs]</a> <span class="k">def</span> <span class="nf">forecast_distribution</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> <span class="o">-></span> <span class="nb">list</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Probabilistic forecast one step ahead</span>
|
||||
|
||||
<span class="sd"> :param data: time series data with the minimal length equal to the max_lag of the model</span>
|
||||
<span class="sd"> :param kwargs: model specific parameters</span>
|
||||
<span class="sd"> :return: a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">'This model do not perform one step ahead distribution forecasts!'</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="FTS.forecast_multivariate"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.forecast_multivariate">[docs]</a> <span class="k">def</span> <span class="nf">forecast_multivariate</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> <span class="o">-></span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Multivariate forecast one step ahead</span>
|
||||
|
||||
<span class="sd"> :param data: Pandas dataframe with one column for each variable and with the minimal length equal to the max_lag of the model</span>
|
||||
<span class="sd"> :param kwargs: model specific parameters</span>
|
||||
<span class="sd"> :return: a Pandas Dataframe object representing the forecasted values for each variable</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">'This model do not perform one step ahead multivariate forecasts!'</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="FTS.forecast_ahead"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.forecast_ahead">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead</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="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-></span> <span class="nb">list</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Point forecast from 1 to H steps ahead, where H is given by the steps parameter</span>
|
||||
|
||||
<span class="sd"> :param data: time series data with the minimal length equal to the max_lag of the model</span>
|
||||
<span class="sd"> :param steps: the number of steps ahead to forecast (default: 1)</span>
|
||||
<span class="sd"> :keyword start_at: in the multi step forecasting, the index of the data where to start forecasting (default: 0)</span>
|
||||
<span class="sd"> :return: a list with the forecasted values</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<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="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">data</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
|
||||
|
||||
<span class="n">start</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">'start_at'</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">data</span><span class="p">[:</span><span class="n">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</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">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">steps</span><span class="o">+</span><span class="n">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">):</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">ret</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">max_lag</span><span class="p">:</span><span class="n">k</span><span class="p">],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tmp</span><span class="p">,(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
|
||||
<span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div>
|
||||
|
||||
<div class="viewcode-block" id="FTS.forecast_ahead_interval"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.forecast_ahead_interval">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead_interval</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="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-></span> <span class="nb">list</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Interval forecast from 1 to H steps ahead, where H is given by the steps parameter</span>
|
||||
|
||||
<span class="sd"> :param data: time series data with the minimal length equal to the max_lag of the model</span>
|
||||
<span class="sd"> :param steps: the number of steps ahead to forecast</span>
|
||||
<span class="sd"> :keyword start_at: in the multi step forecasting, the index of the data where to start forecasting (default: 0)</span>
|
||||
<span class="sd"> :return: a list with the forecasted intervals</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">'This model do not perform multi step ahead interval forecasts!'</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="FTS.forecast_ahead_distribution"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.forecast_ahead_distribution">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead_distribution</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="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-></span> <span class="nb">list</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Probabilistic forecast from 1 to H steps ahead, where H is given by the steps parameter</span>
|
||||
|
||||
<span class="sd"> :param data: time series data with the minimal length equal to the max_lag of the model</span>
|
||||
<span class="sd"> :param steps: the number of steps ahead to forecast</span>
|
||||
<span class="sd"> :keyword start_at: in the multi step forecasting, the index of the data where to start forecasting (default: 0)</span>
|
||||
<span class="sd"> :return: a list with the forecasted Probability Distributions</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">'This model do not perform multi step ahead distribution forecasts!'</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="FTS.forecast_ahead_multivariate"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.forecast_ahead_multivariate">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead_multivariate</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="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-></span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Multivariate forecast n step ahead</span>
|
||||
|
||||
<span class="sd"> :param data: Pandas dataframe with one column for each variable and with the minimal length equal to the max_lag of the model</span>
|
||||
<span class="sd"> :param steps: the number of steps ahead to forecast</span>
|
||||
<span class="sd"> :keyword start_at: in the multi step forecasting, the index of the data where to start forecasting (default: 0)</span>
|
||||
<span class="sd"> :return: a Pandas Dataframe object representing the forecasted values for each variable</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">'This model do not perform one step ahead multivariate forecasts!'</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="FTS.forecast_step"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.forecast_step">[docs]</a> <span class="k">def</span> <span class="nf">forecast_step</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="n">step</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-></span> <span class="nb">list</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Point forecast for H steps ahead, where H is given by the step parameter</span>
|
||||
|
||||
<span class="sd"> :param data: time series data with the minimal length equal to the max_lag of the model</span>
|
||||
<span class="sd"> :param step: the forecasting horizon (default: 1)</span>
|
||||
<span class="sd"> :keyword start_at: in the multi step forecasting, the index of the data where to start forecasting (default: 0)</span>
|
||||
<span class="sd"> :return: a list with the forecasted values</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<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>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">l</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">data</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
|
||||
|
||||
<span class="n">start</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">'start_at'</span><span class="p">,</span><span class="mi">0</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">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
|
||||
<span class="n">sample</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">max_lag</span><span class="p">:</span><span class="n">k</span><span class="p">]</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">step</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tmp</span><span class="p">,(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="FTS.train"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.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>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Method specific parameter fitting</span>
|
||||
|
||||
<span class="sd"> :param data: training time series data</span>
|
||||
<span class="sd"> :param kwargs: Method specific parameters</span>
|
||||
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">pass</span></div>
|
||||
|
||||
<div class="viewcode-block" id="FTS.fit"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.fit">[docs]</a> <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Fit the model's parameters based on the training data.</span>
|
||||
|
||||
<span class="sd"> :param ndata: training time series data</span>
|
||||
<span class="sd"> :param kwargs:</span>
|
||||
|
||||
<span class="sd"> :keyword num_batches: split the training data in num_batches to save memory during the training process</span>
|
||||
<span class="sd"> :keyword save_model: save final model on disk</span>
|
||||
<span class="sd"> :keyword batch_save: save the model between each batch</span>
|
||||
<span class="sd"> :keyword file_path: path to save the model</span>
|
||||
<span class="sd"> :keyword distributed: boolean, indicate if the training procedure will be distributed in a dispy cluster</span>
|
||||
<span class="sd"> :keyword nodes: a list with the dispy cluster nodes addresses</span>
|
||||
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">datetime</span><span class="o">,</span> <span class="nn">copy</span>
|
||||
|
||||
<span class="n">kw</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">ndata</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</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">original_min</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmin</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">original_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">data</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="s1">'partitioner'</span> <span class="ow">in</span> <span class="n">kw</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'partitioner'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_wrapper</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">benchmark_only</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">"Fuzzy sets were not provided for the model. Use 'partitioner' parameter. "</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="s1">'order'</span> <span class="ow">in</span> <span class="n">kw</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'order'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">dump</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'dump'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
|
||||
<span class="n">num_batches</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'num_batches'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
|
||||
<span class="n">save</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'save_model'</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span> <span class="c1"># save model on disk</span>
|
||||
|
||||
<span class="n">batch_save</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'batch_save'</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span> <span class="c1">#save model between batches</span>
|
||||
|
||||
<span class="n">file_path</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'file_path'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
|
||||
<span class="n">distributed</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'distributed'</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">distributed</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">distributed</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">num_batches</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">num_batches</span> <span class="o">=</span> <span class="mi">10</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">distributed</span> <span class="o">==</span> <span class="s1">'dispy'</span><span class="p">:</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.distributed</span> <span class="k">import</span> <span class="n">dispy</span>
|
||||
<span class="n">nodes</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'nodes'</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
|
||||
<span class="n">train_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">'train_method'</span><span class="p">,</span> <span class="n">dispy</span><span class="o">.</span><span class="n">simple_model_train</span><span class="p">)</span>
|
||||
<span class="n">dispy</span><span class="o">.</span><span class="n">distributed_train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">train_method</span><span class="p">,</span> <span class="n">nodes</span><span class="p">,</span> <span class="nb">type</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="n">num_batches</span><span class="p">,</span> <span class="p">{},</span>
|
||||
<span class="o">**</span><span class="n">kw</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">distributed</span> <span class="o">==</span> <span class="s1">'spark'</span><span class="p">:</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.distributed</span> <span class="k">import</span> <span class="n">spark</span>
|
||||
<span class="n">url</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">'url'</span><span class="p">,</span> <span class="s1">'spark://127.0.0.1:7077'</span><span class="p">)</span>
|
||||
<span class="n">app</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">'app'</span><span class="p">,</span> <span class="s1">'pyFTS'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">spark</span><span class="o">.</span><span class="n">distributed_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="n">url</span><span class="o">=</span><span class="n">url</span><span class="p">,</span> <span class="n">app</span><span class="o">=</span><span class="n">app</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">dump</span> <span class="o">==</span> <span class="s1">'time'</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"[{0: %H:%M:%S}] Start training"</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="k">if</span> <span class="n">num_batches</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_wrapper</span><span class="p">:</span>
|
||||
<span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
<span class="n">batch_size</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">n</span> <span class="o">/</span> <span class="n">num_batches</span><span class="p">)</span>
|
||||
<span class="n">bcount</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
|
||||
<span class="n">rng</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">dump</span> <span class="o">==</span> <span class="s1">'tqdm'</span><span class="p">:</span>
|
||||
<span class="kn">from</span> <span class="nn">tqdm</span> <span class="k">import</span> <span class="n">tqdm</span>
|
||||
|
||||
<span class="n">rng</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">rng</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">ct</span> <span class="ow">in</span> <span class="n">rng</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">dump</span> <span class="o">==</span> <span class="s1">'time'</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"[{0: %H:%M:%S}] Starting batch "</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="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">bcount</span><span class="p">))</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
|
||||
<span class="n">mdata</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">ct</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">ct</span> <span class="o">+</span> <span class="n">batch_size</span><span class="p">]</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">mdata</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">ct</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">ct</span> <span class="o">+</span> <span class="n">batch_size</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">mdata</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">batch_save</span><span class="p">:</span>
|
||||
<span class="n">Util</span><span class="o">.</span><span class="n">persist_obj</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">file_path</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">dump</span> <span class="o">==</span> <span class="s1">'time'</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"[{0: %H:%M:%S}] Finish batch "</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="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">bcount</span><span class="p">))</span>
|
||||
|
||||
<span class="n">bcount</span> <span class="o">+=</span> <span class="mi">1</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">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">dump</span> <span class="o">==</span> <span class="s1">'time'</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"[{0: %H:%M:%S}] Finish training"</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="k">if</span> <span class="n">save</span><span class="p">:</span>
|
||||
<span class="n">Util</span><span class="o">.</span><span class="n">persist_obj</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">file_path</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="FTS.clone_parameters"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.clone_parameters">[docs]</a> <span class="k">def</span> <span class="nf">clone_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Import the parameters values from other model</span>
|
||||
|
||||
<span class="sd"> :param model: a model to clone the parameters</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">partitioner</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">lags</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">lags</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">shortname</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">name</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">detail</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">is_high_order</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">min_order</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">has_seasonality</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">has_seasonality</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">has_point_forecasting</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">has_point_forecasting</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">has_interval_forecasting</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">has_probability_forecasting</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">dump</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">dump</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">transformations</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transformations</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">transformations_param</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transformations_param</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">original_max</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">original_min</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">auto_update</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">auto_update</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">benchmark_only</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">benchmark_only</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">indexer</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">indexer</span></div>
|
||||
|
||||
<div class="viewcode-block" id="FTS.append_rule"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.append_rule">[docs]</a> <span class="k">def</span> <span class="nf">append_rule</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Append FLRG rule to the model</span>
|
||||
|
||||
<span class="sd"> :param flrg: rule</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</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="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span> <span class="o">=</span> <span class="n">flrg</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">RHS</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">set</span><span class="p">)):</span>
|
||||
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">RHS</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">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">RHS</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
|
||||
<span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">items</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">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">count</span><span class="o">=</span><span class="n">value</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">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="FTS.merge"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.merge">[docs]</a> <span class="k">def</span> <span class="nf">merge</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Merge the FLRG rules from other model</span>
|
||||
|
||||
<span class="sd"> :param model: source model</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">append_rule</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="FTS.append_transformation"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.append_transformation">[docs]</a> <span class="k">def</span> <span class="nf">append_transformation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transformation</span><span class="p">):</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="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">transformation</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="FTS.apply_transformations"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.apply_transformations">[docs]</a> <span class="k">def</span> <span class="nf">apply_transformations</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="n">params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">updateUoD</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Apply the data transformations for data preprocessing</span>
|
||||
|
||||
<span class="sd"> :param data: input data</span>
|
||||
<span class="sd"> :param params: transformation parameters</span>
|
||||
<span class="sd"> :param updateUoD:</span>
|
||||
<span class="sd"> :param kwargs:</span>
|
||||
<span class="sd"> :return: preprocessed data</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="n">data</span>
|
||||
<span class="k">if</span> <span class="n">updateUoD</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="nb">min</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o"><</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1.1</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.9</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">max</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1.1</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.9</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">params</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">params</span> <span class="o">=</span> <span class="p">[</span> <span class="kc">None</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">]</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">c</span><span class="p">,</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transformations</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">ndata</span> <span class="o">=</span> <span class="n">t</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">params</span><span class="p">[</span><span class="n">c</span><span class="p">],</span> <span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ndata</span></div>
|
||||
|
||||
<div class="viewcode-block" id="FTS.apply_inverse_transformations"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.apply_inverse_transformations">[docs]</a> <span class="k">def</span> <span class="nf">apply_inverse_transformations</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="n">params</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">"""</span>
|
||||
<span class="sd"> Apply the data transformations for data postprocessing</span>
|
||||
|
||||
<span class="sd"> :param data: input data</span>
|
||||
<span class="sd"> :param params: transformation parameters</span>
|
||||
<span class="sd"> :param updateUoD:</span>
|
||||
<span class="sd"> :param kwargs:</span>
|
||||
<span class="sd"> :return: postprocessed data</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">params</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">params</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">]</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">c</span><span class="p">,</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">reversed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transformations</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">ndata</span> <span class="o">=</span> <span class="n">t</span><span class="o">.</span><span class="n">inverse</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">params</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="k">return</span> <span class="n">ndata</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">data</span></div>
|
||||
|
||||
<div class="viewcode-block" id="FTS.get_UoD"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.get_UoD">[docs]</a> <span class="k">def</span> <span class="nf">get_UoD</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="nb">set</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Returns the interval of the known bounds of the universe of discourse (UoD), i. e.,</span>
|
||||
<span class="sd"> the known minimum and maximum values of the time series.</span>
|
||||
|
||||
<span class="sd"> :return: A set with the lower and the upper bounds of the UoD</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">max</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">original_min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="FTS.offset"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.offset">[docs]</a> <span class="k">def</span> <span class="nf">offset</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="nb">int</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Returns the number of lags to skip in the input test data in order to synchronize it with</span>
|
||||
<span class="sd"> the forecasted values given by the predict function. This is necessary due to the order of the</span>
|
||||
<span class="sd"> model, among other parameters.</span>
|
||||
|
||||
<span class="sd"> :return: An integer with the number of lags to skip</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="mi">1</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">"""</span>
|
||||
<span class="sd"> String representation of the model</span>
|
||||
|
||||
<span class="sd"> :return: a string containing the name of the model and the learned rules</span>
|
||||
<span class="sd"> (if the model was already trained)</span>
|
||||
<span class="sd"> """</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">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s1">'common'</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="nb">sorted</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">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">key</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">key</span><span class="p">]</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)):</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="s2">"</span><span class="si">{0}{1}</span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</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="k">else</span><span class="p">:</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="s2">"</span><span class="si">{0}{1}</span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</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="k">return</span> <span class="n">tmp</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">flrgs</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="FTS.len_total"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.len_total">[docs]</a> <span class="k">def</span> <span class="nf">len_total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Total length of the model, adding the number of terms in all rules</span>
|
||||
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="nb">sum</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">])</span></div>
|
||||
|
||||
<div class="viewcode-block" id="FTS.reset_calculated_values"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.reset_calculated_values">[docs]</a> <span class="k">def</span> <span class="nf">reset_calculated_values</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Reset all pre-calculated values on the FLRG's</span>
|
||||
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="o">.</span><span class="n">keys</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">flrg</span><span class="p">]</span><span class="o">.</span><span class="n">reset_calculated_values</span><span class="p">()</span></div>
|
||||
|
||||
<div class="viewcode-block" id="FTS.append_log"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.append_log">[docs]</a> <span class="k">def</span> <span class="nf">append_log</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">operation</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
|
||||
<span class="k">pass</span></div></div>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</pre></div>
|
||||
|
||||
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|
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<li class="nav-item nav-item-this"><a href="">pyFTS.common.transformations.trend</a></li>
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<h1>Source code for pyFTS.common.transformations.trend</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">from</span> <span class="nn">pyFTS.common.transformations.transformation</span> <span class="k">import</span> <span class="n">Transformation</span>
|
||||
<span class="kn">from</span> <span class="nn">pandas</span> <span class="k">import</span> <span class="n">datetime</span>
|
||||
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="k">import</span> <span class="n">LinearRegression</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>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="LinearTrend"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.trend.LinearTrend">[docs]</a><span class="k">class</span> <span class="nc">LinearTrend</span><span class="p">(</span><span class="n">Transformation</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Linear Trend. Estimate</span>
|
||||
|
||||
<span class="sd"> y'(t) = y(t) - (a*t+b)</span>
|
||||
<span class="sd"> y(t) = y'(t) + (a*t+b)</span>
|
||||
<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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">LinearTrend</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="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">'LinearTrend'</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">index_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">'index_type'</span><span class="p">,</span><span class="s1">'linear'</span><span class="p">)</span>
|
||||
<span class="sd">'''The type of the time index used to train the regression coefficients. Available types are: field, datetime'''</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">index_field</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">'index_field'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
<span class="sd">'''The Pandas Dataframe column to use as index'''</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">data_field</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">'data_field'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
<span class="sd">'''The Pandas Dataframe column to use as data'''</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">datetime_mask</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">'datetime_mask'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
<span class="sd">'''The Pandas Dataframe mask for datetime indexes '''</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="sd">'''Regression model'''</span>
|
||||
|
||||
<div class="viewcode-block" id="LinearTrend.train"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.trend.LinearTrend.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>
|
||||
|
||||
<span class="n">x</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">index_field</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">index_type</span> <span class="o">==</span> <span class="s1">'datetime'</span><span class="p">:</span>
|
||||
<span class="n">x</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_numeric</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">downcast</span><span class="o">=</span><span class="s1">'integer'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">indexes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="mi">1</span><span class="p">))</span>
|
||||
<span class="n">values</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">data_field</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">LinearRegression</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">fit</span><span class="p">(</span><span class="n">indexes</span><span class="p">,</span> <span class="n">values</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="LinearTrend.trend"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.trend.LinearTrend.trend">[docs]</a> <span class="k">def</span> <span class="nf">trend</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="n">x</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">index_field</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">index_type</span> <span class="o">==</span> <span class="s1">'datetime'</span><span class="p">:</span>
|
||||
<span class="n">x</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_numeric</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">downcast</span><span class="o">=</span><span class="s1">'integer'</span><span class="p">)</span>
|
||||
<span class="n">indexes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="mi">1</span><span class="p">))</span>
|
||||
<span class="n">_trend</span> <span class="o">=</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">indexes</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">_trend</span></div>
|
||||
|
||||
<div class="viewcode-block" id="LinearTrend.apply"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.trend.LinearTrend.apply">[docs]</a> <span class="k">def</span> <span class="nf">apply</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="n">param</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="n">values</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">data_field</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">_trend</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">trend</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
<span class="n">modified</span> <span class="o">=</span> <span class="n">values</span> <span class="o">-</span> <span class="n">_trend</span>
|
||||
<span class="k">return</span> <span class="n">modified</span></div>
|
||||
|
||||
<div class="viewcode-block" id="LinearTrend.inverse"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.trend.LinearTrend.inverse">[docs]</a> <span class="k">def</span> <span class="nf">inverse</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="n">param</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="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_indexes</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">param</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">index_field</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="n">indexes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="mi">1</span><span class="p">))</span>
|
||||
<span class="n">_trend</span> <span class="o">=</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">indexes</span><span class="p">)</span>
|
||||
<span class="n">modified</span> <span class="o">=</span> <span class="n">data</span> <span class="o">+</span> <span class="n">_trend</span>
|
||||
<span class="k">return</span> <span class="n">modified</span></div>
|
||||
|
||||
<div class="viewcode-block" id="LinearTrend.increment"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.trend.LinearTrend.increment">[docs]</a> <span class="k">def</span> <span class="nf">increment</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">value</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">index_type</span> <span class="o">==</span> <span class="s1">'linear'</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">value</span> <span class="o">+</span> <span class="mi">1</span>
|
||||
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">index_type</span> <span class="o">==</span> <span class="s1">'datetime'</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="s1">'date_offset'</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">'A pandas.DateOffset must be passed in the parameter ''date_offset'''</span><span class="p">)</span>
|
||||
<span class="n">doff</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">'date_offset'</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">value</span> <span class="o">+</span> <span class="n">doff</span></div>
|
||||
|
||||
<div class="viewcode-block" id="LinearTrend.generate_indexes"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.trend.LinearTrend.generate_indexes">[docs]</a> <span class="k">def</span> <span class="nf">generate_indexes</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="n">value</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">index_type</span> <span class="o">==</span> <span class="s1">'datetime'</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">increment</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">datetime_mask</span><span class="p">),</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)]</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">increment</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)]</span>
|
||||
<span class="k">for</span> <span class="n">i</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="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)):</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">increment</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">))</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">index_type</span> <span class="o">==</span> <span class="s1">'datetime'</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">ret</span><span class="p">)</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_numeric</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span> <span class="n">downcast</span><span class="o">=</span><span class="s1">'integer'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ret</span><span class="p">)</span></div></div>
|
||||
</pre></div>
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<h1>Source code for pyFTS.distributed.dispy</h1><div class="highlight"><pre>
|
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<span></span><span class="kn">import</span> <span class="nn">dispy</span> <span class="k">as</span> <span class="nn">dispy</span><span class="o">,</span> <span class="nn">dispy.httpd</span><span class="o">,</span> <span class="nn">logging</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">Util</span>
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
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<div class="viewcode-block" id="start_dispy_cluster"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.dispy.start_dispy_cluster">[docs]</a><span class="k">def</span> <span class="nf">start_dispy_cluster</span><span class="p">(</span><span class="n">method</span><span class="p">,</span> <span class="n">nodes</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Start a new Dispy cluster on 'nodes' to execute the method 'method'</span>
|
||||
|
||||
<span class="sd"> :param method: function to be executed on each cluster node</span>
|
||||
<span class="sd"> :param nodes: list of node names or IP's.</span>
|
||||
<span class="sd"> :return: the dispy cluster instance and the http_server for monitoring</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">cluster</span> <span class="o">=</span> <span class="n">dispy</span><span class="o">.</span><span class="n">JobCluster</span><span class="p">(</span><span class="n">method</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="n">nodes</span><span class="p">,</span> <span class="n">loglevel</span><span class="o">=</span><span class="n">logging</span><span class="o">.</span><span class="n">DEBUG</span><span class="p">,</span> <span class="n">ping_interval</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
|
||||
|
||||
<span class="n">http_server</span> <span class="o">=</span> <span class="n">dispy</span><span class="o">.</span><span class="n">httpd</span><span class="o">.</span><span class="n">DispyHTTPServer</span><span class="p">(</span><span class="n">cluster</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="stop_dispy_cluster"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.dispy.stop_dispy_cluster">[docs]</a><span class="k">def</span> <span class="nf">stop_dispy_cluster</span><span class="p">(</span><span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Stop a dispy cluster and http_server</span>
|
||||
|
||||
<span class="sd"> :param cluster:</span>
|
||||
<span class="sd"> :param http_server:</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="c1">#cluster.wait() # wait for all jobs to finish</span>
|
||||
|
||||
<span class="n">cluster</span><span class="o">.</span><span class="n">print_status</span><span class="p">()</span>
|
||||
|
||||
<span class="n">http_server</span><span class="o">.</span><span class="n">shutdown</span><span class="p">()</span> <span class="c1"># this waits until browser gets all updates</span>
|
||||
<span class="n">cluster</span><span class="o">.</span><span class="n">close</span><span class="p">()</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="get_number_of_cpus"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.dispy.get_number_of_cpus">[docs]</a><span class="k">def</span> <span class="nf">get_number_of_cpus</span><span class="p">(</span><span class="n">cluster</span><span class="p">):</span>
|
||||
<span class="n">cpus</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="k">for</span> <span class="n">dispy_node</span> <span class="ow">in</span> <span class="n">cluster</span><span class="o">.</span><span class="n">status</span><span class="p">()</span><span class="o">.</span><span class="n">nodes</span><span class="p">:</span>
|
||||
<span class="n">cpus</span> <span class="o">+=</span> <span class="n">dispy_node</span><span class="o">.</span><span class="n">cpus</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">cpus</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="simple_model_train"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.dispy.simple_model_train">[docs]</a><span class="k">def</span> <span class="nf">simple_model_train</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">parameters</span><span class="p">):</span>
|
||||
<span class="kn">import</span> <span class="nn">time</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Cluster function that receives a FTS instance 'model' and train using the 'data' and 'parameters'</span>
|
||||
|
||||
<span class="sd"> :param model: a FTS instance</span>
|
||||
<span class="sd"> :param data: training dataset</span>
|
||||
<span class="sd"> :param parameters: parameters for the training process</span>
|
||||
<span class="sd"> :return: the trained model</span>
|
||||
<span class="sd"> """</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">model</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</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">model</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">'training_time'</span><span class="p">]</span> <span class="o">=</span> <span class="n">_end</span> <span class="o">-</span> <span class="n">_start</span>
|
||||
<span class="k">return</span> <span class="n">model</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="distributed_train"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.dispy.distributed_train">[docs]</a><span class="k">def</span> <span class="nf">distributed_train</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">train_method</span><span class="p">,</span> <span class="n">nodes</span><span class="p">,</span> <span class="n">fts_method</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">num_batches</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
|
||||
<span class="n">train_parameters</span><span class="o">=</span><span class="p">{},</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="kn">import</span> <span class="nn">dispy</span><span class="o">,</span> <span class="nn">dispy.httpd</span><span class="o">,</span> <span class="nn">datetime</span>
|
||||
|
||||
<span class="n">batch_save</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">'batch_save'</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span> <span class="c1"># save model between batches</span>
|
||||
|
||||
<span class="n">batch_save_interval</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">'batch_save_interval'</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="n">file_path</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">'file_path'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
|
||||
<span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span> <span class="o">=</span> <span class="n">start_dispy_cluster</span><span class="p">(</span><span class="n">train_method</span><span class="p">,</span> <span class="n">nodes</span><span class="p">)</span>
|
||||
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"[{0: %H:%M:%S}] Distrituted Train Started with </span><span class="si">{1}</span><span class="s2"> CPU's"</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">get_number_of_cpus</span><span class="p">(</span><span class="n">cluster</span><span class="p">)))</span>
|
||||
|
||||
<span class="n">jobs</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
<span class="n">batch_size</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">n</span> <span class="o">/</span> <span class="n">num_batches</span><span class="p">)</span>
|
||||
<span class="n">bcount</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="k">for</span> <span class="n">ct</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">ct</span> <span class="o">-</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">:</span><span class="n">ct</span> <span class="o">+</span> <span class="n">batch_size</span><span class="p">]</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">ct</span> <span class="o">-</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">:</span> <span class="n">ct</span> <span class="o">+</span> <span class="n">batch_size</span><span class="p">]</span>
|
||||
|
||||
<span class="n">tmp_model</span> <span class="o">=</span> <span class="n">fts_method</span><span class="p">()</span>
|
||||
|
||||
<span class="n">tmp_model</span><span class="o">.</span><span class="n">clone_parameters</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
|
||||
|
||||
<span class="n">job</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">submit</span><span class="p">(</span><span class="n">tmp_model</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="n">train_parameters</span><span class="p">)</span>
|
||||
<span class="n">job</span><span class="o">.</span><span class="n">id</span> <span class="o">=</span> <span class="n">bcount</span> <span class="c1"># associate an ID to identify jobs (if needed later)</span>
|
||||
<span class="n">jobs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">job</span><span class="p">)</span>
|
||||
|
||||
<span class="n">bcount</span> <span class="o">+=</span> <span class="mi">1</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">job</span> <span class="ow">in</span> <span class="n">jobs</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"[{0: %H:%M:%S}] Processing batch "</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="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">id</span><span class="p">))</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="n">job</span><span class="p">()</span>
|
||||
<span class="k">if</span> <span class="n">job</span><span class="o">.</span><span class="n">status</span> <span class="o">==</span> <span class="n">dispy</span><span class="o">.</span><span class="n">DispyJob</span><span class="o">.</span><span class="n">Finished</span> <span class="ow">and</span> <span class="n">tmp</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="s1">'training_time'</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">:</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">'training_time'</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">'training_time'</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">'training_time'</span><span class="p">])</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">batch_save</span> <span class="ow">and</span> <span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">id</span> <span class="o">%</span> <span class="n">batch_save_interval</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">Util</span><span class="o">.</span><span class="n">persist_obj</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">file_path</span><span class="p">)</span>
|
||||
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">exception</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">stdout</span><span class="p">)</span>
|
||||
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"[{0: %H:%M:%S}] Finished batch "</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="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">id</span><span class="p">))</span>
|
||||
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"[{0: %H:%M:%S}] Distrituted Train Finished"</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">stop_dispy_cluster</span><span class="p">(</span><span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">model</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="simple_model_predict"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.dispy.simple_model_predict">[docs]</a><span class="k">def</span> <span class="nf">simple_model_predict</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">parameters</span><span class="p">):</span>
|
||||
<span class="kn">import</span> <span class="nn">time</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">forecasts</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">data</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">)</span>
|
||||
<span class="n">_stop</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="k">return</span> <span class="n">forecasts</span><span class="p">,</span> <span class="n">_stop</span> <span class="o">-</span> <span class="n">_start</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="distributed_predict"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.dispy.distributed_predict">[docs]</a><span class="k">def</span> <span class="nf">distributed_predict</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="n">nodes</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">num_batches</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="kn">import</span> <span class="nn">dispy</span><span class="o">,</span> <span class="nn">dispy.httpd</span>
|
||||
|
||||
<span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span> <span class="o">=</span> <span class="n">start_dispy_cluster</span><span class="p">(</span><span class="n">simple_model_predict</span><span class="p">,</span> <span class="n">nodes</span><span class="p">)</span>
|
||||
|
||||
<span class="n">jobs</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
<span class="n">batch_size</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">n</span> <span class="o">/</span> <span class="n">num_batches</span><span class="p">)</span>
|
||||
<span class="n">bcount</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="k">for</span> <span class="n">ct</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">ct</span> <span class="o">-</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">:</span><span class="n">ct</span> <span class="o">+</span> <span class="n">batch_size</span><span class="p">]</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">ct</span> <span class="o">-</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">:</span> <span class="n">ct</span> <span class="o">+</span> <span class="n">batch_size</span><span class="p">]</span>
|
||||
|
||||
<span class="n">job</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">submit</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="n">parameters</span><span class="p">)</span>
|
||||
<span class="n">job</span><span class="o">.</span><span class="n">id</span> <span class="o">=</span> <span class="n">bcount</span> <span class="c1"># associate an ID to identify jobs (if needed later)</span>
|
||||
<span class="n">jobs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">job</span><span class="p">)</span>
|
||||
|
||||
<span class="n">bcount</span> <span class="o">+=</span> <span class="mi">1</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">job</span> <span class="ow">in</span> <span class="n">jobs</span><span class="p">:</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="n">job</span><span class="p">()</span>
|
||||
<span class="k">if</span> <span class="n">job</span><span class="o">.</span><span class="n">status</span> <span class="o">==</span> <span class="n">dispy</span><span class="o">.</span><span class="n">DispyJob</span><span class="o">.</span><span class="n">Finished</span> <span class="ow">and</span> <span class="n">tmp</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="n">job</span><span class="o">.</span><span class="n">id</span> <span class="o"><</span> <span class="n">batch_size</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="n">tmp</span><span class="p">[</span><span class="mi">0</span><span class="p">][:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="k">else</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="n">tmp</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
|
||||
|
||||
<span class="k">if</span> <span class="s1">'forecasting_time'</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">:</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">'forecasting_time'</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">'forecasting_time'</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
|
||||
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">exception</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">stdout</span><span class="p">)</span>
|
||||
|
||||
<span class="n">stop_dispy_cluster</span><span class="p">(</span><span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
</pre></div>
|
||||
|
||||
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|
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|
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© Copyright 2018, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
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<li class="nav-item nav-item-this"><a href="">pyFTS.hyperparam.Evolutionary</a></li>
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<h1>Source code for pyFTS.hyperparam.Evolutionary</h1><div class="highlight"><pre>
|
||||
<span></span><span class="sd">"""</span>
|
||||
<span class="sd">Distributed Evolutionary Hyperparameter Optimization (DEHO) for MVFTS</span>
|
||||
<span class="sd">"""</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">math</span>
|
||||
<span class="kn">import</span> <span class="nn">time</span>
|
||||
<span class="kn">from</span> <span class="nn">functools</span> <span class="k">import</span> <span class="n">reduce</span>
|
||||
<span class="kn">from</span> <span class="nn">operator</span> <span class="k">import</span> <span class="n">itemgetter</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">random</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.benchmarks</span> <span class="k">import</span> <span class="n">Measures</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="k">import</span> <span class="n">Grid</span><span class="p">,</span> <span class="n">Entropy</span> <span class="c1"># , Huarng</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">Membership</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">hofts</span><span class="p">,</span> <span class="n">ifts</span><span class="p">,</span> <span class="n">pwfts</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.hyperparam</span> <span class="k">import</span> <span class="n">Util</span> <span class="k">as</span> <span class="n">hUtil</span>
|
||||
|
||||
|
||||
<span class="n">__measures</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'f1'</span><span class="p">,</span> <span class="s1">'f2'</span><span class="p">,</span> <span class="s1">'rmse'</span><span class="p">,</span> <span class="s1">'size'</span><span class="p">]</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="genotype"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.genotype">[docs]</a><span class="k">def</span> <span class="nf">genotype</span><span class="p">(</span><span class="n">mf</span><span class="p">,</span> <span class="n">npart</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">alpha</span><span class="p">,</span> <span class="n">lags</span><span class="p">,</span> <span class="n">f1</span><span class="p">,</span> <span class="n">f2</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Create the individual genotype</span>
|
||||
|
||||
<span class="sd"> :param mf: membership function</span>
|
||||
<span class="sd"> :param npart: number of partitions</span>
|
||||
<span class="sd"> :param partitioner: partitioner method</span>
|
||||
<span class="sd"> :param order: model order</span>
|
||||
<span class="sd"> :param alpha: alpha-cut</span>
|
||||
<span class="sd"> :param lags: array with lag indexes</span>
|
||||
<span class="sd"> :param f1: accuracy fitness value</span>
|
||||
<span class="sd"> :param f2: parsimony fitness value</span>
|
||||
<span class="sd"> :return: the genotype, a dictionary with all hyperparameters</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">ind</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">mf</span><span class="o">=</span><span class="n">mf</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="n">npart</span><span class="p">,</span> <span class="n">partitioner</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="n">order</span><span class="p">,</span>
|
||||
<span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span> <span class="n">lags</span><span class="o">=</span><span class="n">lags</span><span class="p">,</span> <span class="n">f1</span><span class="o">=</span><span class="n">f1</span><span class="p">,</span> <span class="n">f2</span><span class="o">=</span><span class="n">f2</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">ind</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="random_genotype"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.random_genotype">[docs]</a><span class="k">def</span> <span class="nf">random_genotype</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Create random genotype</span>
|
||||
|
||||
<span class="sd"> :return: the genotype, a dictionary with all hyperparameters</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">order</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
|
||||
<span class="n">lags</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</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="mi">1</span><span class="p">,</span> <span class="n">order</span><span class="o">+</span><span class="mi">1</span><span class="p">)]</span>
|
||||
<span class="k">return</span> <span class="n">genotype</span><span class="p">(</span>
|
||||
<span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
|
||||
<span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span>
|
||||
<span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>
|
||||
<span class="n">order</span><span class="p">,</span>
|
||||
<span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="o">.</span><span class="mi">5</span><span class="p">),</span>
|
||||
<span class="n">lags</span><span class="p">,</span>
|
||||
<span class="kc">None</span><span class="p">,</span>
|
||||
<span class="kc">None</span>
|
||||
<span class="p">)</span></div>
|
||||
|
||||
|
||||
<span class="c1">#</span>
|
||||
<div class="viewcode-block" id="initial_population"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.initial_population">[docs]</a><span class="k">def</span> <span class="nf">initial_population</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Create a random population of size n</span>
|
||||
|
||||
<span class="sd"> :param n: the size of the population</span>
|
||||
<span class="sd"> :return: a list with n random individuals</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">create_random_individual</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">'random_individual'</span><span class="p">,</span> <span class="n">random_genotype</span><span class="p">)</span>
|
||||
|
||||
<span class="n">pop</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
|
||||
<span class="n">pop</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">create_random_individual</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">pop</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="phenotype"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.phenotype">[docs]</a><span class="k">def</span> <span class="nf">phenotype</span><span class="p">(</span><span class="n">individual</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">fts_method</span><span class="p">,</span> <span class="n">parameters</span><span class="o">=</span><span class="p">{},</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Instantiate the genotype, creating a fitted model with the genotype hyperparameters</span>
|
||||
|
||||
<span class="sd"> :param individual: a genotype</span>
|
||||
<span class="sd"> :param train: the training dataset</span>
|
||||
<span class="sd"> :param fts_method: the FTS method </span>
|
||||
<span class="sd"> :param parameters: dict with model specific arguments for fit method.</span>
|
||||
<span class="sd"> :return: a fitted FTS model</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">hofts</span><span class="p">,</span> <span class="n">ifts</span><span class="p">,</span> <span class="n">pwfts</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">individual</span><span class="p">[</span><span class="s1">'mf'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">mf</span> <span class="o">=</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span>
|
||||
<span class="k">elif</span> <span class="n">individual</span><span class="p">[</span><span class="s1">'mf'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
|
||||
<span class="n">mf</span> <span class="o">=</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trapmf</span>
|
||||
<span class="k">elif</span> <span class="n">individual</span><span class="p">[</span><span class="s1">'mf'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">3</span> <span class="ow">and</span> <span class="n">individual</span><span class="p">[</span><span class="s1">'partitioner'</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
|
||||
<span class="n">mf</span> <span class="o">=</span> <span class="n">Membership</span><span class="o">.</span><span class="n">gaussmf</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">mf</span> <span class="o">=</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">individual</span><span class="p">[</span><span class="s1">'partitioner'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">partitioner</span> <span class="o">=</span> <span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">train</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">'npart'</span><span class="p">],</span> <span class="n">func</span><span class="o">=</span><span class="n">mf</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">individual</span><span class="p">[</span><span class="s1">'partitioner'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
|
||||
<span class="n">partitioner</span> <span class="o">=</span> <span class="n">Entropy</span><span class="o">.</span><span class="n">EntropyPartitioner</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">train</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">'npart'</span><span class="p">],</span> <span class="n">func</span><span class="o">=</span><span class="n">mf</span><span class="p">)</span>
|
||||
|
||||
<span class="n">model</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="n">partitioner</span><span class="p">,</span>
|
||||
<span class="n">lags</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">'lags'</span><span class="p">],</span>
|
||||
<span class="n">alpha_cut</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">'alpha'</span><span class="p">],</span>
|
||||
<span class="n">order</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">'order'</span><span class="p">])</span>
|
||||
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">model</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="evaluate"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.evaluate">[docs]</a><span class="k">def</span> <span class="nf">evaluate</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">individual</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Evaluate an individual using a sliding window cross validation over the dataset.</span>
|
||||
|
||||
<span class="sd"> :param dataset: Evaluation dataset</span>
|
||||
<span class="sd"> :param individual: genotype to be tested</span>
|
||||
<span class="sd"> :param window_size: The length of scrolling window for train/test on dataset</span>
|
||||
<span class="sd"> :param train_rate: The train/test split ([0,1])</span>
|
||||
<span class="sd"> :param increment_rate: The increment of the scrolling window, relative to the window_size ([0,1])</span>
|
||||
<span class="sd"> :param parameters: dict with model specific arguments for fit method.</span>
|
||||
<span class="sd"> :return: a tuple (len_lags, rmse) with the parsimony fitness value and the accuracy fitness value</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">hofts</span><span class="p">,</span> <span class="n">ifts</span><span class="p">,</span> <span class="n">pwfts</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.benchmarks</span> <span class="k">import</span> <span class="n">Measures</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.hyperparam.Evolutionary</span> <span class="k">import</span> <span class="n">phenotype</span><span class="p">,</span> <span class="n">__measures</span>
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
|
||||
<span class="n">window_size</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_size'</span><span class="p">,</span> <span class="mi">800</span><span class="p">)</span>
|
||||
<span class="n">train_rate</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">'train_rate'</span><span class="p">,</span> <span class="o">.</span><span class="mi">8</span><span class="p">)</span>
|
||||
<span class="n">increment_rate</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">'increment_rate'</span><span class="p">,</span> <span class="o">.</span><span class="mi">2</span><span class="p">)</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="n">hofts</span><span class="o">.</span><span class="n">WeightedHighOrderFTS</span><span class="p">)</span>
|
||||
<span class="n">parameters</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">'parameters'</span><span class="p">,{})</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">individual</span><span class="p">[</span><span class="s1">'f1'</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">individual</span><span class="p">[</span><span class="s1">'f2'</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="p">{</span> <span class="n">key</span><span class="p">:</span> <span class="n">individual</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">__measures</span> <span class="p">}</span>
|
||||
|
||||
<span class="n">errors</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">lengths</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">count</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">dataset</span><span class="p">,</span> <span class="n">window_size</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="n">train_rate</span><span class="p">,</span> <span class="n">inc</span><span class="o">=</span><span class="n">increment_rate</span><span class="p">):</span>
|
||||
|
||||
<span class="k">try</span><span class="p">:</span>
|
||||
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">phenotype</span><span class="p">(</span><span class="n">individual</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">fts_method</span><span class="o">=</span><span class="n">fts_method</span><span class="p">,</span> <span class="n">parameters</span><span class="o">=</span><span class="n">parameters</span><span class="p">)</span>
|
||||
|
||||
<span class="n">forecasts</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="n">rmse</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">rmse</span><span class="p">(</span><span class="n">test</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="n">lengths</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="p">))</span>
|
||||
|
||||
<span class="n">errors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">rmse</span><span class="p">)</span>
|
||||
|
||||
<span class="k">except</span><span class="p">:</span>
|
||||
<span class="n">lengths</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">nan</span><span class="p">)</span>
|
||||
<span class="n">errors</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">nan</span><span class="p">)</span>
|
||||
|
||||
<span class="k">try</span><span class="p">:</span>
|
||||
<span class="n">_lags</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">lags</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span>
|
||||
|
||||
<span class="n">_rmse</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">errors</span><span class="p">)</span>
|
||||
<span class="n">_len</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">lengths</span><span class="p">)</span>
|
||||
|
||||
<span class="n">f1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="o">.</span><span class="mi">6</span> <span class="o">*</span> <span class="n">_rmse</span><span class="p">,</span> <span class="o">.</span><span class="mi">4</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">errors</span><span class="p">)])</span>
|
||||
<span class="n">f2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="o">.</span><span class="mi">4</span> <span class="o">*</span> <span class="n">_len</span><span class="p">,</span> <span class="o">.</span><span class="mi">6</span> <span class="o">*</span> <span class="n">_lags</span><span class="p">])</span>
|
||||
|
||||
<span class="k">return</span> <span class="p">{</span><span class="s1">'f1'</span><span class="p">:</span> <span class="n">f1</span><span class="p">,</span> <span class="s1">'f2'</span><span class="p">:</span> <span class="n">f2</span><span class="p">,</span> <span class="s1">'rmse'</span><span class="p">:</span> <span class="n">_rmse</span><span class="p">,</span> <span class="s1">'size'</span><span class="p">:</span> <span class="n">_len</span> <span class="p">}</span>
|
||||
<span class="k">except</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="p">{</span><span class="s1">'f1'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> <span class="s1">'f2'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> <span class="s1">'rmse'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> <span class="s1">'size'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">}</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="tournament"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.tournament">[docs]</a><span class="k">def</span> <span class="nf">tournament</span><span class="p">(</span><span class="n">population</span><span class="p">,</span> <span class="n">objective</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Simple tournament selection strategy.</span>
|
||||
|
||||
<span class="sd"> :param population: the population</span>
|
||||
<span class="sd"> :param objective: the objective to be considered on tournament</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">population</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
|
||||
|
||||
<span class="n">r1</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span> <span class="k">if</span> <span class="n">n</span> <span class="o">></span> <span class="mi">2</span> <span class="k">else</span> <span class="mi">0</span>
|
||||
<span class="n">r2</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span> <span class="k">if</span> <span class="n">n</span> <span class="o">></span> <span class="mi">2</span> <span class="k">else</span> <span class="mi">1</span>
|
||||
<span class="n">ix</span> <span class="o">=</span> <span class="n">r1</span> <span class="k">if</span> <span class="n">population</span><span class="p">[</span><span class="n">r1</span><span class="p">][</span><span class="n">objective</span><span class="p">]</span> <span class="o"><</span> <span class="n">population</span><span class="p">[</span><span class="n">r2</span><span class="p">][</span><span class="n">objective</span><span class="p">]</span> <span class="k">else</span> <span class="n">r2</span>
|
||||
<span class="k">return</span> <span class="n">population</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="double_tournament"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.double_tournament">[docs]</a><span class="k">def</span> <span class="nf">double_tournament</span><span class="p">(</span><span class="n">population</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Double tournament selection strategy.</span>
|
||||
|
||||
<span class="sd"> :param population:</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">ancestor1</span> <span class="o">=</span> <span class="n">tournament</span><span class="p">(</span><span class="n">population</span><span class="p">,</span> <span class="s1">'f1'</span><span class="p">)</span>
|
||||
<span class="n">ancestor2</span> <span class="o">=</span> <span class="n">tournament</span><span class="p">(</span><span class="n">population</span><span class="p">,</span> <span class="s1">'f1'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">selected</span> <span class="o">=</span> <span class="n">tournament</span><span class="p">([</span><span class="n">ancestor1</span><span class="p">,</span> <span class="n">ancestor2</span><span class="p">],</span> <span class="s1">'f2'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">selected</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="lag_crossover2"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.lag_crossover2">[docs]</a><span class="k">def</span> <span class="nf">lag_crossover2</span><span class="p">(</span><span class="n">best</span><span class="p">,</span> <span class="n">worst</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Cross over two lag genes</span>
|
||||
|
||||
<span class="sd"> :param best: best genotype</span>
|
||||
<span class="sd"> :param worst: worst genotype</span>
|
||||
<span class="sd"> :return: a tuple (order, lags)</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">order</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="o">.</span><span class="mi">7</span> <span class="o">*</span> <span class="n">best</span><span class="p">[</span><span class="s1">'order'</span><span class="p">]</span> <span class="o">+</span> <span class="o">.</span><span class="mi">3</span> <span class="o">*</span> <span class="n">worst</span><span class="p">[</span><span class="s1">'order'</span><span class="p">]))</span>
|
||||
<span class="n">lags</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">min_order</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">best</span><span class="p">[</span><span class="s1">'order'</span><span class="p">],</span> <span class="n">worst</span><span class="p">[</span><span class="s1">'order'</span><span class="p">])</span>
|
||||
|
||||
<span class="n">max_order</span> <span class="o">=</span> <span class="n">best</span> <span class="k">if</span> <span class="n">best</span><span class="p">[</span><span class="s1">'order'</span><span class="p">]</span> <span class="o">></span> <span class="n">min_order</span> <span class="k">else</span> <span class="n">worst</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="mi">0</span><span class="p">,</span> <span class="n">order</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">k</span> <span class="o"><</span> <span class="n">min_order</span><span class="p">:</span>
|
||||
<span class="n">lags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="o">.</span><span class="mi">7</span> <span class="o">*</span> <span class="n">best</span><span class="p">[</span><span class="s1">'lags'</span><span class="p">][</span><span class="n">k</span><span class="p">]</span> <span class="o">+</span> <span class="o">.</span><span class="mi">3</span> <span class="o">*</span> <span class="n">worst</span><span class="p">[</span><span class="s1">'lags'</span><span class="p">][</span><span class="n">k</span><span class="p">])))</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">lags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">max_order</span><span class="p">[</span><span class="s1">'lags'</span><span class="p">][</span><span class="n">k</span><span class="p">])</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">order</span><span class="p">):</span>
|
||||
<span class="k">while</span> <span class="n">lags</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="o">>=</span> <span class="n">lags</span><span class="p">[</span><span class="n">k</span><span class="p">]:</span>
|
||||
<span class="n">lags</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">+=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">order</span><span class="p">,</span> <span class="n">lags</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="crossover"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.crossover">[docs]</a><span class="k">def</span> <span class="nf">crossover</span><span class="p">(</span><span class="n">population</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Crossover operation between two parents</span>
|
||||
|
||||
<span class="sd"> :param population: the original population</span>
|
||||
<span class="sd"> :return: a genotype</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="kn">import</span> <span class="nn">random</span>
|
||||
|
||||
<span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">population</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
|
||||
|
||||
<span class="n">r1</span><span class="p">,</span> <span class="n">r2</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span>
|
||||
<span class="k">while</span> <span class="n">r1</span> <span class="o">==</span> <span class="n">r2</span><span class="p">:</span>
|
||||
<span class="n">r1</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
|
||||
<span class="n">r2</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">population</span><span class="p">[</span><span class="n">r1</span><span class="p">][</span><span class="s1">'f1'</span><span class="p">]</span> <span class="o"><</span> <span class="n">population</span><span class="p">[</span><span class="n">r2</span><span class="p">][</span><span class="s1">'f1'</span><span class="p">]:</span>
|
||||
<span class="n">best</span> <span class="o">=</span> <span class="n">population</span><span class="p">[</span><span class="n">r1</span><span class="p">]</span>
|
||||
<span class="n">worst</span> <span class="o">=</span> <span class="n">population</span><span class="p">[</span><span class="n">r2</span><span class="p">]</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">best</span> <span class="o">=</span> <span class="n">population</span><span class="p">[</span><span class="n">r2</span><span class="p">]</span>
|
||||
<span class="n">worst</span> <span class="o">=</span> <span class="n">population</span><span class="p">[</span><span class="n">r1</span><span class="p">]</span>
|
||||
|
||||
<span class="n">npart</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="o">.</span><span class="mi">7</span> <span class="o">*</span> <span class="n">best</span><span class="p">[</span><span class="s1">'npart'</span><span class="p">]</span> <span class="o">+</span> <span class="o">.</span><span class="mi">3</span> <span class="o">*</span> <span class="n">worst</span><span class="p">[</span><span class="s1">'npart'</span><span class="p">]))</span>
|
||||
<span class="n">alpha</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="o">.</span><span class="mi">7</span> <span class="o">*</span> <span class="n">best</span><span class="p">[</span><span class="s1">'alpha'</span><span class="p">]</span> <span class="o">+</span> <span class="o">.</span><span class="mi">3</span> <span class="o">*</span> <span class="n">worst</span><span class="p">[</span><span class="s1">'alpha'</span><span class="p">])</span>
|
||||
|
||||
<span class="n">rnd</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">mf</span> <span class="o">=</span> <span class="n">best</span><span class="p">[</span><span class="s1">'mf'</span><span class="p">]</span> <span class="k">if</span> <span class="n">rnd</span> <span class="o"><</span> <span class="o">.</span><span class="mi">7</span> <span class="k">else</span> <span class="n">worst</span><span class="p">[</span><span class="s1">'mf'</span><span class="p">]</span>
|
||||
|
||||
<span class="n">rnd</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">partitioner</span> <span class="o">=</span> <span class="n">best</span><span class="p">[</span><span class="s1">'partitioner'</span><span class="p">]</span> <span class="k">if</span> <span class="n">rnd</span> <span class="o"><</span> <span class="o">.</span><span class="mi">7</span> <span class="k">else</span> <span class="n">worst</span><span class="p">[</span><span class="s1">'partitioner'</span><span class="p">]</span>
|
||||
|
||||
<span class="n">order</span><span class="p">,</span> <span class="n">lags</span> <span class="o">=</span> <span class="n">lag_crossover2</span><span class="p">(</span><span class="n">best</span><span class="p">,</span> <span class="n">worst</span><span class="p">)</span>
|
||||
|
||||
<span class="n">descendent</span> <span class="o">=</span> <span class="n">genotype</span><span class="p">(</span><span class="n">mf</span><span class="p">,</span> <span class="n">npart</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">alpha</span><span class="p">,</span> <span class="n">lags</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">descendent</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="mutation_lags"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.mutation_lags">[docs]</a><span class="k">def</span> <span class="nf">mutation_lags</span><span class="p">(</span><span class="n">lags</span><span class="p">,</span> <span class="n">order</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Mutation operation for lags gene</span>
|
||||
|
||||
<span class="sd"> :param lags:</span>
|
||||
<span class="sd"> :param order:</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">try</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">lags</span><span class="p">)</span>
|
||||
<span class="n">new</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">lag</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">order</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">lag</span> <span class="o"><</span> <span class="n">l</span><span class="p">:</span>
|
||||
<span class="n">new</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> <span class="nb">min</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">lags</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">))))</span> <span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">new</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> <span class="n">new</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span> <span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">order</span> <span class="o">></span> <span class="mi">1</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="mi">1</span><span class="p">,</span> <span class="n">order</span><span class="p">):</span>
|
||||
<span class="k">while</span> <span class="n">new</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o"><=</span> <span class="n">new</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]:</span>
|
||||
<span class="n">new</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">new</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">new</span>
|
||||
<span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">ex</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">lags</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">new</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="mutation"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.mutation">[docs]</a><span class="k">def</span> <span class="nf">mutation</span><span class="p">(</span><span class="n">individual</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Mutation operator</span>
|
||||
|
||||
<span class="sd"> :param individual: an individual genotype</span>
|
||||
<span class="sd"> :param pmut: individual probability o</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">individual</span><span class="p">[</span><span class="s1">'npart'</span><span class="p">]</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">individual</span><span class="p">[</span><span class="s1">'npart'</span><span class="p">]</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">))))</span>
|
||||
<span class="n">individual</span><span class="p">[</span><span class="s1">'alpha'</span><span class="p">]</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="o">.</span><span class="mi">5</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">individual</span><span class="p">[</span><span class="s1">'alpha'</span><span class="p">]</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="o">.</span><span class="mi">5</span><span class="p">)))</span>
|
||||
<span class="n">individual</span><span class="p">[</span><span class="s1">'mf'</span><span class="p">]</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
|
||||
<span class="n">individual</span><span class="p">[</span><span class="s1">'partitioner'</span><span class="p">]</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
|
||||
<span class="n">individual</span><span class="p">[</span><span class="s1">'order'</span><span class="p">]</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">individual</span><span class="p">[</span><span class="s1">'order'</span><span class="p">]</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))))</span>
|
||||
<span class="c1"># Chama a função mutation_lags</span>
|
||||
<span class="n">individual</span><span class="p">[</span><span class="s1">'lags'</span><span class="p">]</span> <span class="o">=</span> <span class="n">mutation_lags</span><span class="p">(</span> <span class="n">individual</span><span class="p">[</span><span class="s1">'lags'</span><span class="p">],</span> <span class="n">individual</span><span class="p">[</span><span class="s1">'order'</span><span class="p">])</span>
|
||||
|
||||
<span class="n">individual</span><span class="p">[</span><span class="s1">'f1'</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="n">individual</span><span class="p">[</span><span class="s1">'f2'</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">individual</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="elitism"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.elitism">[docs]</a><span class="k">def</span> <span class="nf">elitism</span><span class="p">(</span><span class="n">population</span><span class="p">,</span> <span class="n">new_population</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Elitism operation, always select the best individual of the population and discard the worst</span>
|
||||
|
||||
<span class="sd"> :param population:</span>
|
||||
<span class="sd"> :param new_population:</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">population</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">population</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="n">itemgetter</span><span class="p">(</span><span class="s1">'f1'</span><span class="p">))</span>
|
||||
<span class="n">best</span> <span class="o">=</span> <span class="n">population</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
|
||||
|
||||
<span class="n">new_population</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">new_population</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="n">itemgetter</span><span class="p">(</span><span class="s1">'f1'</span><span class="p">))</span>
|
||||
<span class="k">if</span> <span class="n">new_population</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s2">"f1"</span><span class="p">]</span> <span class="o">></span> <span class="n">best</span><span class="p">[</span><span class="s2">"f1"</span><span class="p">]:</span>
|
||||
<span class="n">new_population</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">best</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">new_population</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s2">"f1"</span><span class="p">]</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">"f1"</span><span class="p">]</span> <span class="ow">and</span> <span class="n">new_population</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s2">"f2"</span><span class="p">]</span> <span class="o">></span> <span class="n">best</span><span class="p">[</span><span class="s2">"f2"</span><span class="p">]:</span>
|
||||
<span class="n">new_population</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">best</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">new_population</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="GeneticAlgorithm"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.GeneticAlgorithm">[docs]</a><span class="k">def</span> <span class="nf">GeneticAlgorithm</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Genetic algoritm for Distributed Evolutionary Hyperparameter Optimization (DEHO)</span>
|
||||
|
||||
<span class="sd"> :param dataset: The time series to optimize the FTS</span>
|
||||
<span class="sd"> :keyword ngen: An integer value with the maximum number of generations, default value: 30</span>
|
||||
<span class="sd"> :keyword mgen: An integer value with the maximum number of generations without improvement to stop, default value 7</span>
|
||||
<span class="sd"> :keyword npop: An integer value with the population size, default value: 20</span>
|
||||
<span class="sd"> :keyword pcross: A float value between 0 and 1 with the probability of crossover, default: .5</span>
|
||||
<span class="sd"> :keyword psel: A float value between 0 and 1 with the probability of selection, default: .5</span>
|
||||
<span class="sd"> :keyword pmut: A float value between 0 and 1 with the probability of mutation, default: .3</span>
|
||||
<span class="sd"> :keyword fts_method: The FTS method to optimize</span>
|
||||
<span class="sd"> :keyword parameters: dict with model specific arguments for fts_method</span>
|
||||
<span class="sd"> :keyword elitism: A boolean value indicating if the best individual must always survive to next population</span>
|
||||
<span class="sd"> :keyword initial_operator: a function that receives npop and return a random population with size npop</span>
|
||||
<span class="sd"> :keyword evalutation_operator: a function that receives a dataset and an individual and return its fitness</span>
|
||||
<span class="sd"> :keyword selection_operator: a function that receives the whole population and return a selected individual</span>
|
||||
<span class="sd"> :keyword crossover_operator: a function that receives the whole population and return a descendent individual</span>
|
||||
<span class="sd"> :keyword mutation_operator: a function that receives one individual and return a changed individual</span>
|
||||
<span class="sd"> :keyword window_size: An integer value with the the length of scrolling window for train/test on dataset</span>
|
||||
<span class="sd"> :keyword train_rate: A float value between 0 and 1 with the train/test split ([0,1])</span>
|
||||
<span class="sd"> :keyword increment_rate: A float value between 0 and 1 with the the increment of the scrolling window,</span>
|
||||
<span class="sd"> relative to the window_size ([0,1])</span>
|
||||
<span class="sd"> :keyword collect_statistics: A boolean value indicating to collect statistics for each generation</span>
|
||||
<span class="sd"> :keyword distributed: A value indicating it the execution will be local and sequential (distributed=False),</span>
|
||||
<span class="sd"> or parallel and distributed (distributed='dispy' or distributed='spark')</span>
|
||||
<span class="sd"> :keyword cluster: If distributed='dispy' the list of cluster nodes, else if distributed='spark' it is the master node</span>
|
||||
<span class="sd"> :return: the best genotype</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">statistics</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">ngen</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">'ngen'</span><span class="p">,</span><span class="mi">30</span><span class="p">)</span>
|
||||
<span class="n">mgen</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">'mgen'</span><span class="p">,</span> <span class="mi">7</span><span class="p">)</span>
|
||||
<span class="n">npop</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">'npop'</span><span class="p">,</span><span class="mi">20</span><span class="p">)</span>
|
||||
<span class="n">psel</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">'psel'</span><span class="p">,</span> <span class="o">.</span><span class="mi">5</span><span class="p">)</span>
|
||||
<span class="n">pcross</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">'pcross'</span><span class="p">,</span><span class="o">.</span><span class="mi">5</span><span class="p">)</span>
|
||||
<span class="n">pmut</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">'pmut'</span><span class="p">,</span><span class="o">.</span><span class="mi">3</span><span class="p">)</span>
|
||||
<span class="n">distributed</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">'distributed'</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
|
||||
|
||||
<span class="n">initial_operator</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">'initial_operator'</span><span class="p">,</span> <span class="n">initial_population</span><span class="p">)</span>
|
||||
<span class="n">evaluation_operator</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">'evaluation_operator'</span><span class="p">,</span> <span class="n">evaluate</span><span class="p">)</span>
|
||||
<span class="n">selection_operator</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">'selection_operator'</span><span class="p">,</span> <span class="n">double_tournament</span><span class="p">)</span>
|
||||
<span class="n">crossover_operator</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">'crossover_operator'</span><span class="p">,</span> <span class="n">crossover</span><span class="p">)</span>
|
||||
<span class="n">mutation_operator</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">'mutation_operator'</span><span class="p">,</span> <span class="n">mutation</span><span class="p">)</span>
|
||||
|
||||
<span class="n">_elitism</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">'elitism'</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="n">elitism_operator</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">'elitism_operator'</span><span class="p">,</span> <span class="n">elitism</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">distributed</span> <span class="o">==</span> <span class="s1">'dispy'</span><span class="p">:</span>
|
||||
<span class="n">cluster</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'cluster'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
|
||||
<span class="n">collect_statistics</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">'collect_statistics'</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="n">no_improvement_count</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
|
||||
<span class="n">new_population</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">population</span> <span class="o">=</span> <span class="n">initial_operator</span><span class="p">(</span><span class="n">npop</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<span class="n">last_best</span> <span class="o">=</span> <span class="n">population</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
|
||||
<span class="n">best</span> <span class="o">=</span> <span class="n">population</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
|
||||
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Evaluating initial population </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()))</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">distributed</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">individual</span> <span class="ow">in</span> <span class="n">population</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">evaluation_operator</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">individual</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">__measures</span><span class="p">:</span>
|
||||
<span class="n">individual</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">ret</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
|
||||
<span class="k">elif</span> <span class="n">distributed</span><span class="o">==</span><span class="s1">'dispy'</span><span class="p">:</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.distributed</span> <span class="k">import</span> <span class="n">dispy</span> <span class="k">as</span> <span class="n">dUtil</span>
|
||||
<span class="kn">import</span> <span class="nn">dispy</span>
|
||||
<span class="n">jobs</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">individual</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">population</span><span class="p">):</span>
|
||||
<span class="n">job</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">submit</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">individual</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="n">job</span><span class="o">.</span><span class="n">id</span> <span class="o">=</span> <span class="n">ct</span>
|
||||
<span class="n">jobs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">job</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">job</span> <span class="ow">in</span> <span class="n">jobs</span><span class="p">:</span>
|
||||
<span class="n">result</span> <span class="o">=</span> <span class="n">job</span><span class="p">()</span>
|
||||
<span class="k">if</span> <span class="n">job</span><span class="o">.</span><span class="n">status</span> <span class="o">==</span> <span class="n">dispy</span><span class="o">.</span><span class="n">DispyJob</span><span class="o">.</span><span class="n">Finished</span> <span class="ow">and</span> <span class="n">result</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">__measures</span><span class="p">:</span>
|
||||
<span class="n">population</span><span class="p">[</span><span class="n">job</span><span class="o">.</span><span class="n">id</span><span class="p">][</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">exception</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">stdout</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ngen</span><span class="p">):</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"GENERATION </span><span class="si">{}</span><span class="s2"> </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()))</span>
|
||||
|
||||
<span class="n">generation_statistics</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
|
||||
<span class="c1"># Selection</span>
|
||||
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">npop</span> <span class="o">*</span> <span class="n">psel</span><span class="p">)):</span>
|
||||
<span class="n">new_population</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">selection_operator</span><span class="p">(</span><span class="n">population</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">))</span>
|
||||
|
||||
<span class="c1"># Crossover</span>
|
||||
<span class="n">new</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">npop</span> <span class="o">*</span> <span class="n">pcross</span><span class="p">)):</span>
|
||||
<span class="n">new</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">crossover_operator</span><span class="p">(</span><span class="n">new_population</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">))</span>
|
||||
|
||||
<span class="n">new_population</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">new</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># Mutation</span>
|
||||
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">individual</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">new_population</span><span class="p">):</span>
|
||||
<span class="n">rnd</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">rnd</span> <span class="o"><</span> <span class="n">pmut</span><span class="p">:</span>
|
||||
<span class="n">new_population</span><span class="p">[</span><span class="n">ct</span><span class="p">]</span> <span class="o">=</span> <span class="n">mutation_operator</span><span class="p">(</span><span class="n">individual</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># Evaluation</span>
|
||||
<span class="k">if</span> <span class="n">collect_statistics</span><span class="p">:</span>
|
||||
<span class="n">stats</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">__measures</span><span class="p">:</span>
|
||||
<span class="n">stats</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">distributed</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">individual</span> <span class="ow">in</span> <span class="n">new_population</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">evaluation_operator</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">individual</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">__measures</span><span class="p">:</span>
|
||||
<span class="n">individual</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">ret</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
|
||||
<span class="k">if</span> <span class="n">collect_statistics</span><span class="p">:</span> <span class="n">stats</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>
|
||||
|
||||
<span class="k">elif</span> <span class="n">distributed</span> <span class="o">==</span> <span class="s1">'dispy'</span><span class="p">:</span>
|
||||
<span class="n">jobs</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">individual</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">new_population</span><span class="p">):</span>
|
||||
<span class="n">job</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">submit</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">individual</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="n">job</span><span class="o">.</span><span class="n">id</span> <span class="o">=</span> <span class="n">ct</span>
|
||||
<span class="n">jobs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">job</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">job</span> <span class="ow">in</span> <span class="n">jobs</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s1">'job id </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">id</span><span class="p">))</span>
|
||||
<span class="n">result</span> <span class="o">=</span> <span class="n">job</span><span class="p">()</span>
|
||||
<span class="k">if</span> <span class="n">job</span><span class="o">.</span><span class="n">status</span> <span class="o">==</span> <span class="n">dispy</span><span class="o">.</span><span class="n">DispyJob</span><span class="o">.</span><span class="n">Finished</span> <span class="ow">and</span> <span class="n">result</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">__measures</span><span class="p">:</span>
|
||||
<span class="n">new_population</span><span class="p">[</span><span class="n">job</span><span class="o">.</span><span class="n">id</span><span class="p">][</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
|
||||
<span class="k">if</span> <span class="n">collect_statistics</span><span class="p">:</span> <span class="n">stats</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">result</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">exception</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">stdout</span><span class="p">)</span>
|
||||
|
||||
|
||||
<span class="k">if</span> <span class="n">collect_statistics</span><span class="p">:</span>
|
||||
<span class="n">mean_stats</span> <span class="o">=</span> <span class="p">{</span><span class="n">key</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmedian</span><span class="p">(</span><span class="n">stats</span><span class="p">[</span><span class="n">key</span><span class="p">])</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">__measures</span> <span class="p">}</span>
|
||||
|
||||
<span class="n">generation_statistics</span><span class="p">[</span><span class="s1">'population'</span><span class="p">]</span> <span class="o">=</span> <span class="n">mean_stats</span>
|
||||
|
||||
<span class="c1"># Elitism</span>
|
||||
<span class="k">if</span> <span class="n">_elitism</span><span class="p">:</span>
|
||||
<span class="n">population</span> <span class="o">=</span> <span class="n">elitism_operator</span><span class="p">(</span><span class="n">population</span><span class="p">,</span> <span class="n">new_population</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<span class="n">population</span> <span class="o">=</span> <span class="n">population</span><span class="p">[:</span><span class="n">npop</span><span class="p">]</span>
|
||||
|
||||
<span class="n">new_population</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">last_best</span> <span class="o">=</span> <span class="n">best</span>
|
||||
|
||||
<span class="n">best</span> <span class="o">=</span> <span class="n">population</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">collect_statistics</span><span class="p">:</span>
|
||||
<span class="n">generation_statistics</span><span class="p">[</span><span class="s1">'best'</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="n">key</span><span class="p">:</span> <span class="n">best</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">__measures</span> <span class="p">}</span>
|
||||
|
||||
<span class="n">statistics</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">generation_statistics</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">last_best</span><span class="p">[</span><span class="s1">'f1'</span><span class="p">]</span> <span class="o"><=</span> <span class="n">best</span><span class="p">[</span><span class="s1">'f1'</span><span class="p">]</span> <span class="ow">and</span> <span class="n">last_best</span><span class="p">[</span><span class="s1">'f2'</span><span class="p">]</span> <span class="o"><=</span> <span class="n">best</span><span class="p">[</span><span class="s1">'f2'</span><span class="p">]:</span>
|
||||
<span class="n">no_improvement_count</span> <span class="o">+=</span> <span class="mi">1</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"WITHOUT IMPROVEMENT </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">no_improvement_count</span><span class="p">))</span>
|
||||
<span class="n">pmut</span> <span class="o">+=</span> <span class="o">.</span><span class="mi">05</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">no_improvement_count</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="n">pcross</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">'pcross'</span><span class="p">,</span> <span class="o">.</span><span class="mi">5</span><span class="p">)</span>
|
||||
<span class="n">pmut</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">'pmut'</span><span class="p">,</span> <span class="o">.</span><span class="mi">3</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">best</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">no_improvement_count</span> <span class="o">==</span> <span class="n">mgen</span><span class="p">:</span>
|
||||
<span class="k">break</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">best</span><span class="p">,</span> <span class="n">statistics</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="process_experiment"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.process_experiment">[docs]</a><span class="k">def</span> <span class="nf">process_experiment</span><span class="p">(</span><span class="n">fts_method</span><span class="p">,</span> <span class="n">result</span><span class="p">,</span> <span class="n">datasetname</span><span class="p">,</span> <span class="n">conn</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Persist the results of an DEHO execution in sqlite database (best hyperparameters) and json file (generation statistics)</span>
|
||||
|
||||
<span class="sd"> :param fts_method:</span>
|
||||
<span class="sd"> :param result:</span>
|
||||
<span class="sd"> :param datasetname:</span>
|
||||
<span class="sd"> :param conn:</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">log_result</span><span class="p">(</span><span class="n">conn</span><span class="p">,</span> <span class="n">datasetname</span><span class="p">,</span> <span class="n">fts_method</span><span class="p">,</span> <span class="n">result</span><span class="p">[</span><span class="s1">'individual'</span><span class="p">])</span>
|
||||
<span class="n">persist_statistics</span><span class="p">(</span><span class="n">datasetname</span><span class="p">,</span> <span class="n">result</span><span class="p">[</span><span class="s1">'statistics'</span><span class="p">])</span>
|
||||
<span class="k">return</span> <span class="n">result</span><span class="p">[</span><span class="s1">'individual'</span><span class="p">]</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="persist_statistics"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.persist_statistics">[docs]</a><span class="k">def</span> <span class="nf">persist_statistics</span><span class="p">(</span><span class="n">datasetname</span><span class="p">,</span> <span class="n">statistics</span><span class="p">):</span>
|
||||
<span class="kn">import</span> <span class="nn">json</span>
|
||||
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">'statistics_</span><span class="si">{}</span><span class="s1">.json'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">datasetname</span><span class="p">),</span> <span class="s1">'w'</span><span class="p">)</span> <span class="k">as</span> <span class="n">file</span><span class="p">:</span>
|
||||
<span class="n">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">json</span><span class="o">.</span><span class="n">dumps</span><span class="p">(</span><span class="n">statistics</span><span class="p">))</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="log_result"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.log_result">[docs]</a><span class="k">def</span> <span class="nf">log_result</span><span class="p">(</span><span class="n">conn</span><span class="p">,</span> <span class="n">datasetname</span><span class="p">,</span> <span class="n">fts_method</span><span class="p">,</span> <span class="n">result</span><span class="p">):</span>
|
||||
<span class="n">metrics</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'rmse'</span><span class="p">,</span> <span class="s1">'size'</span><span class="p">,</span> <span class="s1">'time'</span><span class="p">]</span>
|
||||
<span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="n">metrics</span><span class="p">:</span>
|
||||
<span class="n">record</span> <span class="o">=</span> <span class="p">(</span><span class="n">datasetname</span><span class="p">,</span> <span class="s1">'Evolutive'</span><span class="p">,</span> <span class="n">fts_method</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">result</span><span class="p">[</span><span class="s1">'mf'</span><span class="p">],</span>
|
||||
<span class="n">result</span><span class="p">[</span><span class="s1">'order'</span><span class="p">],</span> <span class="n">result</span><span class="p">[</span><span class="s1">'partitioner'</span><span class="p">],</span> <span class="n">result</span><span class="p">[</span><span class="s1">'npart'</span><span class="p">],</span>
|
||||
<span class="n">result</span><span class="p">[</span><span class="s1">'alpha'</span><span class="p">],</span> <span class="nb">str</span><span class="p">(</span><span class="n">result</span><span class="p">[</span><span class="s1">'lags'</span><span class="p">]),</span> <span class="n">metric</span><span class="p">,</span> <span class="n">result</span><span class="p">[</span><span class="n">metric</span><span class="p">])</span>
|
||||
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">record</span><span class="p">)</span>
|
||||
|
||||
<span class="n">hUtil</span><span class="o">.</span><span class="n">insert_hyperparam</span><span class="p">(</span><span class="n">record</span><span class="p">,</span> <span class="n">conn</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="execute"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.execute">[docs]</a><span class="k">def</span> <span class="nf">execute</span><span class="p">(</span><span class="n">datasetname</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Batch execution of Distributed Evolutionary Hyperparameter Optimization (DEHO) for monovariate methods</span>
|
||||
|
||||
<span class="sd"> :param datasetname:</span>
|
||||
<span class="sd"> :param dataset: The time series to optimize the FTS</span>
|
||||
<span class="sd"> :keyword file:</span>
|
||||
<span class="sd"> :keyword experiments:</span>
|
||||
<span class="sd"> :keyword distributed:</span>
|
||||
<span class="sd"> :keyword ngen: An integer value with the maximum number of generations, default value: 30</span>
|
||||
<span class="sd"> :keyword mgen: An integer value with the maximum number of generations without improvement to stop, default value 7</span>
|
||||
<span class="sd"> :keyword npop: An integer value with the population size, default value: 20</span>
|
||||
<span class="sd"> :keyword pcross: A float value between 0 and 1 with the probability of crossover, default: .5</span>
|
||||
<span class="sd"> :keyword psel: A float value between 0 and 1 with the probability of selection, default: .5</span>
|
||||
<span class="sd"> :keyword pmut: A float value between 0 and 1 with the probability of mutation, default: .3</span>
|
||||
<span class="sd"> :keyword fts_method: The FTS method to optimize</span>
|
||||
<span class="sd"> :keyword parameters: dict with model specific arguments for fts_method</span>
|
||||
<span class="sd"> :keyword elitism: A boolean value indicating if the best individual must always survive to next population</span>
|
||||
<span class="sd"> :keyword initial_operator: a function that receives npop and return a random population with size npop</span>
|
||||
<span class="sd"> :keyword random_individual: create an random genotype</span>
|
||||
<span class="sd"> :keyword evalutation_operator: a function that receives a dataset and an individual and return its fitness</span>
|
||||
<span class="sd"> :keyword selection_operator: a function that receives the whole population and return a selected individual</span>
|
||||
<span class="sd"> :keyword crossover_operator: a function that receives the whole population and return a descendent individual</span>
|
||||
<span class="sd"> :keyword mutation_operator: a function that receives one individual and return a changed individual</span>
|
||||
<span class="sd"> :keyword window_size: An integer value with the the length of scrolling window for train/test on dataset</span>
|
||||
<span class="sd"> :keyword train_rate: A float value between 0 and 1 with the train/test split ([0,1])</span>
|
||||
<span class="sd"> :keyword increment_rate: A float value between 0 and 1 with the the increment of the scrolling window,</span>
|
||||
<span class="sd"> relative to the window_size ([0,1])</span>
|
||||
<span class="sd"> :keyword collect_statistics: A boolean value indicating to collect statistics for each generation</span>
|
||||
<span class="sd"> :keyword distributed: A value indicating it the execution will be local and sequential (distributed=False),</span>
|
||||
<span class="sd"> or parallel and distributed (distributed='dispy' or distributed='spark')</span>
|
||||
<span class="sd"> :keyword cluster: If distributed='dispy' the list of cluster nodes, else if distributed='spark' it is the master node</span>
|
||||
<span class="sd"> :return: the best genotype</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">file</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">'file'</span><span class="p">,</span> <span class="s1">'hyperparam.db'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">conn</span> <span class="o">=</span> <span class="n">hUtil</span><span class="o">.</span><span class="n">open_hyperparam_db</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
|
||||
|
||||
<span class="n">experiments</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">'experiments'</span><span class="p">,</span> <span class="mi">30</span><span class="p">)</span>
|
||||
|
||||
<span class="n">distributed</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">'distributed'</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</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="n">hofts</span><span class="o">.</span><span class="n">WeightedHighOrderFTS</span><span class="p">)</span>
|
||||
<span class="n">shortname</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">fts_method</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">'.'</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">distributed</span> <span class="o">==</span> <span class="s1">'dispy'</span><span class="p">:</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.distributed</span> <span class="k">import</span> <span class="n">dispy</span> <span class="k">as</span> <span class="n">dUtil</span>
|
||||
<span class="n">nodes</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">'nodes'</span><span class="p">,</span> <span class="p">[</span><span class="s1">'127.0.0.1'</span><span class="p">])</span>
|
||||
<span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span> <span class="o">=</span> <span class="n">dUtil</span><span class="o">.</span><span class="n">start_dispy_cluster</span><span class="p">(</span><span class="n">evaluate</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="n">nodes</span><span class="p">)</span>
|
||||
<span class="n">kwargs</span><span class="p">[</span><span class="s1">'cluster'</span><span class="p">]</span> <span class="o">=</span> <span class="n">cluster</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">i</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">experiments</span><span class="p">):</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Experiment </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</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">ret</span><span class="p">,</span> <span class="n">statistics</span> <span class="o">=</span> <span class="n">GeneticAlgorithm</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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">ret</span><span class="p">[</span><span class="s1">'time'</span><span class="p">]</span> <span class="o">=</span> <span class="n">end</span> <span class="o">-</span> <span class="n">start</span>
|
||||
<span class="n">experiment</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'individual'</span><span class="p">:</span> <span class="n">ret</span><span class="p">,</span> <span class="s1">'statistics'</span><span class="p">:</span> <span class="n">statistics</span><span class="p">}</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">process_experiment</span><span class="p">(</span><span class="n">shortname</span><span class="p">,</span> <span class="n">experiment</span><span class="p">,</span> <span class="n">datasetname</span><span class="p">,</span> <span class="n">conn</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">distributed</span> <span class="o">==</span> <span class="s1">'dispy'</span><span class="p">:</span>
|
||||
<span class="n">dUtil</span><span class="o">.</span><span class="n">stop_dispy_cluster</span><span class="p">(</span><span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
</pre></div>
|
||||
|
||||
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|
||||
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|
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|
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<div class="sphinxsidebarwrapper">
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<h3 id="searchlabel">Quick search</h3>
|
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<form class="search" action="../../../search.html" method="get">
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<input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
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<input type="submit" value="Go" />
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<a href="../../../genindex.html" title="General Index"
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© Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
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<title>pyFTS.hyperparam.GridSearch — pyFTS 1.7 documentation</title>
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<h1>Source code for pyFTS.hyperparam.GridSearch</h1><div class="highlight"><pre>
|
||||
<span></span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">Util</span><span class="p">,</span> <span class="n">Membership</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">hofts</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="k">import</span> <span class="n">Grid</span><span class="p">,</span> <span class="n">Entropy</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.benchmarks</span> <span class="k">import</span> <span class="n">Measures</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.hyperparam</span> <span class="k">import</span> <span class="n">Util</span> <span class="k">as</span> <span class="n">hUtil</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">itertools</span> <span class="k">import</span> <span class="n">product</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="dict_individual"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.GridSearch.dict_individual">[docs]</a><span class="k">def</span> <span class="nf">dict_individual</span><span class="p">(</span><span class="n">mf</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">partitions</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">lags</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="p">{</span>
|
||||
<span class="s1">'mf'</span><span class="p">:</span> <span class="n">mf</span><span class="p">,</span>
|
||||
<span class="s1">'partitioner'</span><span class="p">:</span> <span class="n">partitioner</span><span class="p">,</span>
|
||||
<span class="s1">'npart'</span><span class="p">:</span> <span class="n">partitions</span><span class="p">,</span>
|
||||
<span class="s1">'alpha'</span><span class="p">:</span> <span class="n">alpha_cut</span><span class="p">,</span>
|
||||
<span class="s1">'order'</span><span class="p">:</span> <span class="n">order</span><span class="p">,</span>
|
||||
<span class="s1">'lags'</span><span class="p">:</span> <span class="n">lags</span>
|
||||
<span class="p">}</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="cluster_method"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.GridSearch.cluster_method">[docs]</a><span class="k">def</span> <span class="nf">cluster_method</span><span class="p">(</span><span class="n">individual</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">Util</span><span class="p">,</span> <span class="n">Membership</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">hofts</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="k">import</span> <span class="n">Grid</span><span class="p">,</span> <span class="n">Entropy</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.benchmarks</span> <span class="k">import</span> <span class="n">Measures</span>
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">individual</span><span class="p">[</span><span class="s1">'mf'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">mf</span> <span class="o">=</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span>
|
||||
<span class="k">elif</span> <span class="n">individual</span><span class="p">[</span><span class="s1">'mf'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
|
||||
<span class="n">mf</span> <span class="o">=</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trapmf</span>
|
||||
<span class="k">elif</span> <span class="n">individual</span><span class="p">[</span><span class="s1">'mf'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">3</span> <span class="ow">and</span> <span class="n">individual</span><span class="p">[</span><span class="s1">'partitioner'</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
|
||||
<span class="n">mf</span> <span class="o">=</span> <span class="n">Membership</span><span class="o">.</span><span class="n">gaussmf</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">mf</span> <span class="o">=</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span>
|
||||
|
||||
<span class="n">window_size</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_size'</span><span class="p">,</span> <span class="mi">800</span><span class="p">)</span>
|
||||
<span class="n">train_rate</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">'train_rate'</span><span class="p">,</span> <span class="o">.</span><span class="mi">8</span><span class="p">)</span>
|
||||
<span class="n">increment_rate</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">'increment_rate'</span><span class="p">,</span> <span class="o">.</span><span class="mi">2</span><span class="p">)</span>
|
||||
<span class="n">parameters</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">'parameters'</span><span class="p">,</span> <span class="p">{})</span>
|
||||
|
||||
<span class="n">errors</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">sizes</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">count</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">dataset</span><span class="p">,</span> <span class="n">window_size</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="n">train_rate</span><span class="p">,</span> <span class="n">inc</span><span class="o">=</span><span class="n">increment_rate</span><span class="p">):</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">individual</span><span class="p">[</span><span class="s1">'partitioner'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">partitioner</span> <span class="o">=</span> <span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">train</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">'npart'</span><span class="p">],</span> <span class="n">func</span><span class="o">=</span><span class="n">mf</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">individual</span><span class="p">[</span><span class="s1">'partitioner'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
|
||||
<span class="n">npart</span> <span class="o">=</span> <span class="n">individual</span><span class="p">[</span><span class="s1">'npart'</span><span class="p">]</span> <span class="k">if</span> <span class="n">individual</span><span class="p">[</span><span class="s1">'npart'</span><span class="p">]</span> <span class="o">></span> <span class="mi">10</span> <span class="k">else</span> <span class="mi">10</span>
|
||||
<span class="n">partitioner</span> <span class="o">=</span> <span class="n">Entropy</span><span class="o">.</span><span class="n">EntropyPartitioner</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">train</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="n">npart</span><span class="p">,</span> <span class="n">func</span><span class="o">=</span><span class="n">mf</span><span class="p">)</span>
|
||||
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">hofts</span><span class="o">.</span><span class="n">WeightedHighOrderFTS</span><span class="p">(</span><span class="n">partitioner</span><span class="o">=</span><span class="n">partitioner</span><span class="p">,</span>
|
||||
<span class="n">lags</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">'lags'</span><span class="p">],</span>
|
||||
<span class="n">alpha_cut</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">'alpha'</span><span class="p">],</span>
|
||||
<span class="n">order</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">'order'</span><span class="p">])</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train</span><span class="p">)</span>
|
||||
|
||||
<span class="n">forecasts</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="c1">#rmse, mape, u = Measures.get_point_statistics(test, model)</span>
|
||||
<span class="n">rmse</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">rmse</span><span class="p">(</span><span class="n">test</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">)</span>
|
||||
|
||||
<span class="n">size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
|
||||
|
||||
<span class="n">errors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">rmse</span><span class="p">)</span>
|
||||
<span class="n">sizes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">size</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="p">{</span><span class="s1">'parameters'</span><span class="p">:</span> <span class="n">individual</span><span class="p">,</span> <span class="s1">'rmse'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">errors</span><span class="p">),</span> <span class="s1">'size'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">size</span><span class="p">)}</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="process_jobs"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.GridSearch.process_jobs">[docs]</a><span class="k">def</span> <span class="nf">process_jobs</span><span class="p">(</span><span class="n">jobs</span><span class="p">,</span> <span class="n">datasetname</span><span class="p">,</span> <span class="n">conn</span><span class="p">):</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.distributed</span> <span class="k">import</span> <span class="n">dispy</span> <span class="k">as</span> <span class="n">dUtil</span>
|
||||
<span class="kn">import</span> <span class="nn">dispy</span>
|
||||
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">job</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">jobs</span><span class="p">):</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Processing job </span><span class="si">{}</span><span class="s2">"</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="n">result</span> <span class="o">=</span> <span class="n">job</span><span class="p">()</span>
|
||||
<span class="k">if</span> <span class="n">job</span><span class="o">.</span><span class="n">status</span> <span class="o">==</span> <span class="n">dispy</span><span class="o">.</span><span class="n">DispyJob</span><span class="o">.</span><span class="n">Finished</span> <span class="ow">and</span> <span class="n">result</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Processing result of </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">result</span><span class="p">))</span>
|
||||
|
||||
<span class="n">metrics</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'rmse'</span><span class="p">:</span> <span class="n">result</span><span class="p">[</span><span class="s1">'rmse'</span><span class="p">],</span> <span class="s1">'size'</span><span class="p">:</span> <span class="n">result</span><span class="p">[</span><span class="s1">'size'</span><span class="p">]}</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="n">metrics</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
|
||||
|
||||
<span class="n">param</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="s1">'parameters'</span><span class="p">]</span>
|
||||
|
||||
<span class="n">record</span> <span class="o">=</span> <span class="p">(</span><span class="n">datasetname</span><span class="p">,</span> <span class="s1">'GridSearch'</span><span class="p">,</span> <span class="s1">'WHOFTS'</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">param</span><span class="p">[</span><span class="s1">'mf'</span><span class="p">],</span>
|
||||
<span class="n">param</span><span class="p">[</span><span class="s1">'order'</span><span class="p">],</span> <span class="n">param</span><span class="p">[</span><span class="s1">'partitioner'</span><span class="p">],</span> <span class="n">param</span><span class="p">[</span><span class="s1">'npart'</span><span class="p">],</span>
|
||||
<span class="n">param</span><span class="p">[</span><span class="s1">'alpha'</span><span class="p">],</span> <span class="nb">str</span><span class="p">(</span><span class="n">param</span><span class="p">[</span><span class="s1">'lags'</span><span class="p">]),</span> <span class="n">metric</span><span class="p">,</span> <span class="n">metrics</span><span class="p">[</span><span class="n">metric</span><span class="p">])</span>
|
||||
|
||||
|
||||
<span class="n">hUtil</span><span class="o">.</span><span class="n">insert_hyperparam</span><span class="p">(</span><span class="n">record</span><span class="p">,</span> <span class="n">conn</span><span class="p">)</span>
|
||||
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">exception</span><span class="p">)</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">stdout</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="execute"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.GridSearch.execute">[docs]</a><span class="k">def</span> <span class="nf">execute</span><span class="p">(</span><span class="n">hyperparams</span><span class="p">,</span> <span class="n">datasetname</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.distributed</span> <span class="k">import</span> <span class="n">dispy</span> <span class="k">as</span> <span class="n">dUtil</span>
|
||||
<span class="kn">import</span> <span class="nn">dispy</span>
|
||||
|
||||
<span class="n">nodes</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">'nodes'</span><span class="p">,[</span><span class="s1">'127.0.0.1'</span><span class="p">])</span>
|
||||
|
||||
<span class="n">individuals</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">if</span> <span class="s1">'lags'</span> <span class="ow">in</span> <span class="n">hyperparams</span><span class="p">:</span>
|
||||
<span class="n">lags</span> <span class="o">=</span> <span class="n">hyperparams</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'lags'</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">lags</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</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="mi">50</span><span class="p">)]</span>
|
||||
|
||||
<span class="n">keys_sorted</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">hyperparams</span><span class="o">.</span><span class="n">keys</span><span class="p">())]</span>
|
||||
|
||||
<span class="n">index</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="nb">len</span><span class="p">(</span><span class="n">keys_sorted</span><span class="p">)):</span>
|
||||
<span class="n">index</span><span class="p">[</span><span class="n">keys_sorted</span><span class="p">[</span><span class="n">k</span><span class="p">]]</span> <span class="o">=</span> <span class="n">k</span>
|
||||
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Evaluation order: </span><span class="se">\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">index</span><span class="p">))</span>
|
||||
|
||||
<span class="n">hp_values</span> <span class="o">=</span> <span class="p">[</span>
|
||||
<span class="p">[</span><span class="n">v</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">hyperparams</span><span class="p">[</span><span class="n">hp</span><span class="p">]]</span>
|
||||
<span class="k">for</span> <span class="n">hp</span> <span class="ow">in</span> <span class="n">keys_sorted</span>
|
||||
<span class="p">]</span>
|
||||
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Evaluation values: </span><span class="se">\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">hp_values</span><span class="p">))</span>
|
||||
|
||||
<span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span> <span class="o">=</span> <span class="n">dUtil</span><span class="o">.</span><span class="n">start_dispy_cluster</span><span class="p">(</span><span class="n">cluster_method</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="n">nodes</span><span class="p">)</span>
|
||||
<span class="n">file</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">'file'</span><span class="p">,</span> <span class="s1">'hyperparam.db'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">conn</span> <span class="o">=</span> <span class="n">hUtil</span><span class="o">.</span><span class="n">open_hyperparam_db</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">instance</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">hp_values</span><span class="p">):</span>
|
||||
<span class="n">partitions</span> <span class="o">=</span> <span class="n">instance</span><span class="p">[</span><span class="n">index</span><span class="p">[</span><span class="s1">'partitions'</span><span class="p">]]</span>
|
||||
<span class="n">partitioner</span> <span class="o">=</span> <span class="n">instance</span><span class="p">[</span><span class="n">index</span><span class="p">[</span><span class="s1">'partitioner'</span><span class="p">]]</span>
|
||||
<span class="n">mf</span> <span class="o">=</span> <span class="n">instance</span><span class="p">[</span><span class="n">index</span><span class="p">[</span><span class="s1">'mf'</span><span class="p">]]</span>
|
||||
<span class="n">alpha_cut</span> <span class="o">=</span> <span class="n">instance</span><span class="p">[</span><span class="n">index</span><span class="p">[</span><span class="s1">'alpha'</span><span class="p">]]</span>
|
||||
<span class="n">order</span> <span class="o">=</span> <span class="n">instance</span><span class="p">[</span><span class="n">index</span><span class="p">[</span><span class="s1">'order'</span><span class="p">]]</span>
|
||||
<span class="n">count</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="k">for</span> <span class="n">lag1</span> <span class="ow">in</span> <span class="n">lags</span><span class="p">:</span> <span class="c1"># o é o lag1</span>
|
||||
<span class="n">_lags</span> <span class="o">=</span> <span class="p">[</span><span class="n">lag1</span><span class="p">]</span>
|
||||
<span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
|
||||
<span class="k">if</span> <span class="n">order</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">lag2</span> <span class="ow">in</span> <span class="n">lags</span><span class="p">:</span> <span class="c1"># o é o lag1</span>
|
||||
<span class="n">_lags2</span> <span class="o">=</span> <span class="p">[</span><span class="n">lag1</span><span class="p">,</span> <span class="n">lag1</span><span class="o">+</span><span class="n">lag2</span><span class="p">]</span>
|
||||
<span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
|
||||
<span class="k">if</span> <span class="n">order</span> <span class="o">></span> <span class="mi">2</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">lag3</span> <span class="ow">in</span> <span class="n">lags</span><span class="p">:</span> <span class="c1"># o é o lag1</span>
|
||||
<span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
|
||||
<span class="n">_lags3</span> <span class="o">=</span> <span class="p">[</span><span class="n">lag1</span><span class="p">,</span> <span class="n">lag1</span> <span class="o">+</span> <span class="n">lag2</span><span class="p">,</span> <span class="n">lag1</span> <span class="o">+</span> <span class="n">lag2</span><span class="o">+</span><span class="n">lag3</span> <span class="p">]</span>
|
||||
<span class="n">individuals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dict_individual</span><span class="p">(</span><span class="n">mf</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">partitions</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">_lags3</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="p">))</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">individuals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
|
||||
<span class="n">dict_individual</span><span class="p">(</span><span class="n">mf</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">partitions</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">_lags2</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="p">))</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">individuals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dict_individual</span><span class="p">(</span><span class="n">mf</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">partitions</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">_lags</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="p">))</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">count</span> <span class="o">></span> <span class="mi">10</span><span class="p">:</span>
|
||||
<span class="n">jobs</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">ind</span> <span class="ow">in</span> <span class="n">individuals</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Testing individual </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ind</span><span class="p">))</span>
|
||||
<span class="n">job</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">submit</span><span class="p">(</span><span class="n">ind</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="n">jobs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">job</span><span class="p">)</span>
|
||||
|
||||
<span class="n">process_jobs</span><span class="p">(</span><span class="n">jobs</span><span class="p">,</span> <span class="n">datasetname</span><span class="p">,</span> <span class="n">conn</span><span class="p">)</span>
|
||||
|
||||
<span class="n">count</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
|
||||
<span class="n">individuals</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">dUtil</span><span class="o">.</span><span class="n">stop_dispy_cluster</span><span class="p">(</span><span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span><span class="p">)</span></div>
|
||||
</pre></div>
|
||||
|
||||
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|
||||
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|
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|
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>index</a></li>
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>modules</a> |</li>
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© Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
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177
docs/build/html/_modules/pyFTS/models/chen.html
vendored
177
docs/build/html/_modules/pyFTS/models/chen.html
vendored
@ -1,177 +0,0 @@
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<h1>Source code for pyFTS.models.chen</h1><div class="highlight"><pre>
|
||||
<span></span><span class="sd">"""</span>
|
||||
<span class="sd">First Order Conventional Fuzzy Time Series by Chen (1996)</span>
|
||||
|
||||
<span class="sd">S.-M. Chen, “Forecasting enrollments based on fuzzy time series,” Fuzzy Sets Syst., vol. 81, no. 3, pp. 311–319, 1996.</span>
|
||||
<span class="sd">"""</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">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>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ConventionalFLRG"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.chen.ConventionalFLRG">[docs]</a><span class="k">class</span> <span class="nc">ConventionalFLRG</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">):</span>
|
||||
<span class="sd">"""First Order Conventional Fuzzy Logical Relationship Group"""</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">LHS</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">ConventionalFLRG</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="mi">1</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">LHS</span> <span class="o">=</span> <span class="n">LHS</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
|
||||
|
||||
<div class="viewcode-block" id="ConventionalFLRG.get_key"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.chen.ConventionalFLRG.get_key">[docs]</a> <span class="k">def</span> <span class="nf">get_key</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="n">sets</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">name</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ConventionalFLRG.append_rhs"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.chen.ConventionalFLRG.append_rhs">[docs]</a> <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</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="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">c</span><span class="p">)</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="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">LHS</span><span class="p">)</span> <span class="o">+</span> <span class="s2">" -> "</span>
|
||||
<span class="n">tmp2</span> <span class="o">=</span> <span class="s2">""</span>
|
||||
<span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">s</span><span class="p">:</span> <span class="n">s</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">tmp2</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="s2">","</span>
|
||||
<span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">tmp</span> <span class="o">+</span> <span class="n">tmp2</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ConventionalFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.chen.ConventionalFTS">[docs]</a><span class="k">class</span> <span class="nc">ConventionalFTS</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
|
||||
<span class="sd">"""Conventional Fuzzy Time Series"""</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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">ConventionalFTS</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">order</span><span class="o">=</span><span class="mi">1</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">name</span> <span class="o">=</span> <span class="s2">"Conventional FTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">"Chen"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">"CFTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
|
||||
<div class="viewcode-block" id="ConventionalFTS.generate_flrg"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.chen.ConventionalFTS.generate_flrg">[docs]</a> <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">):</span>
|
||||
<span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">flr</span><span class="o">.</span><span class="n">LHS</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="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</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">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span> <span class="o">=</span> <span class="n">ConventionalFLRG</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</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">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ConventionalFTS.train"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.chen.ConventionalFTS.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>
|
||||
|
||||
<span class="n">tmpdata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">'maximum'</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">flrs</span> <span class="o">=</span> <span class="n">FLR</span><span class="o">.</span><span class="n">generate_non_recurrent_flrs</span><span class="p">(</span><span class="n">tmpdata</span><span class="p">,</span> <span class="n">steps</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">standard_horizon</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg</span><span class="p">(</span><span class="n">flrs</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ConventionalFTS.forecast"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.chen.ConventionalFTS.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">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
|
||||
<span class="n">explain</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">'explain'</span><span class="p">,</span><span class="kc">False</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="k">if</span> <span class="ow">not</span> <span class="n">explain</span> <span class="k">else</span> <span class="mi">1</span>
|
||||
|
||||
<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="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
|
||||
|
||||
<span class="n">actual</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">get_maximum_membership_fuzzyset</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Fuzzyfication:</span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> -> </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">))</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">actual</span><span class="o">.</span><span class="n">name</span> <span class="ow">not</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">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">actual</span><span class="o">.</span><span class="n">centroid</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Rules:</span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> -> </span><span class="si">{}</span><span class="s2"> (Naïve)</span><span class="se">\t</span><span class="s2"> Midpoint: </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n\n</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">,</span><span class="n">actual</span><span class="o">.</span><span class="n">centroid</span><span class="p">))</span>
|
||||
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">_flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>
|
||||
|
||||
<span class="n">mp</span> <span class="o">=</span> <span class="n">_flrg</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mp</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Rules:</span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> </span><span class="se">\t</span><span class="s2"> Midpoint: </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">_flrg</span><span class="p">),</span> <span class="n">mp</span><span class="p">))</span>
|
||||
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Deffuzyfied value: </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mp</span><span class="p">))</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div></div>
|
||||
</pre></div>
|
||||
|
||||
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148
docs/build/html/_modules/pyFTS/models/cheng.html
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vendored
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<h1>Source code for pyFTS.models.cheng</h1><div class="highlight"><pre>
|
||||
<span></span><span class="sd">"""</span>
|
||||
<span class="sd">Trend Weighted Fuzzy Time Series by Cheng, Chen and Wu (2009)</span>
|
||||
|
||||
<span class="sd">C.-H. Cheng, Y.-S. Chen, and Y.-L. Wu, “Forecasting innovation diffusion of products using trend-weighted fuzzy time-series model,” </span>
|
||||
<span class="sd">Expert Syst. Appl., vol. 36, no. 2, pp. 1826–1832, 2009.</span>
|
||||
<span class="sd">"""</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">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="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">yu</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="TrendWeightedFLRG"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.cheng.TrendWeightedFLRG">[docs]</a><span class="k">class</span> <span class="nc">TrendWeightedFLRG</span><span class="p">(</span><span class="n">yu</span><span class="o">.</span><span class="n">WeightedFLRG</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> First Order Trend Weighted Fuzzy Logical Relationship Group</span>
|
||||
<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">LHS</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">TrendWeightedFLRG</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">LHS</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">w</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
|
||||
<div class="viewcode-block" id="TrendWeightedFLRG.weights"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.cheng.TrendWeightedFLRG.weights">[docs]</a> <span class="k">def</span> <span class="nf">weights</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">count_nochange</span> <span class="o">=</span> <span class="mf">0.0</span>
|
||||
<span class="n">count_up</span> <span class="o">=</span> <span class="mf">0.0</span>
|
||||
<span class="n">count_down</span> <span class="o">=</span> <span class="mf">0.0</span>
|
||||
<span class="n">weights</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="k">if</span> <span class="n">sets</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span> <span class="o">==</span> <span class="n">sets</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span><span class="p">:</span>
|
||||
<span class="n">count_nochange</span> <span class="o">+=</span> <span class="mf">1.0</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="n">count_nochange</span>
|
||||
<span class="k">elif</span> <span class="n">sets</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span> <span class="o">></span> <span class="n">sets</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span><span class="p">:</span>
|
||||
<span class="n">count_down</span> <span class="o">+=</span> <span class="mf">1.0</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="n">count_down</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">count_up</span> <span class="o">+=</span> <span class="mf">1.0</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="n">count_up</span>
|
||||
<span class="n">weights</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
|
||||
|
||||
<span class="n">tot</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">k</span> <span class="o">/</span> <span class="n">tot</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">weights</span><span class="p">])</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span></div></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="TrendWeightedFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.cheng.TrendWeightedFTS">[docs]</a><span class="k">class</span> <span class="nc">TrendWeightedFTS</span><span class="p">(</span><span class="n">yu</span><span class="o">.</span><span class="n">WeightedFTS</span><span class="p">):</span>
|
||||
<span class="sd">"""First Order Trend Weighted Fuzzy Time Series"""</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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">TrendWeightedFTS</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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">"TWFTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"Trend Weighted FTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">"Cheng"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
|
||||
<div class="viewcode-block" id="TrendWeightedFTS.generate_FLRG"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.cheng.TrendWeightedFTS.generate_FLRG">[docs]</a> <span class="k">def</span> <span class="nf">generate_FLRG</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">):</span>
|
||||
<span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">flr</span><span class="o">.</span><span class="n">LHS</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="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</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">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span> <span class="o">=</span> <span class="n">TrendWeightedFLRG</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</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">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div></div>
|
||||
</pre></div>
|
||||
|
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<h1>Source code for pyFTS.models.ensemble.ensemble</h1><div class="highlight"><pre>
|
||||
<span></span><span class="sd">"""</span>
|
||||
<span class="sd">EnsembleFTS wraps several FTS methods to ensemble their forecasts, providing point,</span>
|
||||
<span class="sd">interval and probabilistic forecasting.</span>
|
||||
|
||||
<span class="sd">Silva, P. C. L et al. Probabilistic Forecasting with Seasonal Ensemble Fuzzy Time-Series</span>
|
||||
<span class="sd">XIII Brazilian Congress on Computational Intelligence, 2017. Rio de Janeiro, Brazil.</span>
|
||||
<span class="sd">"""</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">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">SortedCollection</span><span class="p">,</span> <span class="n">fts</span><span class="p">,</span> <span class="n">tree</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">chen</span><span class="p">,</span> <span class="n">cheng</span><span class="p">,</span> <span class="n">hofts</span><span class="p">,</span> <span class="n">hwang</span><span class="p">,</span> <span class="n">ismailefendi</span><span class="p">,</span> <span class="n">sadaei</span><span class="p">,</span> <span class="n">song</span><span class="p">,</span> <span class="n">yu</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="k">import</span> <span class="n">ProbabilityDistribution</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="k">import</span> <span class="n">Grid</span>
|
||||
<span class="kn">import</span> <span class="nn">scipy.stats</span> <span class="k">as</span> <span class="nn">st</span>
|
||||
<span class="kn">from</span> <span class="nn">itertools</span> <span class="k">import</span> <span class="n">product</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="sampler"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.sampler">[docs]</a><span class="k">def</span> <span class="nf">sampler</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">quantiles</span><span class="p">,</span> <span class="n">bounds</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">qt</span> <span class="ow">in</span> <span class="n">quantiles</span><span class="p">:</span>
|
||||
<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">nanpercentile</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">q</span><span class="o">=</span><span class="n">qt</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span>
|
||||
<span class="k">if</span> <span class="n">bounds</span><span class="p">:</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">min</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="EnsembleFTS"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS">[docs]</a><span class="k">class</span> <span class="nc">EnsembleFTS</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Ensemble FTS</span>
|
||||
<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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">"EnsembleFTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"Ensemble FTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">is_wrapper</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">models</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="sd">"""A list of FTS models, the ensemble components"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">parameters</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="sd">"""A list with the parameters for each component model"""</span>
|
||||
<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>
|
||||
<span class="sd">"""The quantiles """</span>
|
||||
<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>
|
||||
<span class="sd">"""The method used to mix the several model's forecasts into a unique point forecast. Options: mean, median, quantile, exponential"""</span>
|
||||
<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>
|
||||
<span class="sd">"""The method used to mix the several model's forecasts into a interval forecast. Options: quantile, extremum, normal"""</span>
|
||||
|
||||
<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>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Append a new trained model to the ensemble</span>
|
||||
|
||||
<span class="sd"> :param model: FTS model</span>
|
||||
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">model</span><span class="o">.</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="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">has_seasonality</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">has_seasonality</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">original_min</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">original_min</span>
|
||||
<span class="k">elif</span> <span class="n">model</span><span class="o">.</span><span class="n">original_max</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">original_max</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="EnsembleFTS.get_UoD"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_UoD">[docs]</a> <span class="k">def</span> <span class="nf">get_UoD</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">original_min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span><span class="p">]</span></div>
|
||||
|
||||
<div class="viewcode-block" id="EnsembleFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.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>
|
||||
<span class="k">pass</span></div>
|
||||
|
||||
<div class="viewcode-block" id="EnsembleFTS.get_models_forecasts"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_models_forecasts">[docs]</a> <span class="k">def</span> <span class="nf">get_models_forecasts</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="n">tmp</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="bp">self</span><span class="o">.</span><span class="n">models</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="ow">or</span> <span class="n">model</span><span class="o">.</span><span class="n">has_seasonality</span><span class="p">:</span>
|
||||
<span class="n">forecast</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
|
||||
<span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="o">-</span><span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">:]</span>
|
||||
<span class="n">forecast</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">sample</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">forecast</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">))</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">forecast</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">forecast</span> <span class="o">=</span> <span class="n">forecast</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">forecast</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">))</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">forecast</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">forecast</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">forecast</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
|
||||
<span class="n">tmp</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">forecast</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">forecast</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">tmp</span></div>
|
||||
|
||||
<div class="viewcode-block" id="EnsembleFTS.get_point"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_point">[docs]</a> <span class="k">def</span> <span class="nf">get_point</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">forecasts</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">point_method</span> <span class="o">==</span> <span class="s1">'mean'</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">point_method</span> <span class="o">==</span> <span class="s1">'median'</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanpercentile</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">point_method</span> <span class="o">==</span> <span class="s1">'quantile'</span><span class="p">:</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>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanpercentile</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">alpha</span><span class="o">*</span><span class="mi">100</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">point_method</span> <span class="o">==</span> <span class="s1">'exponential'</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="bp">self</span><span class="o">.</span><span class="n">models</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">l</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">forecasts</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
|
||||
<span class="n">w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">l</span> <span class="o">-</span> <span class="n">k</span><span class="p">))</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">)])</span>
|
||||
<span class="n">w</span> <span class="o">=</span> <span class="n">w</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="n">w</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">*</span> <span class="n">forecasts</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">)])</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="EnsembleFTS.get_interval"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_interval">[docs]</a> <span class="k">def</span> <span class="nf">get_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">):</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">interval_method</span> <span class="o">==</span> <span class="s1">'extremum'</span><span class="p">:</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="nb">min</span><span class="p">(</span><span class="n">forecasts</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)])</span>
|
||||
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">interval_method</span> <span class="o">==</span> <span class="s1">'quantile'</span><span class="p">:</span>
|
||||
<span class="n">qt_lo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanpercentile</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">q</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">*</span> <span class="mi">100</span><span class="p">)</span>
|
||||
<span class="n">qt_up</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanpercentile</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">q</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">)</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">qt_lo</span><span class="p">,</span> <span class="n">qt_up</span><span class="p">])</span>
|
||||
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">interval_method</span> <span class="o">==</span> <span class="s1">'normal'</span><span class="p">:</span>
|
||||
<span class="n">mu</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
|
||||
<span class="n">sigma</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanvar</span><span class="p">(</span><span class="n">forecasts</span><span class="p">))</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mu</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">sigma</span><span class="p">)</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mu</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">sigma</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="EnsembleFTS.get_distribution_interquantile"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_distribution_interquantile">[docs]</a> <span class="k">def</span> <span class="nf">get_distribution_interquantile</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">alpha</span><span class="p">):</span>
|
||||
<span class="n">size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
|
||||
<span class="n">qt_lower</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">size</span> <span class="o">*</span> <span class="n">alpha</span><span class="p">))</span> <span class="o">-</span> <span class="mi">1</span>
|
||||
<span class="n">qt_upper</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">size</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span> <span class="n">alpha</span><span class="p">)))</span> <span class="o">-</span> <span class="mi">1</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)[</span><span class="n">qt_lower</span> <span class="p">:</span> <span class="n">qt_upper</span><span class="p">]</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="EnsembleFTS.forecast"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.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>
|
||||
|
||||
<span class="k">if</span> <span class="s2">"method"</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
|
||||
<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">'method'</span><span class="p">,</span><span class="s1">'mean'</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">data</span><span class="p">)</span>
|
||||
<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="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
|
||||
<span class="n">sample</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">max_lag</span> <span class="p">:</span> <span class="n">k</span><span class="p">]</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_models_forecasts</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
||||
<span class="n">point</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_point</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">point</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="EnsembleFTS.forecast_interval"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_interval">[docs]</a> <span class="k">def</span> <span class="nf">forecast_interval</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>
|
||||
|
||||
<span class="k">if</span> <span class="s2">"method"</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
|
||||
<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">'method'</span><span class="p">,</span><span class="s1">'quantile'</span><span class="p">)</span>
|
||||
|
||||
<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="s1">'alpha'</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</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">data</span><span class="p">)</span>
|
||||
|
||||
<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="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
|
||||
<span class="n">sample</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="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_models_forecasts</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
||||
<span class="n">interval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_interval</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">interval</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">interval</span> <span class="o">=</span> <span class="n">interval</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">interval</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="EnsembleFTS.forecast_ahead_interval"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_ahead_interval">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead_interval</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="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
|
||||
<span class="k">if</span> <span class="s1">'method'</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
|
||||
<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">'method'</span><span class="p">,</span><span class="s1">'quantile'</span><span class="p">)</span>
|
||||
|
||||
<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="s1">'alpha'</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">start</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">'start_at'</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
|
||||
|
||||
<span class="n">sample</span> <span class="o">=</span> <span class="p">[[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:</span> <span class="n">start</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="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="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">steps</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">forecasts</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">lags</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">i</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">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
|
||||
<span class="n">lags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sample</span><span class="p">[</span><span class="n">i</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="c1"># Trace the possible paths</span>
|
||||
<span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">lags</span><span class="p">):</span>
|
||||
<span class="n">forecasts</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">get_models_forecasts</span><span class="p">(</span><span class="n">path</span><span class="p">))</span>
|
||||
|
||||
<span class="n">sample</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sampler</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">.</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">),</span> <span class="n">bounds</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
|
||||
|
||||
<span class="n">interval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_interval</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">interval</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">interval</span> <span class="o">=</span> <span class="n">interval</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">interval</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div>
|
||||
|
||||
<div class="viewcode-block" id="EnsembleFTS.forecast_distribution"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_distribution">[docs]</a> <span class="k">def</span> <span class="nf">forecast_distribution</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>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">smooth</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">"smooth"</span><span class="p">,</span> <span class="s2">"KDE"</span><span class="p">)</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="kc">None</span><span class="p">)</span>
|
||||
|
||||
<span class="n">uod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_UoD</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="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)):</span>
|
||||
|
||||
<span class="n">sample</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="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_models_forecasts</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">alpha</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">forecasts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_distribution_interquantile</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span> <span class="n">alpha</span><span class="p">)</span>
|
||||
|
||||
<span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="n">smooth</span><span class="p">,</span> <span class="n">uod</span><span class="o">=</span><span class="n">uod</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">forecasts</span><span class="p">,</span>
|
||||
<span class="n">name</span><span class="o">=</span><span class="s2">""</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">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="EnsembleFTS.forecast_ahead_distribution"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_ahead_distribution">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead_distribution</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="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="s1">'method'</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
|
||||
<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">'method'</span><span class="p">,</span><span class="s1">'mean'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">smooth</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">"smooth"</span><span class="p">,</span> <span class="s2">"histogram"</span><span class="p">)</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="kc">None</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">start</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">'start_at'</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
|
||||
|
||||
<span class="n">uod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_UoD</span><span class="p">()</span>
|
||||
|
||||
<span class="n">sample</span> <span class="o">=</span> <span class="p">[[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:</span> <span class="n">start</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="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="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">steps</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">forecasts</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">lags</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">i</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">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
|
||||
<span class="n">lags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sample</span><span class="p">[</span><span class="n">i</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="c1"># Trace the possible paths</span>
|
||||
<span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">lags</span><span class="p">):</span>
|
||||
<span class="n">forecasts</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">get_models_forecasts</span><span class="p">(</span><span class="n">path</span><span class="p">))</span>
|
||||
|
||||
<span class="n">sample</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sampler</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">.</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">),</span> <span class="n">bounds</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">alpha</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">forecasts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_distribution_interquantile</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span> <span class="n">alpha</span><span class="p">)</span>
|
||||
|
||||
<span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="n">smooth</span><span class="p">,</span> <span class="n">uod</span><span class="o">=</span><span class="n">uod</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">forecasts</span><span class="p">,</span>
|
||||
<span class="n">name</span><span class="o">=</span><span class="s2">""</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">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="SimpleEnsembleFTS"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS">[docs]</a><span class="k">class</span> <span class="nc">SimpleEnsembleFTS</span><span class="p">(</span><span class="n">EnsembleFTS</span><span class="p">):</span>
|
||||
<span class="sd">'''</span>
|
||||
<span class="sd"> An homogeneous FTS method ensemble with variations on partitionings and orders.</span>
|
||||
<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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">SimpleEnsembleFTS</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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">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="n">hofts</span><span class="o">.</span><span class="n">WeightedHighOrderFTS</span><span class="p">)</span>
|
||||
<span class="sd">"""FTS method class that will be used on internal models"""</span>
|
||||
<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>
|
||||
<span class="sd">"""UoD partitioner class that will be used on internal methods"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">partitions</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">'partitions'</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">35</span><span class="p">,</span><span class="mi">10</span><span class="p">))</span>
|
||||
<span class="sd">"""Possible variations of number of partitions on internal models"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">orders</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">'orders'</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">])</span>
|
||||
<span class="sd">"""Possible variations of order on internal models"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</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">'name'</span><span class="p">,</span> <span class="s1">'EnsembleFTS-'</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">method</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">'.'</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
|
||||
|
||||
<div class="viewcode-block" id="SimpleEnsembleFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS.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>
|
||||
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitions</span><span class="p">:</span>
|
||||
<span class="n">fs</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="n">npart</span><span class="o">=</span><span class="n">k</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">order</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">orders</span><span class="p">:</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span><span class="p">(</span><span class="n">partitioner</span><span class="o">=</span><span class="n">fs</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="n">order</span><span class="p">)</span>
|
||||
|
||||
<span class="n">tmp</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="bp">self</span><span class="o">.</span><span class="n">append_model</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span></div></div>
|
||||
|
||||
|
||||
<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>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Creates an EnsembleFTS with all point forecast methods, sharing the same partitioner</span>
|
||||
<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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">3</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span><span class="s2">"Ensemble FTS"</span>
|
||||
|
||||
<div class="viewcode-block" id="AllMethodEnsembleFTS.set_transformations"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS.set_transformations">[docs]</a> <span class="k">def</span> <span class="nf">set_transformations</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">):</span>
|
||||
<span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">:</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">append_transformation</span><span class="p">(</span><span class="n">t</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="AllMethodEnsembleFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS.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>
|
||||
<span class="n">fo_methods</span> <span class="o">=</span> <span class="p">[</span><span class="n">song</span><span class="o">.</span><span class="n">ConventionalFTS</span><span class="p">,</span> <span class="n">chen</span><span class="o">.</span><span class="n">ConventionalFTS</span><span class="p">,</span> <span class="n">yu</span><span class="o">.</span><span class="n">WeightedFTS</span><span class="p">,</span> <span class="n">cheng</span><span class="o">.</span><span class="n">TrendWeightedFTS</span><span class="p">,</span>
|
||||
<span class="n">sadaei</span><span class="o">.</span><span class="n">ExponentialyWeightedFTS</span><span class="p">,</span> <span class="n">ismailefendi</span><span class="o">.</span><span class="n">ImprovedWeightedFTS</span><span class="p">]</span>
|
||||
|
||||
<span class="n">ho_methods</span> <span class="o">=</span> <span class="p">[</span><span class="n">hofts</span><span class="o">.</span><span class="n">HighOrderFTS</span><span class="p">,</span> <span class="n">hwang</span><span class="o">.</span><span class="n">HighOrderFTS</span><span class="p">]</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">method</span> <span class="ow">in</span> <span class="n">fo_methods</span><span class="p">:</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">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="bp">self</span><span class="o">.</span><span class="n">set_transformations</span><span class="p">(</span><span class="n">model</span><span class="p">)</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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">append_model</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">method</span> <span class="ow">in</span> <span class="n">ho_methods</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">o</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="bp">self</span><span class="o">.</span><span class="n">order</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">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="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">min_order</span> <span class="o">>=</span> <span class="n">o</span><span class="p">:</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">o</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">set_transformations</span><span class="p">(</span><span class="n">model</span><span class="p">)</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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">append_model</span><span class="p">(</span><span class="n">model</span><span class="p">)</span></div></div>
|
||||
|
||||
|
||||
|
||||
|
||||
</pre></div>
|
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<h1>Source code for pyFTS.models.ensemble.multiseasonal</h1><div class="highlight"><pre>
|
||||
<span></span><span class="sd">"""</span>
|
||||
<span class="sd">Silva, P. C. L et al. Probabilistic Forecasting with Seasonal Ensemble Fuzzy Time-Series</span>
|
||||
<span class="sd">XIII Brazilian Congress on Computational Intelligence, 2017. Rio de Janeiro, Brazil.</span>
|
||||
<span class="sd">"""</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">pyFTS.common</span> <span class="k">import</span> <span class="n">Util</span> <span class="k">as</span> <span class="n">cUtil</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.models.ensemble</span> <span class="k">import</span> <span class="n">ensemble</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.models.seasonal</span> <span class="k">import</span> <span class="n">cmsfts</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="k">import</span> <span class="n">ProbabilityDistribution</span>
|
||||
<span class="kn">from</span> <span class="nn">copy</span> <span class="k">import</span> <span class="n">deepcopy</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">import</span> <span class="nn">multiprocessing</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="train_individual_model"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.multiseasonal.train_individual_model">[docs]</a><span class="k">def</span> <span class="nf">train_individual_model</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">indexer</span><span class="p">):</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">'.'</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="n">diff</span> <span class="o">=</span> <span class="s2">"_diff"</span> <span class="k">if</span> <span class="n">partitioner</span><span class="o">.</span><span class="n">transformation</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="s2">""</span>
|
||||
<span class="n">_key</span> <span class="o">=</span> <span class="s2">"msfts_"</span> <span class="o">+</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="n">partitions</span><span class="p">)</span> <span class="o">+</span> <span class="n">diff</span> <span class="o">+</span> <span class="s2">"_"</span> <span class="o">+</span> <span class="n">indexer</span><span class="o">.</span><span class="n">name</span>
|
||||
|
||||
<span class="nb">print</span><span class="p">(</span><span class="n">_key</span><span class="p">)</span>
|
||||
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">cmsfts</span><span class="o">.</span><span class="n">ContextualMultiSeasonalFTS</span><span class="p">(</span><span class="n">_key</span><span class="p">,</span> <span class="n">indexer</span><span class="o">=</span><span class="n">indexer</span><span class="p">)</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">append_transformation</span><span class="p">(</span><span class="n">partitioner</span><span class="o">.</span><span class="n">transformation</span><span class="p">)</span>
|
||||
<span class="n">model</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="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="n">cUtil</span><span class="o">.</span><span class="n">persist_obj</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="s2">"models/"</span><span class="o">+</span><span class="n">_key</span><span class="o">+</span><span class="s2">".pkl"</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">model</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="SeasonalEnsembleFTS"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS">[docs]</a><span class="k">class</span> <span class="nc">SeasonalEnsembleFTS</span><span class="p">(</span><span class="n">ensemble</span><span class="o">.</span><span class="n">EnsembleFTS</span><span class="p">):</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">name</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">SeasonalEnsembleFTS</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="o">=</span><span class="s2">"Seasonal Ensemble FTS"</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">min_order</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">indexers</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">partitioners</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">has_seasonality</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
|
||||
<div class="viewcode-block" id="SeasonalEnsembleFTS.update_uod"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS.update_uod">[docs]</a> <span class="k">def</span> <span class="nf">update_uod</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="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</span><span class="p">))</span></div>
|
||||
|
||||
<div class="viewcode-block" id="SeasonalEnsembleFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS.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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</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">count</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="k">for</span> <span class="n">ix</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexers</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">pt</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioners</span><span class="p">:</span>
|
||||
<span class="n">pool</span><span class="p">[</span><span class="n">count</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'ix'</span><span class="p">:</span> <span class="n">ix</span><span class="p">,</span> <span class="s1">'pt'</span><span class="p">:</span> <span class="n">pt</span><span class="p">}</span>
|
||||
<span class="n">count</span> <span class="o">+=</span> <span class="mi">1</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">train_individual_model</span><span class="p">)(</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">pool</span><span class="p">[</span><span class="n">m</span><span class="p">][</span><span class="s1">'pt'</span><span class="p">]),</span> <span class="n">data</span><span class="p">,</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">pool</span><span class="p">[</span><span class="n">m</span><span class="p">][</span><span class="s1">'ix'</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="o">.</span><span class="n">keys</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="bp">self</span><span class="o">.</span><span class="n">append_model</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
|
||||
|
||||
<span class="n">cUtil</span><span class="o">.</span><span class="n">persist_obj</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">"models/"</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">".pkl"</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="SeasonalEnsembleFTS.forecast_distribution"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS.forecast_distribution">[docs]</a> <span class="k">def</span> <span class="nf">forecast_distribution</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>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">smooth</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">"smooth"</span><span class="p">,</span> <span class="s2">"KDE"</span><span class="p">)</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="kc">None</span><span class="p">)</span>
|
||||
|
||||
<span class="n">uod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_UoD</span><span class="p">()</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">:</span>
|
||||
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_models_forecasts</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">alpha</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_distribution_interquantile</span><span class="p">(</span> <span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span> <span class="n">alpha</span><span class="p">)</span>
|
||||
|
||||
<span class="n">name</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">indexer</span><span class="o">.</span><span class="n">get_index</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="n">k</span><span class="p">]))</span>
|
||||
|
||||
<span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="n">smooth</span><span class="p">,</span> <span class="n">uod</span><span class="o">=</span><span class="n">uod</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">tmp</span><span class="p">,</span>
|
||||
<span class="n">name</span><span class="o">=</span><span class="n">name</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">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div></div>
|
||||
</pre></div>
|
||||
|
||||
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383
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<h1>Source code for pyFTS.models.hofts</h1><div class="highlight"><pre>
|
||||
<span></span><span class="sd">"""</span>
|
||||
<span class="sd">High Order FTS</span>
|
||||
|
||||
<span class="sd">Severiano, S. A. Jr; Silva, P. C. L.; Sadaei, H. J.; Guimarães, F. G. Very Short-term Solar Forecasting</span>
|
||||
<span class="sd">using Fuzzy Time Series. 2017 IEEE International Conference on Fuzzy Systems. DOI10.1109/FUZZ-IEEE.2017.8015732</span>
|
||||
<span class="sd">"""</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">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>
|
||||
<span class="kn">from</span> <span class="nn">itertools</span> <span class="k">import</span> <span class="n">product</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="HighOrderFLRG"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFLRG">[docs]</a><span class="k">class</span> <span class="nc">HighOrderFLRG</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">):</span>
|
||||
<span class="sd">"""Conventional High Order Fuzzy Logical Relationship Group"""</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">order</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">HighOrderFLRG</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">order</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">LHS</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">strlhs</span> <span class="o">=</span> <span class="s2">""</span>
|
||||
|
||||
<div class="viewcode-block" id="HighOrderFLRG.append_rhs"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFLRG.append_rhs">[docs]</a> <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</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="k">if</span> <span class="n">c</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">c</span></div>
|
||||
|
||||
<div class="viewcode-block" id="HighOrderFLRG.append_lhs"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFLRG.append_lhs">[docs]</a> <span class="k">def</span> <span class="nf">append_lhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">c</span><span class="p">,(</span><span class="nb">tuple</span><span class="p">,</span><span class="nb">list</span><span class="p">)):</span>
|
||||
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">c</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">k</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">LHS</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">c</span><span class="p">)</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="n">tmp</span> <span class="o">=</span> <span class="s2">""</span>
|
||||
<span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</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="s2">","</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span> <span class="o">+</span> <span class="n">c</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="o">+</span> <span class="s2">" -> "</span> <span class="o">+</span> <span class="n">tmp</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="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="WeightedHighOrderFLRG"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFLRG">[docs]</a><span class="k">class</span> <span class="nc">WeightedHighOrderFLRG</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">):</span>
|
||||
<span class="sd">"""Weighted High Order Fuzzy Logical Relationship Group"""</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">order</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">WeightedHighOrderFLRG</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">order</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">LHS</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">=</span> <span class="mf">0.0</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">strlhs</span> <span class="o">=</span> <span class="s2">""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
|
||||
<div class="viewcode-block" id="WeightedHighOrderFLRG.append_rhs"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFLRG.append_rhs">[docs]</a> <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">fset</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="n">count</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">'count'</span><span class="p">,</span><span class="mf">1.0</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">fset</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">fset</span><span class="p">]</span> <span class="o">=</span> <span class="n">count</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">fset</span><span class="p">]</span> <span class="o">+=</span> <span class="n">count</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">+=</span> <span class="n">count</span></div>
|
||||
|
||||
<div class="viewcode-block" id="WeightedHighOrderFLRG.append_lhs"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFLRG.append_lhs">[docs]</a> <span class="k">def</span> <span class="nf">append_lhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">c</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="WeightedHighOrderFLRG.weights"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFLRG.weights">[docs]</a> <span class="k">def</span> <span class="nf">weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span></div>
|
||||
|
||||
<div class="viewcode-block" id="WeightedHighOrderFLRG.get_midpoint"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFLRG.get_midpoint">[docs]</a> <span class="k">def</span> <span class="nf">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">midpoint</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">mp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">sets</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">midpoint</span> <span class="o">=</span> <span class="n">mp</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">())</span>
|
||||
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">midpoint</span></div>
|
||||
|
||||
<div class="viewcode-block" id="WeightedHighOrderFLRG.get_lower"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFLRG.get_lower">[docs]</a> <span class="k">def</span> <span class="nf">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">lw</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="o">=</span> <span class="n">lw</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">())</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span></div>
|
||||
|
||||
<div class="viewcode-block" id="WeightedHighOrderFLRG.get_upper"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFLRG.get_upper">[docs]</a> <span class="k">def</span> <span class="nf">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">up</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">upper</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="o">=</span> <span class="n">up</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">())</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</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="n">_str</span> <span class="o">=</span> <span class="s2">""</span>
|
||||
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
|
||||
<span class="n">_str</span> <span class="o">+=</span> <span class="s2">", "</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">_str</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="s2">""</span>
|
||||
<span class="n">_str</span> <span class="o">+=</span> <span class="n">k</span> <span class="o">+</span> <span class="s2">" ("</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="o">+</span> <span class="s2">")"</span>
|
||||
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="o">+</span> <span class="s2">" -> "</span> <span class="o">+</span> <span class="n">_str</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="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="HighOrderFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS">[docs]</a><span class="k">class</span> <span class="nc">HighOrderFTS</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
|
||||
<span class="sd">"""Conventional High Order Fuzzy Time Series"""</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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">HighOrderFTS</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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"High Order FTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">"HOFTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">"Severiano, Silva, Sadaei and Guimarães"</span>
|
||||
<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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">order</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">"order"</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">configure_lags</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="HighOrderFTS.configure_lags"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.configure_lags">[docs]</a> <span class="k">def</span> <span class="nf">configure_lags</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>
|
||||
<span class="k">if</span> <span class="s2">"order"</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">order</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">"order"</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="s2">"lags"</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">lags</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">"lags"</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lags</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lags</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">max_lag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">lags</span> <span class="o">=</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="bp">self</span><span class="o">.</span><span class="n">order</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="HighOrderFTS.generate_lhs_flrg"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.generate_lhs_flrg">[docs]</a> <span class="k">def</span> <span class="nf">generate_lhs_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="n">explain</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
|
||||
<span class="n">nsample</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">"sets"</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha_cut</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">sample</span><span class="p">]</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">append_log</span><span class="p">(</span><span class="s2">"Fuzzyfication"</span><span class="p">,</span><span class="s2">"</span><span class="si">{}</span><span class="s2"> -> </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">nsample</span><span class="p">))</span>
|
||||
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg_fuzzyfied</span><span class="p">(</span><span class="n">nsample</span><span class="p">,</span> <span class="n">explain</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="HighOrderFTS.generate_lhs_flrg_fuzzyfied"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.generate_lhs_flrg_fuzzyfied">[docs]</a> <span class="k">def</span> <span class="nf">generate_lhs_flrg_fuzzyfied</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="n">explain</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="n">lags</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">flrgs</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">o</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lags</span><span class="p">):</span>
|
||||
<span class="n">lhs</span> <span class="o">=</span> <span class="n">sample</span><span class="p">[</span><span class="n">o</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span>
|
||||
<span class="n">lags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">lhs</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">append_log</span><span class="p">(</span><span class="s2">"Ordering Lags"</span><span class="p">,</span> <span class="s2">"Lag </span><span class="si">{}</span><span class="s2"> Value </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="n">lhs</span><span class="p">))</span>
|
||||
|
||||
<span class="c1"># Trace the possible paths</span>
|
||||
<span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">lags</span><span class="p">):</span>
|
||||
<span class="n">flrg</span> <span class="o">=</span> <span class="n">HighOrderFLRG</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">lhs</span> <span class="ow">in</span> <span class="n">path</span><span class="p">:</span>
|
||||
<span class="n">flrg</span><span class="o">.</span><span class="n">append_lhs</span><span class="p">(</span><span class="n">lhs</span><span class="p">)</span>
|
||||
|
||||
<span class="n">flrgs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">flrgs</span></div>
|
||||
|
||||
<div class="viewcode-block" id="HighOrderFTS.generate_flrg"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.generate_flrg">[docs]</a> <span class="k">def</span> <span class="nf">generate_flrg</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="n">_tmp_steps</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">standard_horizon</span> <span class="o">-</span> <span class="mi">1</span>
|
||||
<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>
|
||||
<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="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span> <span class="o">-</span> <span class="n">_tmp_steps</span><span class="p">):</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dump</span><span class="p">:</span> <span class="nb">print</span><span class="p">(</span><span class="s2">"FLR: "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">k</span><span class="p">))</span>
|
||||
|
||||
<span class="n">sample</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">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
|
||||
|
||||
<span class="n">rhs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">+</span> <span class="n">_tmp_steps</span><span class="p">],</span> <span class="n">mode</span><span class="o">=</span><span class="s2">"sets"</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha_cut</span><span class="p">)</span>
|
||||
|
||||
<span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="n">flrgs</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</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="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span> <span class="o">=</span> <span class="n">flrg</span><span class="p">;</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">st</span> <span class="ow">in</span> <span class="n">rhs</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">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">st</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="HighOrderFTS.generate_flrg_fuzzyfied"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.generate_flrg_fuzzyfied">[docs]</a> <span class="k">def</span> <span class="nf">generate_flrg_fuzzyfied</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="n">_tmp_steps</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">standard_horizon</span> <span class="o">-</span> <span class="mi">1</span>
|
||||
<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>
|
||||
<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="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span> <span class="o">-</span> <span class="n">_tmp_steps</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dump</span><span class="p">:</span> <span class="nb">print</span><span class="p">(</span><span class="s2">"FLR: "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">k</span><span class="p">))</span>
|
||||
|
||||
<span class="n">sample</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">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
|
||||
|
||||
<span class="n">rhs</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">_tmp_steps</span><span class="p">]</span>
|
||||
|
||||
<span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg_fuzzyfied</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="n">flrgs</span><span class="p">:</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</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="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span> <span class="o">=</span> <span class="n">flrg</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">st</span> <span class="ow">in</span> <span class="n">rhs</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">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">st</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="HighOrderFTS.train"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">configure_lags</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'fuzzyfied'</span><span class="p">,</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg</span><span class="p">(</span><span class="n">data</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">generate_flrg_fuzzyfied</span><span class="p">(</span><span class="n">data</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="HighOrderFTS.forecast"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.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">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
|
||||
<span class="n">explain</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">'explain'</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
|
||||
|
||||
<span class="n">fuzzyfied</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">'fuzzyfied'</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
|
||||
|
||||
<span class="n">mode</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">'mode'</span><span class="p">,</span> <span class="s1">'mean'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</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="k">if</span> <span class="ow">not</span> <span class="n">explain</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">+</span> <span class="mi">1</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">l</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">ndata</span>
|
||||
<span class="k">elif</span> <span class="n">l</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span>
|
||||
<span class="n">l</span> <span class="o">+=</span> <span class="mi">1</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="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
|
||||
|
||||
<span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</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">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
|
||||
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">fuzzyfied</span><span class="p">:</span>
|
||||
<span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">explain</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg_fuzzyfied</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">explain</span><span class="p">)</span>
|
||||
|
||||
<span class="n">midpoints</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">memberships</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="n">flrgs</span><span class="p">:</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</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="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">mp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span><span class="o">.</span><span class="n">centroid</span>
|
||||
<span class="n">mv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">sample</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">fuzzyfied</span> <span class="k">else</span> <span class="kc">None</span>
|
||||
<span class="n">midpoints</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mp</span><span class="p">)</span>
|
||||
<span class="n">memberships</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">append_log</span><span class="p">(</span><span class="s2">"Rule Matching"</span><span class="p">,</span> <span class="s2">"</span><span class="si">{}</span><span class="s2"> -> </span><span class="si">{}</span><span class="s2"> (Naïve) Midpoint: </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">),</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
|
||||
<span class="n">mp</span><span class="p">))</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
|
||||
<span class="n">mp</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
|
||||
<span class="n">mv</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_membership</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">fuzzyfied</span> <span class="k">else</span> <span class="kc">None</span>
|
||||
<span class="n">midpoints</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mp</span><span class="p">)</span>
|
||||
<span class="n">memberships</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">append_log</span><span class="p">(</span><span class="s2">"Rule Matching"</span><span class="p">,</span> <span class="s2">"</span><span class="si">{}</span><span class="s2">, Midpoint: </span><span class="si">{}</span><span class="s2"> Membership: </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">(),</span> <span class="n">mp</span><span class="p">,</span> <span class="n">mv</span><span class="p">))</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s2">"mean"</span> <span class="ow">or</span> <span class="n">fuzzyfied</span><span class="p">:</span>
|
||||
<span class="n">final</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">midpoints</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">explain</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">append_log</span><span class="p">(</span><span class="s2">"Deffuzyfication"</span><span class="p">,</span> <span class="s2">"By Mean: </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">final</span><span class="p">))</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">final</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">midpoints</span><span class="p">,</span> <span class="n">memberships</span><span class="p">)</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">memberships</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">explain</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">append_log</span><span class="p">(</span><span class="s2">"Deffuzyfication"</span><span class="p">,</span> <span class="s2">"By Memberships: </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">final</span><span class="p">))</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">final</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="WeightedHighOrderFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFTS">[docs]</a><span class="k">class</span> <span class="nc">WeightedHighOrderFTS</span><span class="p">(</span><span class="n">HighOrderFTS</span><span class="p">):</span>
|
||||
<span class="sd">"""Weighted High Order Fuzzy Time Series"""</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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">WeightedHighOrderFTS</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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"Weighted High Order FTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">"WHOFTS"</span>
|
||||
|
||||
<div class="viewcode-block" id="WeightedHighOrderFTS.generate_lhs_flrg_fuzzyfied"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFTS.generate_lhs_flrg_fuzzyfied">[docs]</a> <span class="k">def</span> <span class="nf">generate_lhs_flrg_fuzzyfied</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="n">explain</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="n">lags</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">flrgs</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">o</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lags</span><span class="p">):</span>
|
||||
<span class="n">lags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sample</span><span class="p">[</span><span class="n">o</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\t</span><span class="s2"> (Lag </span><span class="si">{}</span><span class="s2">) </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="n">sample</span><span class="p">[</span><span class="n">o</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
|
||||
|
||||
<span class="c1"># Trace the possible paths</span>
|
||||
<span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">lags</span><span class="p">):</span>
|
||||
<span class="n">flrg</span> <span class="o">=</span> <span class="n">WeightedHighOrderFLRG</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">lhs</span> <span class="ow">in</span> <span class="n">path</span><span class="p">:</span>
|
||||
<span class="n">flrg</span><span class="o">.</span><span class="n">append_lhs</span><span class="p">(</span><span class="n">lhs</span><span class="p">)</span>
|
||||
|
||||
<span class="n">flrgs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">flrgs</span></div></div>
|
||||
</pre></div>
|
||||
|
||||
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<h1>Source code for pyFTS.models.hwang</h1><div class="highlight"><pre>
|
||||
<span></span><span class="sd">"""</span>
|
||||
<span class="sd">High Order Fuzzy Time Series by Hwang, Chen and Lee (1998)</span>
|
||||
|
||||
<span class="sd">Jeng-Ren Hwang, Shyi-Ming Chen, and Chia-Hoang Lee, “Handling forecasting problems using fuzzy time series,” </span>
|
||||
<span class="sd">Fuzzy Sets Syst., no. 100, pp. 217–228, 1998.</span>
|
||||
<span class="sd">"""</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">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>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="HighOrderFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hwang.HighOrderFTS">[docs]</a><span class="k">class</span> <span class="nc">HighOrderFTS</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">HighOrderFTS</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>
|
||||
<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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">2</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"Hwang High Order FTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">"Hwang"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">"Hwang"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">configure_lags</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="HighOrderFTS.configure_lags"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hwang.HighOrderFTS.configure_lags">[docs]</a> <span class="k">def</span> <span class="nf">configure_lags</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>
|
||||
<span class="k">if</span> <span class="s2">"order"</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">order</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">"order"</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span></div>
|
||||
|
||||
<div class="viewcode-block" id="HighOrderFTS.forecast"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hwang.HighOrderFTS.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">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="p">)</span>
|
||||
|
||||
<span class="n">cn</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.0</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">)])</span>
|
||||
<span class="n">ow</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.0</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">)]</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)])</span>
|
||||
<span class="n">rn</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.0</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">)]</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)])</span>
|
||||
<span class="n">ft</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.0</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">)])</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">t</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="bp">self</span><span class="o">.</span><span class="n">order</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)):</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">ix</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">):</span>
|
||||
<span class="n">s</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
|
||||
<span class="n">cn</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">grant_bounds</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">t</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">))</span>
|
||||
<span class="k">for</span> <span class="n">w</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="bp">self</span><span class="o">.</span><span class="n">order</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
|
||||
<span class="n">ow</span><span class="p">[</span><span class="n">w</span><span class="p">,</span> <span class="n">ix</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">FuzzySet</span><span class="o">.</span><span class="n">grant_bounds</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">t</span> <span class="o">-</span> <span class="n">w</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">))</span>
|
||||
<span class="n">rn</span><span class="p">[</span><span class="n">w</span><span class="p">,</span> <span class="n">ix</span><span class="p">]</span> <span class="o">=</span> <span class="n">ow</span><span class="p">[</span><span class="n">w</span><span class="p">,</span> <span class="n">ix</span><span class="p">]</span> <span class="o">*</span> <span class="n">cn</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
|
||||
<span class="n">ft</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">ft</span><span class="p">[</span><span class="n">ix</span><span class="p">],</span> <span class="n">rn</span><span class="p">[</span><span class="n">w</span><span class="p">,</span> <span class="n">ix</span><span class="p">])</span>
|
||||
<span class="n">mft</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">ft</span><span class="p">)</span>
|
||||
<span class="n">out</span> <span class="o">=</span> <span class="mf">0.0</span>
|
||||
<span class="n">count</span> <span class="o">=</span> <span class="mf">0.0</span>
|
||||
<span class="k">for</span> <span class="n">ix</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">):</span>
|
||||
<span class="n">s</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
|
||||
<span class="k">if</span> <span class="n">ft</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span> <span class="o">==</span> <span class="n">mft</span><span class="p">:</span>
|
||||
<span class="n">out</span> <span class="o">=</span> <span class="n">out</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span>
|
||||
<span class="n">count</span> <span class="o">+=</span> <span class="mf">1.0</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">out</span> <span class="o">/</span> <span class="n">count</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="HighOrderFTS.train"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hwang.HighOrderFTS.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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">configure_lags</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div></div>
|
||||
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|
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273
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<h1>Source code for pyFTS.models.ifts</h1><div class="highlight"><pre>
|
||||
<span></span><span class="ch">#!/usr/bin/python</span>
|
||||
<span class="c1"># -*- coding: utf8 -*-</span>
|
||||
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd">High Order Interval Fuzzy Time Series</span>
|
||||
|
||||
<span class="sd">SILVA, Petrônio CL; SADAEI, Hossein Javedani; GUIMARÃES, Frederico Gadelha. Interval Forecasting with Fuzzy Time Series.</span>
|
||||
<span class="sd">In: Computational Intelligence (SSCI), 2016 IEEE Symposium Series on. IEEE, 2016. p. 1-8.</span>
|
||||
<span class="sd">"""</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">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">tree</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">hofts</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="IntervalFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.IntervalFTS">[docs]</a><span class="k">class</span> <span class="nc">IntervalFTS</span><span class="p">(</span><span class="n">hofts</span><span class="o">.</span><span class="n">HighOrderFTS</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> High Order Interval Fuzzy Time Series</span>
|
||||
<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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">IntervalFTS</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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">"IFTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"Interval FTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">"Silva, P.; Guimarães, F.; Sadaei, H. (2016)"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">has_point_forecasting</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
|
||||
<div class="viewcode-block" id="IntervalFTS.get_upper"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.IntervalFTS.get_upper">[docs]</a> <span class="k">def</span> <span class="nf">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</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="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span><span class="o">.</span><span class="n">upper</span>
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="IntervalFTS.get_lower"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.IntervalFTS.get_lower">[docs]</a> <span class="k">def</span> <span class="nf">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</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="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span><span class="o">.</span><span class="n">lower</span>
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="IntervalFTS.get_sequence_membership"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.IntervalFTS.get_sequence_membership">[docs]</a> <span class="k">def</span> <span class="nf">get_sequence_membership</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="n">fuzzySets</span><span class="p">):</span>
|
||||
<span class="n">mb</span> <span class="o">=</span> <span class="p">[</span><span class="n">fuzzySets</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">k</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="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">))]</span>
|
||||
<span class="k">return</span> <span class="n">mb</span></div>
|
||||
|
||||
<div class="viewcode-block" id="IntervalFTS.forecast_interval"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.IntervalFTS.forecast_interval">[docs]</a> <span class="k">def</span> <span class="nf">forecast_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</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="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="k">if</span> <span class="n">l</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="k">return</span> <span class="n">ndata</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="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
|
||||
|
||||
<span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</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">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
|
||||
|
||||
<span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
||||
|
||||
<span class="n">up</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">lo</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">affected_flrgs_memberships</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="n">flrgs</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
|
||||
<span class="n">mv</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_membership</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
|
||||
<span class="n">up</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">flrg</span><span class="p">))</span>
|
||||
<span class="n">lo</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">flrg</span><span class="p">))</span>
|
||||
<span class="n">affected_flrgs_memberships</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># gerar o intervalo</span>
|
||||
<span class="n">norm</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">affected_flrgs_memberships</span><span class="p">)</span>
|
||||
<span class="n">lo_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">lo</span><span class="p">)</span> <span class="o">/</span> <span class="n">norm</span>
|
||||
<span class="n">up_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">up</span><span class="p">)</span> <span class="o">/</span> <span class="n">norm</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">lo_</span><span class="p">,</span> <span class="n">up_</span><span class="p">])</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="IntervalFTS.forecast_ahead_interval"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.IntervalFTS.forecast_ahead_interval">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead_interval</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="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="n">start</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">'start_at'</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[[</span><span class="n">x</span><span class="p">,</span> <span class="n">x</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="n">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</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="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">+</span> <span class="n">steps</span><span class="p">):</span>
|
||||
<span class="n">interval_lower</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_uod</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">([</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">ret</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">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]])[</span><span class="mi">0</span><span class="p">])</span>
|
||||
<span class="n">interval_upper</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_uod</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">([</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">ret</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">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]])[</span><span class="mi">0</span><span class="p">])</span>
|
||||
<span class="n">interval</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">nanmin</span><span class="p">(</span><span class="n">interval_lower</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmax</span><span class="p">(</span><span class="n">interval_upper</span><span class="p">)]</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">interval</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="WeightedIntervalFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.WeightedIntervalFTS">[docs]</a><span class="k">class</span> <span class="nc">WeightedIntervalFTS</span><span class="p">(</span><span class="n">hofts</span><span class="o">.</span><span class="n">WeightedHighOrderFTS</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Weighted High Order Interval Fuzzy Time Series</span>
|
||||
<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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">WeightedIntervalFTS</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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">"WIFTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"Weighted Interval FTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">"Silva, P.; Guimarães, F.; Sadaei, H. (2016)"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">has_point_forecasting</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
|
||||
<div class="viewcode-block" id="WeightedIntervalFTS.get_upper"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.WeightedIntervalFTS.get_upper">[docs]</a> <span class="k">def</span> <span class="nf">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</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="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span><span class="o">.</span><span class="n">upper</span>
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="WeightedIntervalFTS.get_lower"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.WeightedIntervalFTS.get_lower">[docs]</a> <span class="k">def</span> <span class="nf">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</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="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span><span class="o">.</span><span class="n">lower</span>
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="WeightedIntervalFTS.get_sequence_membership"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.WeightedIntervalFTS.get_sequence_membership">[docs]</a> <span class="k">def</span> <span class="nf">get_sequence_membership</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="n">fuzzySets</span><span class="p">):</span>
|
||||
<span class="n">mb</span> <span class="o">=</span> <span class="p">[</span><span class="n">fuzzySets</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">k</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="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">))]</span>
|
||||
<span class="k">return</span> <span class="n">mb</span></div>
|
||||
|
||||
<div class="viewcode-block" id="WeightedIntervalFTS.forecast_interval"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.WeightedIntervalFTS.forecast_interval">[docs]</a> <span class="k">def</span> <span class="nf">forecast_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</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="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="k">if</span> <span class="n">l</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="k">return</span> <span class="n">ndata</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="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
|
||||
|
||||
<span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</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">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
|
||||
|
||||
<span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
||||
|
||||
<span class="n">up</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">lo</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">affected_flrgs_memberships</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="n">flrgs</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
|
||||
<span class="n">mv</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_membership</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
|
||||
<span class="n">up</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">flrg</span><span class="p">))</span>
|
||||
<span class="n">lo</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">flrg</span><span class="p">))</span>
|
||||
<span class="n">affected_flrgs_memberships</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># gerar o intervalo</span>
|
||||
<span class="n">norm</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">affected_flrgs_memberships</span><span class="p">)</span>
|
||||
<span class="n">lo_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">lo</span><span class="p">)</span> <span class="o">/</span> <span class="n">norm</span>
|
||||
<span class="n">up_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">up</span><span class="p">)</span> <span class="o">/</span> <span class="n">norm</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">lo_</span><span class="p">,</span> <span class="n">up_</span><span class="p">])</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="WeightedIntervalFTS.forecast_ahead_interval"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.WeightedIntervalFTS.forecast_ahead_interval">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead_interval</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="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="n">start</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">'start_at'</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[[</span><span class="n">x</span><span class="p">,</span> <span class="n">x</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="n">start</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</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="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">+</span> <span class="n">steps</span><span class="p">):</span>
|
||||
<span class="n">interval_lower</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_uod</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">([</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">ret</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">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]])[</span><span class="mi">0</span><span class="p">])</span>
|
||||
<span class="n">interval_upper</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_uod</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">([</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">ret</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">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]])[</span><span class="mi">0</span><span class="p">])</span>
|
||||
<span class="n">interval</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">nanmin</span><span class="p">(</span><span class="n">interval_lower</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmax</span><span class="p">(</span><span class="n">interval_upper</span><span class="p">)]</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">interval</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div></div>
|
||||
|
||||
|
||||
</pre></div>
|
||||
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<h1>Source code for pyFTS.models.incremental.IncrementalEnsemble</h1><div class="highlight"><pre>
|
||||
<span></span><span class="sd">'''</span>
|
||||
<span class="sd">Time Variant/Incremental Ensemble of FTS methods</span>
|
||||
<span class="sd">'''</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">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>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="k">import</span> <span class="n">Grid</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">hofts</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.models.ensemble</span> <span class="k">import</span> <span class="n">ensemble</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="IncrementalEnsembleFTS"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS">[docs]</a><span class="k">class</span> <span class="nc">IncrementalEnsembleFTS</span><span class="p">(</span><span class="n">ensemble</span><span class="o">.</span><span class="n">EnsembleFTS</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Time Variant/Incremental Ensemble of FTS methods</span>
|
||||
<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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">IncrementalEnsembleFTS</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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">"IncrementalEnsembleFTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"Incremental Ensemble FTS"</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">order</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">'order'</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<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>
|
||||
<span class="sd">"""The partitioner method to be called when a new model is build"""</span>
|
||||
<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>
|
||||
<span class="sd">"""The partitioner method parameters"""</span>
|
||||
|
||||
<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="n">hofts</span><span class="o">.</span><span class="n">WeightedHighOrderFTS</span><span class="p">)</span>
|
||||
<span class="sd">"""The FTS method to be called when a new model is build"""</span>
|
||||
<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>
|
||||
<span class="sd">"""The FTS method specific parameters"""</span>
|
||||
|
||||
<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>
|
||||
<span class="sd">"""The memory window length"""</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">batch_size</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">'batch_size'</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
|
||||
<span class="sd">"""The batch interval between each retraining"""</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">num_models</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">'num_models'</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
|
||||
<span class="sd">"""The number of models to hold in the ensemble"""</span>
|
||||
|
||||
<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">'exponential'</span><span class="p">)</span>
|
||||
|
||||
<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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_lag</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>
|
||||
|
||||
<div class="viewcode-block" id="IncrementalEnsembleFTS.offset"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.offset">[docs]</a> <span class="k">def</span> <span class="nf">offset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span></div>
|
||||
|
||||
<div class="viewcode-block" id="IncrementalEnsembleFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.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>
|
||||
|
||||
<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>
|
||||
<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="n">partitioner</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>
|
||||
<span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_high_order</span><span class="p">:</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="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>
|
||||
<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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">append_model</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="p">)</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">num_models</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="IncrementalEnsembleFTS.forecast"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.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>
|
||||
<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>
|
||||
<span class="n">no_update</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">'no_update'</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">no_update</span><span class="p">:</span>
|
||||
<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="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
|
||||
<span class="n">sample</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="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_models_forecasts</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
||||
<span class="n">point</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_point</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">point</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">ret</span>
|
||||
|
||||
<span class="n">data_window</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<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="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
|
||||
|
||||
<span class="n">k2</span> <span class="o">=</span> <span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span>
|
||||
|
||||
<span class="n">data_window</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">k2</span><span class="p">])</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">k2</span> <span class="o">>=</span> <span class="bp">self</span><span class="o">.</span><span class="n">window_length</span><span class="p">:</span>
|
||||
<span class="n">data_window</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">k</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">k2</span> <span class="o">>=</span> <span class="bp">self</span><span class="o">.</span><span class="n">window_length</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">data_window</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k2</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_models_forecasts</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
||||
<span class="n">point</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_point</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">point</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="IncrementalEnsembleFTS.forecast_ahead"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.forecast_ahead">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead</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="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<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="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">data</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
|
||||
|
||||
<span class="n">start</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">'start_at'</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">data</span><span class="p">[:</span><span class="n">start</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="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">start</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">steps</span><span class="o">+</span><span class="n">start</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">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">ret</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="n">no_update</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tmp</span><span class="p">,(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
|
||||
<span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div></div>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</pre></div>
|
||||
|
||||
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<h1>Source code for pyFTS.models.incremental.TimeVariant</h1><div class="highlight"><pre>
|
||||
<span></span><span class="sd">"""</span>
|
||||
<span class="sd">Meta model that wraps another FTS method and continously retrain it using a data window with</span>
|
||||
<span class="sd">the most recent data</span>
|
||||
<span class="sd">"""</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">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>
|
||||
<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="Retrainer"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.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>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Meta model for incremental/online learning that retrain its internal model after</span>
|
||||
<span class="sd"> data windows controlled by the parameter 'batch_size', using as the training data a</span>
|
||||
<span class="sd"> window of recent lags, whose size is controlled by the parameter 'window_length'.</span>
|
||||
<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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<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>
|
||||
|
||||
<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>
|
||||
<span class="sd">"""The partitioner method to be called when a new model is build"""</span>
|
||||
<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>
|
||||
<span class="sd">"""The partitioner method parameters"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="sd">"""The most recent trained partitioner"""</span>
|
||||
|
||||
<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>
|
||||
<span class="sd">"""The FTS method to be called when a new model is build"""</span>
|
||||
<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>
|
||||
<span class="sd">"""The FTS method specific parameters"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="sd">"""The most recent trained model"""</span>
|
||||
|
||||
<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>
|
||||
<span class="sd">"""The memory window length"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">auto_update</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<span class="sd">"""If true the model is updated at each time and not recreated"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">batch_size</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">'batch_size'</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
|
||||
<span class="sd">"""The batch interval between each retraining"""</span>
|
||||
<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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">is_time_variant</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_lag</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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">is_wrapper</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
|
||||
<div class="viewcode-block" id="Retrainer.train"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.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>
|
||||
<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>
|
||||
<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="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">fts_params</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">is_high_order</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">order</span> <span class="o">=</span> <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>
|
||||
<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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">"TimeVariant - "</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">shortname</span></div>
|
||||
|
||||
<div class="viewcode-block" id="Retrainer.forecast"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.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>
|
||||
<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>
|
||||
|
||||
<span class="n">no_update</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">'no_update'</span><span class="p">,</span><span class="kc">False</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">no_update</span><span class="p">:</span>
|
||||
<span class="k">return</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">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<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>
|
||||
|
||||
<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="o">+</span><span class="mi">1</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="n">k</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="ow">is</span> <span class="kc">None</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>
|
||||
|
||||
<div class="viewcode-block" id="Retrainer.forecast_ahead"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.forecast_ahead">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead</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="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<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="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">data</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
|
||||
|
||||
<span class="n">start</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">'start_at'</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">data</span><span class="p">[:</span><span class="n">start</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="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">start</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">steps</span><span class="o">+</span><span class="n">start</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">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">ret</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="n">no_update</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tmp</span><span class="p">,(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
|
||||
<span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div>
|
||||
|
||||
<div class="viewcode-block" id="Retrainer.offset"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.offset">[docs]</a> <span class="k">def</span> <span class="nf">offset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</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>
|
||||
|
||||
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<h1>Source code for pyFTS.models.ismailefendi</h1><div class="highlight"><pre>
|
||||
<span></span><span class="sd">"""</span>
|
||||
<span class="sd">First Order Improved Weighted Fuzzy Time Series by Efendi, Ismail and Deris (2013)</span>
|
||||
|
||||
<span class="sd">R. Efendi, Z. Ismail, and M. M. Deris, “Improved weight Fuzzy Time Series as used in the exchange rates forecasting of </span>
|
||||
<span class="sd">US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1, p. 1350005, 2013.</span>
|
||||
<span class="sd">"""</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">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>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ImprovedWeightedFLRG"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFLRG">[docs]</a><span class="k">class</span> <span class="nc">ImprovedWeightedFLRG</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">):</span>
|
||||
<span class="sd">"""First Order Improved Weighted Fuzzy Logical Relationship Group"""</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">LHS</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">ImprovedWeightedFLRG</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="mi">1</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">LHS</span> <span class="o">=</span> <span class="n">LHS</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">rhs_counts</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">=</span> <span class="mf">0.0</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
|
||||
<div class="viewcode-block" id="ImprovedWeightedFLRG.append_rhs"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFLRG.append_rhs">[docs]</a> <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</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">count</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">'count'</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">c</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">c</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">rhs_counts</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">count</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">rhs_counts</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">+=</span> <span class="n">count</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">+=</span> <span class="n">count</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ImprovedWeightedFLRG.weights"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFLRG.weights">[docs]</a> <span class="k">def</span> <span class="nf">weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">rhs_counts</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</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="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span> <span class="o">+</span> <span class="s2">" -> "</span>
|
||||
<span class="n">tmp2</span> <span class="o">=</span> <span class="s2">""</span>
|
||||
<span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">tmp2</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="s2">","</span>
|
||||
<span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="n">c</span> <span class="o">+</span> <span class="s2">"("</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rhs_counts</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="o">+</span> <span class="s2">")"</span>
|
||||
<span class="k">return</span> <span class="n">tmp</span> <span class="o">+</span> <span class="n">tmp2</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="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ImprovedWeightedFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFTS">[docs]</a><span class="k">class</span> <span class="nc">ImprovedWeightedFTS</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
|
||||
<span class="sd">"""First Order Improved Weighted Fuzzy Time Series"""</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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">ImprovedWeightedFTS</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">order</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">"IWFTS"</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">name</span> <span class="o">=</span> <span class="s2">"Improved Weighted FTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">"Ismail & Efendi"</span>
|
||||
|
||||
<div class="viewcode-block" id="ImprovedWeightedFTS.generate_flrg"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFTS.generate_flrg">[docs]</a> <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">):</span>
|
||||
<span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">flr</span><span class="o">.</span><span class="n">LHS</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="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</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">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span> <span class="o">=</span> <span class="n">ImprovedWeightedFLRG</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</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">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ImprovedWeightedFTS.train"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFTS.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">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
|
||||
<span class="n">tmpdata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">'maximum'</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">flrs</span> <span class="o">=</span> <span class="n">FLR</span><span class="o">.</span><span class="n">generate_recurrent_flrs</span><span class="p">(</span><span class="n">tmpdata</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg</span><span class="p">(</span><span class="n">flrs</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ImprovedWeightedFTS.forecast"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFTS.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">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
|
||||
<span class="n">explain</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">'explain'</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">ordered_sets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ordered_sets</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">set_ordered</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ndata</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="k">if</span> <span class="ow">not</span> <span class="n">explain</span> <span class="k">else</span> <span class="mi">1</span>
|
||||
|
||||
<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="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
|
||||
|
||||
<span class="n">actual</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">get_maximum_membership_fuzzyset</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">ordered_sets</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Fuzzyfication:</span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> -> </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">))</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">actual</span><span class="o">.</span><span class="n">name</span> <span class="ow">not</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">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">actual</span><span class="o">.</span><span class="n">centroid</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Rules:</span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> -> </span><span class="si">{}</span><span class="s2"> (Naïve)</span><span class="se">\t</span><span class="s2"> Midpoint: </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n\n</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">,</span><span class="n">actual</span><span class="o">.</span><span class="n">centroid</span><span class="p">))</span>
|
||||
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>
|
||||
<span class="n">mp</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_midpoints</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
|
||||
|
||||
<span class="n">final</span> <span class="o">=</span> <span class="n">mp</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">weights</span><span class="p">())</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">final</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Rules:</span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n\n</span><span class="s2"> "</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">flrg</span><span class="p">)))</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Midpoints: </span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="se">\n\n</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mp</span><span class="p">))</span>
|
||||
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Deffuzyfied value: </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">final</span><span class="p">))</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div></div>
|
||||
</pre></div>
|
||||
|
||||
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<h1>Source code for pyFTS.models.multivariate.cmvfts</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 class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
|
||||
<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>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">hofts</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.models.multivariate</span> <span class="k">import</span> <span class="n">mvfts</span><span class="p">,</span> <span class="n">grid</span><span class="p">,</span> <span class="n">common</span>
|
||||
<span class="kn">from</span> <span class="nn">types</span> <span class="k">import</span> <span class="n">LambdaType</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ClusteredMVFTS"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS">[docs]</a><span class="k">class</span> <span class="nc">ClusteredMVFTS</span><span class="p">(</span><span class="n">mvfts</span><span class="o">.</span><span class="n">MVFTS</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Meta model for high order, clustered multivariate FTS</span>
|
||||
<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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">ClusteredMVFTS</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>
|
||||
|
||||
<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="n">hofts</span><span class="o">.</span><span class="n">WeightedHighOrderFTS</span><span class="p">)</span>
|
||||
<span class="sd">"""The FTS method to be called when a new model is build"""</span>
|
||||
<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>
|
||||
<span class="sd">"""The FTS method specific parameters"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="sd">"""The most recent trained model"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">knn</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">'knn'</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
|
||||
|
||||
<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>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">is_clustered</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">order</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">"order"</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">lags</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">"lags"</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">alpha_cut</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">'alpha_cut'</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">"ClusteredMVFTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"Clustered Multivariate FTS"</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">pre_fuzzyfy</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">'pre_fuzzyfy'</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">fuzzyfy_mode</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">'fuzzyfy_mode'</span><span class="p">,</span> <span class="s1">'sets'</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="ClusteredMVFTS.fuzzyfy"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.fuzzyfy">[docs]</a> <span class="k">def</span> <span class="nf">fuzzyfy</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="n">ndata</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">iterrows</span><span class="p">()</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="k">else</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
|
||||
<span class="n">data_point</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">format_data</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
|
||||
<span class="n">ndata</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</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">mode</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">fuzzyfy_mode</span><span class="p">))</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ndata</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ClusteredMVFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.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>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">fts_params</span><span class="p">[</span><span class="s1">'order'</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
|
||||
|
||||
<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="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">fts_params</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</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="bp">self</span><span class="o">.</span><span class="n">check_data</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">model</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">fuzzyfied</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pre_fuzzyfy</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">prune</span><span class="p">()</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ClusteredMVFTS.check_data"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.check_data">[docs]</a> <span class="k">def</span> <span class="nf">check_data</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="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">pre_fuzzyfy</span><span class="p">:</span>
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">format_data</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">to_dict</span><span class="p">(</span><span class="s1">'records'</span><span class="p">)]</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ndata</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ClusteredMVFTS.forecast"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.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>
|
||||
|
||||
<span class="n">ndata1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</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="bp">self</span><span class="o">.</span><span class="n">check_data</span><span class="p">(</span><span class="n">ndata1</span><span class="p">)</span>
|
||||
|
||||
<span class="n">pre_fuzz</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">'pre_fuzzyfy'</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">pre_fuzzyfy</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">fuzzyfied</span><span class="o">=</span><span class="n">pre_fuzz</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="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">apply_inverse_transformations</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span>
|
||||
<span class="n">params</span><span class="o">=</span><span class="n">data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ClusteredMVFTS.forecast_interval"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_interval">[docs]</a> <span class="k">def</span> <span class="nf">forecast_interval</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>
|
||||
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">has_interval_forecasting</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">"The internal method does not support interval forecasting!"</span><span class="p">)</span>
|
||||
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">check_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
|
||||
<span class="n">pre_fuzz</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">'pre_fuzzyfy'</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">pre_fuzzyfy</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">fuzzyfied</span><span class="o">=</span><span class="n">pre_fuzz</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ClusteredMVFTS.forecast_distribution"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_distribution">[docs]</a> <span class="k">def</span> <span class="nf">forecast_distribution</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>
|
||||
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">has_probability_forecasting</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">"The internal method does not support probabilistic forecasting!"</span><span class="p">)</span>
|
||||
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">check_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
|
||||
<span class="n">pre_fuzz</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">'pre_fuzzyfy'</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">pre_fuzzyfy</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">forecast_distribution</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">fuzzyfied</span><span class="o">=</span><span class="n">pre_fuzz</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ClusteredMVFTS.forecast_ahead_distribution"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_ahead_distribution">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead_distribution</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="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
|
||||
<span class="n">generators</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">'generators'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">generators</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">'You must provide parameter </span><span class="se">\'</span><span class="s1">generators</span><span class="se">\'</span><span class="s1">! generators is a dict where the keys'</span> <span class="o">+</span>
|
||||
<span class="s1">' are the dataframe column names (except the target_variable) and the values are '</span> <span class="o">+</span>
|
||||
<span class="s1">'lambda functions that accept one value (the actual value of the variable) '</span>
|
||||
<span class="s1">' and return the next value or trained FTS models that accept the actual values and '</span>
|
||||
<span class="s1">'forecast new ones.'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
|
||||
<span class="n">start</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">'start_at'</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">start</span><span class="p">:</span> <span class="n">start</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</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="mi">0</span><span class="p">,</span> <span class="n">steps</span><span class="p">):</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_distribution</span><span class="p">(</span><span class="n">sample</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
|
||||
|
||||
<span class="n">new_data_point</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">data_label</span> <span class="ow">in</span> <span class="n">generators</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
|
||||
<span class="k">if</span> <span class="n">data_label</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">],</span> <span class="n">LambdaType</span><span class="p">):</span>
|
||||
<span class="n">last_data_point</span> <span class="o">=</span> <span class="n">sample</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="n">new_data_point</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">](</span><span class="n">last_data_point</span><span class="p">[</span><span class="n">data_label</span><span class="p">])</span>
|
||||
|
||||
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">],</span> <span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
|
||||
<span class="n">gen_model</span> <span class="o">=</span> <span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span>
|
||||
<span class="n">last_data_point</span> <span class="o">=</span> <span class="n">sample</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="n">gen_model</span><span class="o">.</span><span class="n">order</span><span class="p">:]</span>
|
||||
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">gen_model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
|
||||
<span class="n">last_data_point</span> <span class="o">=</span> <span class="n">last_data_point</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
|
||||
|
||||
<span class="n">new_data_point</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">gen_model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">last_data_point</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
|
||||
|
||||
<span class="n">new_data_point</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">expected_value</span><span class="p">()</span>
|
||||
|
||||
<span class="n">sample</span> <span class="o">=</span> <span class="n">sample</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">new_data_point</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ClusteredMVFTS.forecast_multivariate"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_multivariate">[docs]</a> <span class="k">def</span> <span class="nf">forecast_multivariate</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>
|
||||
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">check_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
|
||||
<span class="n">generators</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">'generators'</span><span class="p">,</span> <span class="p">{})</span>
|
||||
|
||||
<span class="n">already_processed_cols</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
|
||||
<span class="n">ret</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">fuzzyfied</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pre_fuzzyfy</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">var</span><span class="o">.</span><span class="n">data_label</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">already_processed_cols</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">var</span><span class="o">.</span><span class="n">data_label</span> <span class="ow">in</span> <span class="n">generators</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">generators</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">],</span> <span class="n">LambdaType</span><span class="p">):</span>
|
||||
<span class="n">fx</span> <span class="o">=</span> <span class="n">generators</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span>
|
||||
<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="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</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">ret</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">fx</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:]]</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ret</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">fx</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])]</span>
|
||||
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">generators</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">],</span> <span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">generators</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
|
||||
<span class="n">ret</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ret</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">name</span> <span class="o">!=</span> <span class="n">var</span><span class="o">.</span><span class="n">name</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span> <span class="o">=</span> <span class="n">var</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">change_target_variable</span><span class="p">(</span><span class="n">var</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">partitioner</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">reset_calculated_values</span><span class="p">()</span>
|
||||
<span class="n">ret</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">fuzzyfied</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pre_fuzzyfy</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<span class="n">already_processed_cols</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">ret</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ClusteredMVFTS.forecast_ahead_multivariate"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_ahead_multivariate">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead_multivariate</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="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
|
||||
<span class="n">start</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">'start_at'</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="o">+</span><span class="n">start</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="mi">0</span><span class="p">,</span> <span class="n">steps</span><span class="p">):</span>
|
||||
<span class="n">sample</span> <span class="o">=</span> <span class="n">ret</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">k</span><span class="p">:</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="o">+</span><span class="n">k</span><span class="p">]</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_multivariate</span><span class="p">(</span><span class="n">sample</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">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</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>
|
||||
|
||||
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|
||||
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<h1>Source code for pyFTS.models.multivariate.granular</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">from</span> <span class="nn">pyFTS.models.multivariate</span> <span class="k">import</span> <span class="n">cmvfts</span><span class="p">,</span> <span class="n">grid</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">hofts</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="GranularWMVFTS"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.granular.GranularWMVFTS">[docs]</a><span class="k">class</span> <span class="nc">GranularWMVFTS</span><span class="p">(</span><span class="n">cmvfts</span><span class="o">.</span><span class="n">ClusteredMVFTS</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Granular multivariate weighted high order FTS</span>
|
||||
<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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">GranularWMVFTS</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>
|
||||
|
||||
<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="n">hofts</span><span class="o">.</span><span class="n">WeightedHighOrderFTS</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="kc">None</span>
|
||||
<span class="sd">"""The most recent trained model"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">knn</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">'knn'</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">order</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">"order"</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">"FIG-FTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"Fuzzy Information Granular FTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">mode</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">'mode'</span><span class="p">,</span><span class="s1">'sets'</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="GranularWMVFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.granular.GranularWMVFTS.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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">IncrementalGridCluster</span><span class="p">(</span>
|
||||
<span class="n">explanatory_variables</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">,</span>
|
||||
<span class="n">target_variable</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="p">,</span>
|
||||
<span class="n">neighbors</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="p">)</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">GranularWMVFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div></div>
|
||||
|
||||
</pre></div>
|
||||
|
||||
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|
||||
<div class="body" role="main">
|
||||
|
||||
<h1>Source code for pyFTS.models.multivariate.mvfts</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">fts</span><span class="p">,</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">Membership</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="k">import</span> <span class="n">Grid</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.models.multivariate</span> <span class="k">import</span> <span class="n">FLR</span> <span class="k">as</span> <span class="n">MVFLR</span><span class="p">,</span> <span class="n">common</span><span class="p">,</span> <span class="n">flrg</span> <span class="k">as</span> <span class="n">mvflrg</span>
|
||||
<span class="kn">from</span> <span class="nn">itertools</span> <span class="k">import</span> <span class="n">product</span>
|
||||
<span class="kn">from</span> <span class="nn">types</span> <span class="k">import</span> <span class="n">LambdaType</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">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="product_dict"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.product_dict">[docs]</a><span class="k">def</span> <span class="nf">product_dict</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Code by Seth Johnson</span>
|
||||
<span class="sd"> :param kwargs:</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">keys</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
|
||||
<span class="n">vals</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">values</span><span class="p">()</span>
|
||||
<span class="k">for</span> <span class="n">instance</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">vals</span><span class="p">):</span>
|
||||
<span class="k">yield</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">keys</span><span class="p">,</span> <span class="n">instance</span><span class="p">))</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MVFTS"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS">[docs]</a><span class="k">class</span> <span class="nc">MVFTS</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Multivariate extension of Chen's ConventionalFTS method</span>
|
||||
<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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">MVFTS</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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</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">'explanatory_variables'</span><span class="p">,[])</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">target_variable</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">'target_variable'</span><span class="p">,</span><span class="kc">None</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">"MVFTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"Multivariate FTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
|
||||
<div class="viewcode-block" id="MVFTS.append_transformation"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.append_transformation">[docs]</a> <span class="k">def</span> <span class="nf">append_transformation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transformation</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">transformation</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">'The transformation is not multivariate'</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">transformation</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">transformations_param</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="MVFTS.append_variable"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.append_variable">[docs]</a> <span class="k">def</span> <span class="nf">append_variable</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">var</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Append a new endogenous variable to the model</span>
|
||||
|
||||
<span class="sd"> :param var: variable object</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">var</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="MVFTS.format_data"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.format_data">[docs]</a> <span class="k">def</span> <span class="nf">format_data</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="n">ndata</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">:</span>
|
||||
<span class="n">ndata</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="o">=</span> <span class="n">var</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">extractor</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">])</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ndata</span></div>
|
||||
|
||||
<div class="viewcode-block" id="MVFTS.apply_transformations"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.apply_transformations">[docs]</a> <span class="k">def</span> <span class="nf">apply_transformations</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="n">params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">updateUoD</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">transformation</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">):</span>
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="n">transformation</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">transformations_param</span><span class="p">[</span><span class="n">ct</span><span class="p">])</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">:</span>
|
||||
<span class="k">try</span><span class="p">:</span>
|
||||
<span class="n">values</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="c1">#if isinstance(ndata, pd.DataFrame) else ndata[var.data_label]</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="ow">and</span> <span class="n">var</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s1">'common'</span><span class="p">:</span>
|
||||
<span class="n">ndata</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">values</span><span class="p">,</span>
|
||||
<span class="n">var</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">min</span><span class="p">,</span> <span class="n">var</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">max</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ndata</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">var</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">values</span><span class="p">)</span>
|
||||
<span class="k">except</span><span class="p">:</span>
|
||||
<span class="k">pass</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ndata</span></div>
|
||||
|
||||
<div class="viewcode-block" id="MVFTS.generate_lhs_flrs"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.generate_lhs_flrs">[docs]</a> <span class="k">def</span> <span class="nf">generate_lhs_flrs</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="n">flrs</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">lags</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="k">for</span> <span class="n">vc</span><span class="p">,</span> <span class="n">var</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">):</span>
|
||||
<span class="n">data_point</span> <span class="o">=</span> <span class="n">data</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">lags</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="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">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="n">tuples</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product_dict</span><span class="p">(</span><span class="o">**</span><span class="n">lags</span><span class="p">):</span>
|
||||
<span class="n">flr</span> <span class="o">=</span> <span class="n">MVFLR</span><span class="o">.</span><span class="n">FLR</span><span class="p">()</span>
|
||||
|
||||
<span class="n">flr</span><span class="o">.</span><span class="n">LHS</span> <span class="o">=</span> <span class="n">path</span>
|
||||
|
||||
<span class="c1">#for var, fset in path.items():</span>
|
||||
<span class="c1"># flr.set_lhs(var, fset)</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">):</span>
|
||||
<span class="n">flrs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">flr</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">flrs</span></div>
|
||||
|
||||
<div class="viewcode-block" id="MVFTS.generate_flrs"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.generate_flrs">[docs]</a> <span class="k">def</span> <span class="nf">generate_flrs</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="n">flrs</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">ct</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="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">)):</span>
|
||||
<span class="n">ix</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">ct</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="n">data_point</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">format_data</span><span class="p">(</span> <span class="n">data</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span> <span class="p">)</span>
|
||||
|
||||
<span class="n">tmp_flrs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrs</span><span class="p">(</span><span class="n">data_point</span><span class="p">)</span>
|
||||
|
||||
<span class="n">target_ix</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">ct</span><span class="p">]</span>
|
||||
<span class="n">target_point</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">][</span><span class="n">target_ix</span><span class="p">]</span>
|
||||
<span class="n">target</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">fuzzyfy_instance</span><span class="p">(</span><span class="n">target_point</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">tmp_flrs</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">v</span><span class="p">,</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">target</span><span class="p">:</span>
|
||||
<span class="n">new_flr</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">flr</span><span class="p">)</span>
|
||||
<span class="n">new_flr</span><span class="o">.</span><span class="n">set_rhs</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
|
||||
<span class="n">flrs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">new_flr</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">flrs</span></div>
|
||||
|
||||
<div class="viewcode-block" id="MVFTS.generate_flrg"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.generate_flrg">[docs]</a> <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">):</span>
|
||||
<span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
|
||||
<span class="n">flrg</span> <span class="o">=</span> <span class="n">mvflrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">(</span><span class="n">lhs</span><span class="o">=</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</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="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span> <span class="o">=</span> <span class="n">flrg</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="MVFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.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>
|
||||
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
|
||||
<span class="n">flrs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_flrs</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">generate_flrg</span><span class="p">(</span><span class="n">flrs</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="MVFTS.forecast"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.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>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
<span class="n">c</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">ndata</span><span class="o">.</span><span class="n">iterrows</span><span class="p">()</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="k">else</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ndata</span><span class="p">):</span>
|
||||
<span class="n">data_point</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">format_data</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
|
||||
<span class="n">flrs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrs</span><span class="p">(</span><span class="n">data_point</span><span class="p">)</span>
|
||||
<span class="n">mvs</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">mps</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
|
||||
<span class="n">flrg</span> <span class="o">=</span> <span class="n">mvflrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">(</span><span class="n">lhs</span><span class="o">=</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</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="c1">#Naïve approach is applied when no rules were found</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">name</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">:</span>
|
||||
<span class="n">fs</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>
|
||||
<span class="n">fset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">fs</span><span class="p">]</span>
|
||||
<span class="n">mp</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">centroid</span>
|
||||
<span class="n">mv</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">data_point</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">name</span><span class="p">])</span>
|
||||
<span class="n">mvs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
|
||||
<span class="n">mps</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mp</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">mvs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mf">0.</span><span class="p">)</span>
|
||||
<span class="n">mps</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mf">0.</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">_flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
|
||||
<span class="n">mvs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_flrg</span><span class="o">.</span><span class="n">get_membership</span><span class="p">(</span><span class="n">data_point</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">))</span>
|
||||
<span class="n">mps</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_flrg</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">))</span>
|
||||
|
||||
<span class="n">mv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">mvs</span><span class="p">)</span>
|
||||
<span class="n">mp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">mps</span><span class="p">)</span>
|
||||
|
||||
<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">dot</span><span class="p">(</span><span class="n">mv</span><span class="p">,</span><span class="n">mp</span><span class="o">.</span><span class="n">T</span><span class="p">)</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">mv</span><span class="p">))</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">apply_inverse_transformations</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span>
|
||||
<span class="n">params</span><span class="o">=</span><span class="n">data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="MVFTS.forecast_ahead"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.forecast_ahead">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead</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="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="n">generators</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">'generators'</span><span class="p">,</span><span class="kc">None</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">generators</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">'You must provide parameter </span><span class="se">\'</span><span class="s1">generators</span><span class="se">\'</span><span class="s1">! generators is a dict where the keys'</span> <span class="o">+</span>
|
||||
<span class="s1">' are the dataframe column names (except the target_variable) and the values are '</span> <span class="o">+</span>
|
||||
<span class="s1">'lambda functions that accept one value (the actual value of the variable) '</span>
|
||||
<span class="s1">' and return the next value or trained FTS models that accept the actual values and '</span>
|
||||
<span class="s1">'forecast new ones.'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
|
||||
<span class="n">start</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">'start_at'</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">start</span><span class="p">:</span> <span class="n">start</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]</span>
|
||||
<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="mi">0</span><span class="p">,</span> <span class="n">steps</span><span class="p">):</span>
|
||||
<span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:]</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
|
||||
|
||||
<span class="n">new_data_point</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">data_label</span> <span class="ow">in</span> <span class="n">generators</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
|
||||
<span class="k">if</span> <span class="n">data_label</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">],</span> <span class="n">LambdaType</span><span class="p">):</span>
|
||||
<span class="n">last_data_point</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">ndata</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span>
|
||||
<span class="n">new_data_point</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">](</span><span class="n">last_data_point</span><span class="p">[</span><span class="n">data_label</span><span class="p">])</span>
|
||||
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">],</span> <span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
|
||||
<span class="n">gen_model</span> <span class="o">=</span> <span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span>
|
||||
<span class="n">last_data_point</span> <span class="o">=</span> <span class="n">sample</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="n">gen_model</span><span class="o">.</span><span class="n">order</span><span class="p">:]</span>
|
||||
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">gen_model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
|
||||
<span class="n">last_data_point</span> <span class="o">=</span> <span class="n">last_data_point</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
|
||||
|
||||
<span class="n">new_data_point</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">gen_model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">last_data_point</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
|
||||
|
||||
<span class="n">new_data_point</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span>
|
||||
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">new_data_point</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div>
|
||||
|
||||
<div class="viewcode-block" id="MVFTS.forecast_interval"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.forecast_interval">[docs]</a> <span class="k">def</span> <span class="nf">forecast_interval</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>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
<span class="n">c</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">ndata</span><span class="o">.</span><span class="n">iterrows</span><span class="p">()</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="k">else</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ndata</span><span class="p">):</span>
|
||||
<span class="n">data_point</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">format_data</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
|
||||
<span class="n">flrs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrs</span><span class="p">(</span><span class="n">data_point</span><span class="p">)</span>
|
||||
<span class="n">mvs</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">ups</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">los</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
|
||||
<span class="n">flrg</span> <span class="o">=</span> <span class="n">mvflrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">(</span><span class="n">lhs</span><span class="o">=</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</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="c1">#Naïve approach is applied when no rules were found</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">name</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">:</span>
|
||||
<span class="n">fs</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>
|
||||
<span class="n">fset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">fs</span><span class="p">]</span>
|
||||
<span class="n">up</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">upper</span>
|
||||
<span class="n">lo</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">lower</span>
|
||||
<span class="n">mv</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">data_point</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">name</span><span class="p">])</span>
|
||||
<span class="n">mvs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
|
||||
<span class="n">ups</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">up</span><span class="p">)</span>
|
||||
<span class="n">los</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">lo</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">mvs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mf">0.</span><span class="p">)</span>
|
||||
<span class="n">ups</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mf">0.</span><span class="p">)</span>
|
||||
<span class="n">los</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mf">0.</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">_flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
|
||||
<span class="n">mvs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_flrg</span><span class="o">.</span><span class="n">get_membership</span><span class="p">(</span><span class="n">data_point</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">))</span>
|
||||
<span class="n">ups</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_flrg</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">))</span>
|
||||
<span class="n">los</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_flrg</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">))</span>
|
||||
|
||||
<span class="n">mv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">mvs</span><span class="p">)</span>
|
||||
<span class="n">up</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">mv</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">ups</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
|
||||
<span class="n">lo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">mv</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">los</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">lo</span><span class="p">,</span> <span class="n">up</span><span class="p">])</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">apply_inverse_transformations</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span>
|
||||
<span class="n">params</span><span class="o">=</span><span class="n">data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="MVFTS.forecast_ahead_interval"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.forecast_ahead_interval">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead_interval</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="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="n">generators</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">'generators'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">generators</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">'You must provide parameter </span><span class="se">\'</span><span class="s1">generators</span><span class="se">\'</span><span class="s1">! generators is a dict where the keys'</span> <span class="o">+</span>
|
||||
<span class="s1">' are the dataframe column names (except the target_variable) and the values are '</span> <span class="o">+</span>
|
||||
<span class="s1">'lambda functions that accept one value (the actual value of the variable) '</span>
|
||||
<span class="s1">' and return the next value or trained FTS models that accept the actual values and '</span>
|
||||
<span class="s1">'forecast new ones.'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
|
||||
<span class="n">start</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">'start_at'</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">ix</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">start</span><span class="p">:</span> <span class="n">start</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]</span>
|
||||
<span class="n">lo</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span> <span class="c1">#[ndata.loc[k] for k in ix]</span>
|
||||
<span class="n">up</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span> <span class="c1">#[ndata.loc[k] for k in ix]</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="mi">0</span><span class="p">,</span> <span class="n">steps</span><span class="p">):</span>
|
||||
<span class="n">tmp_lo</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">(</span><span class="n">lo</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
|
||||
<span class="n">tmp_up</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">(</span><span class="n">up</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="nb">min</span><span class="p">(</span><span class="n">tmp_lo</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">tmp_up</span><span class="p">)])</span>
|
||||
|
||||
<span class="n">new_data_point_lo</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="n">new_data_point_up</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">data_label</span> <span class="ow">in</span> <span class="n">generators</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
|
||||
<span class="k">if</span> <span class="n">data_label</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">],</span> <span class="n">LambdaType</span><span class="p">):</span>
|
||||
<span class="n">last_data_point_lo</span> <span class="o">=</span> <span class="n">lo</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">lo</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span>
|
||||
<span class="n">new_data_point_lo</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">](</span><span class="n">last_data_point_lo</span><span class="p">[</span><span class="n">data_label</span><span class="p">])</span>
|
||||
<span class="n">last_data_point_up</span> <span class="o">=</span> <span class="n">up</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">up</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span>
|
||||
<span class="n">new_data_point_up</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">](</span><span class="n">last_data_point_up</span><span class="p">[</span><span class="n">data_label</span><span class="p">])</span>
|
||||
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">],</span> <span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span>
|
||||
<span class="n">last_data_point_lo</span> <span class="o">=</span> <span class="n">lo</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">lo</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="o">-</span><span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">:]]</span>
|
||||
<span class="n">last_data_point_up</span> <span class="o">=</span> <span class="n">up</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">up</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="o">-</span><span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">:]]</span>
|
||||
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
|
||||
<span class="n">last_data_point_lo</span> <span class="o">=</span> <span class="n">last_data_point_lo</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
|
||||
<span class="n">last_data_point_up</span> <span class="o">=</span> <span class="n">last_data_point_up</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
|
||||
|
||||
<span class="n">new_data_point_lo</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">last_data_point_lo</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
|
||||
<span class="n">new_data_point_up</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">last_data_point_up</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
|
||||
|
||||
<span class="n">new_data_point_lo</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">tmp_lo</span><span class="p">)</span>
|
||||
<span class="n">new_data_point_up</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">tmp_up</span><span class="p">)</span>
|
||||
|
||||
<span class="n">lo</span> <span class="o">=</span> <span class="n">lo</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">new_data_point_lo</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">up</span> <span class="o">=</span> <span class="n">up</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">new_data_point_up</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div>
|
||||
|
||||
<div class="viewcode-block" id="MVFTS.clone_parameters"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.clone_parameters">[docs]</a> <span class="k">def</span> <span class="nf">clone_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">MVFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">clone_parameters</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">explanatory_variables</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">target_variable</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="n">_str</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">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
|
||||
<span class="n">_str</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">k</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">_str</span></div>
|
||||
|
||||
|
||||
</pre></div>
|
||||
|
||||
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<h1>Source code for pyFTS.models.multivariate.variable</h1><div class="highlight"><pre>
|
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<span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">fts</span><span class="p">,</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">Membership</span><span class="p">,</span> <span class="n">tree</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="k">import</span> <span class="n">Grid</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.models.multivariate</span> <span class="k">import</span> <span class="n">FLR</span> <span class="k">as</span> <span class="n">MVFLR</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="Variable"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.variable.Variable">[docs]</a><span class="k">class</span> <span class="nc">Variable</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> A variable of a fuzzy time series multivariate model. Each variable contains its own</span>
|
||||
<span class="sd"> transformations and partitioners.</span>
|
||||
<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">name</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
|
||||
<span class="sd"> :param name:</span>
|
||||
<span class="sd"> :param \**kwargs: See below</span>
|
||||
|
||||
<span class="sd"> :Keyword Arguments:</span>
|
||||
<span class="sd"> * *alias* -- Alternative name for the variable</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">name</span>
|
||||
<span class="sd">"""A string with the name of the variable"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">alias</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">'alias'</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
|
||||
<span class="sd">"""A string with the alias of the variable"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">data_label</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">'data_label'</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
|
||||
<span class="sd">"""A string with the column name on DataFrame"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">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">'common'</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">data_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">'data_type'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
<span class="sd">"""The type of the data column on Pandas Dataframe"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">mask</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">'mask'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
<span class="sd">"""The mask for format the data column on Pandas Dataframe"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">transformation</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">'transformation'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
<span class="sd">"""Pre processing transformation for the variable"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">transformation_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">'transformation_params'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="sd">"""UoD partitioner for the variable data"""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">alpha_cut</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">'alpha_cut'</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)</span>
|
||||
<span class="sd">"""Minimal membership value to be considered on fuzzyfication process"""</span>
|
||||
|
||||
|
||||
<span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'data'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="Variable.build"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.variable.Variable.build">[docs]</a> <span class="k">def</span> <span class="nf">build</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>
|
||||
<span class="sd">"""</span>
|
||||
|
||||
<span class="sd"> :param kwargs:</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">fs</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'partitioner'</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">mf</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'func'</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="n">np</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'npart'</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
|
||||
<span class="n">data</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">'data'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
<span class="n">kw</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'partitioner_specific'</span><span class="p">,</span> <span class="p">{})</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="n">fs</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="bp">self</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="n">np</span><span class="p">,</span> <span class="n">func</span><span class="o">=</span><span class="n">mf</span><span class="p">,</span>
|
||||
<span class="n">transformation</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">transformation</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alias</span><span class="p">,</span>
|
||||
<span class="n">variable</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">name</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="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">name</span></div>
|
||||
|
||||
<div class="viewcode-block" id="Variable.apply_transformations"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.variable.Variable.apply_transformations">[docs]</a> <span class="k">def</span> <span class="nf">apply_transformations</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>
|
||||
|
||||
<span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'params'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">transformation_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">'params'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</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="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformation</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformation_params</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">data</span></div>
|
||||
|
||||
<div class="viewcode-block" id="Variable.apply_inverse_transformations"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.variable.Variable.apply_inverse_transformations">[docs]</a> <span class="k">def</span> <span class="nf">apply_inverse_transformations</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>
|
||||
|
||||
<span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'params'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">transformation_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">'params'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</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="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformation</span><span class="o">.</span><span class="n">inverse</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformation_params</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">data</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="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span></div>
|
||||
</pre></div>
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<h1>Source code for pyFTS.models.multivariate.wmvfts</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">fts</span><span class="p">,</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">Membership</span><span class="p">,</span> <span class="n">tree</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="k">import</span> <span class="n">Grid</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.models.multivariate</span> <span class="k">import</span> <span class="n">mvfts</span><span class="p">,</span> <span class="n">FLR</span> <span class="k">as</span> <span class="n">MVFLR</span><span class="p">,</span> <span class="n">common</span><span class="p">,</span> <span class="n">flrg</span> <span class="k">as</span> <span class="n">mvflrg</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>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="WeightedFLRG"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedFLRG">[docs]</a><span class="k">class</span> <span class="nc">WeightedFLRG</span><span class="p">(</span><span class="n">mvflrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Weighted Multivariate Fuzzy Logical Rule Group</span>
|
||||
<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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">WeightedFLRG</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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">order</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">'order'</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">LHS</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">'lhs'</span><span class="p">,</span> <span class="p">{})</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">=</span> <span class="mf">0.0</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
|
||||
<div class="viewcode-block" id="WeightedFLRG.append_rhs"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedFLRG.append_rhs">[docs]</a> <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">fset</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="n">count</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">'count'</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">fset</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">fset</span><span class="p">]</span> <span class="o">=</span> <span class="n">count</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">fset</span><span class="p">]</span> <span class="o">+=</span> <span class="n">count</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">+=</span> <span class="n">count</span></div>
|
||||
|
||||
<div class="viewcode-block" id="WeightedFLRG.weights"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedFLRG.weights">[docs]</a> <span class="k">def</span> <span class="nf">weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span></div>
|
||||
|
||||
<div class="viewcode-block" id="WeightedFLRG.get_midpoint"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedFLRG.get_midpoint">[docs]</a> <span class="k">def</span> <span class="nf">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">midpoint</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">mp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">sets</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">midpoint</span> <span class="o">=</span> <span class="n">mp</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">())</span>
|
||||
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">midpoint</span></div>
|
||||
|
||||
<div class="viewcode-block" id="WeightedFLRG.get_lower"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedFLRG.get_lower">[docs]</a> <span class="k">def</span> <span class="nf">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">lw</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="o">=</span> <span class="n">lw</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">())</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span></div>
|
||||
|
||||
<div class="viewcode-block" id="WeightedFLRG.get_upper"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedFLRG.get_upper">[docs]</a> <span class="k">def</span> <span class="nf">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">up</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">upper</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="o">=</span> <span class="n">up</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">())</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</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="n">_str</span> <span class="o">=</span> <span class="s2">""</span>
|
||||
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
|
||||
<span class="n">_str</span> <span class="o">+=</span> <span class="s2">", "</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">_str</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="s2">""</span>
|
||||
<span class="n">_str</span> <span class="o">+=</span> <span class="n">k</span> <span class="o">+</span> <span class="s2">" ("</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="o">+</span> <span class="s2">")"</span>
|
||||
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="o">+</span> <span class="s2">" -> "</span> <span class="o">+</span> <span class="n">_str</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="WeightedMVFTS"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedMVFTS">[docs]</a><span class="k">class</span> <span class="nc">WeightedMVFTS</span><span class="p">(</span><span class="n">mvfts</span><span class="o">.</span><span class="n">MVFTS</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Weighted Multivariate FTS</span>
|
||||
<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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">WeightedMVFTS</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">order</span><span class="o">=</span><span class="mi">1</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">shortname</span> <span class="o">=</span> <span class="s2">"WeightedMVFTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"Weighted Multivariate FTS"</span>
|
||||
|
||||
<div class="viewcode-block" id="WeightedMVFTS.generate_flrg"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedMVFTS.generate_flrg">[docs]</a> <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">):</span>
|
||||
<span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
|
||||
<span class="n">flrg</span> <span class="o">=</span> <span class="n">WeightedFLRG</span><span class="p">(</span><span class="n">lhs</span><span class="o">=</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</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="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span> <span class="o">=</span> <span class="n">flrg</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div></div>
|
||||
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<li class="nav-item nav-item-this"><a href="">pyFTS.models.nonstationary.cvfts</a></li>
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<h1>Source code for pyFTS.models.nonstationary.cvfts</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 class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">hofts</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.models.nonstationary</span> <span class="k">import</span> <span class="n">common</span><span class="p">,</span><span class="n">nsfts</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">flrg</span><span class="p">,</span> <span class="n">tree</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="HighOrderNonstationaryFLRG"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.HighOrderNonstationaryFLRG">[docs]</a><span class="k">class</span> <span class="nc">HighOrderNonstationaryFLRG</span><span class="p">(</span><span class="n">hofts</span><span class="o">.</span><span class="n">HighOrderFTS</span><span class="p">):</span>
|
||||
<span class="sd">"""Conventional High Order Fuzzy Logical Relationship Group"""</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">order</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">HighOrderNonstationaryFLRG</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">order</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">LHS</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">strlhs</span> <span class="o">=</span> <span class="s2">""</span>
|
||||
|
||||
<div class="viewcode-block" id="HighOrderNonstationaryFLRG.append_rhs"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.HighOrderNonstationaryFLRG.append_rhs">[docs]</a> <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</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="k">if</span> <span class="n">c</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">c</span></div>
|
||||
|
||||
<div class="viewcode-block" id="HighOrderNonstationaryFLRG.append_lhs"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.HighOrderNonstationaryFLRG.append_lhs">[docs]</a> <span class="k">def</span> <span class="nf">append_lhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">c</span><span class="p">)</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="n">tmp</span> <span class="o">=</span> <span class="s2">""</span>
|
||||
<span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</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="s2">","</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span> <span class="o">+</span> <span class="n">c</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="o">+</span> <span class="s2">" -> "</span> <span class="o">+</span> <span class="n">tmp</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="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ConditionalVarianceFTS"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS">[docs]</a><span class="k">class</span> <span class="nc">ConditionalVarianceFTS</span><span class="p">(</span><span class="n">hofts</span><span class="o">.</span><span class="n">HighOrderFTS</span><span class="p">):</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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">ConditionalVarianceFTS</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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"Conditional Variance FTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">"CVFTS "</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">append_transformation</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">transformation</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">min_stack</span> <span class="o">=</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="mi">0</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_stack</span> <span class="o">=</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="mi">0</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">inputs</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">residuals</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">variance_residual</span> <span class="o">=</span> <span class="mf">0.</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">mean_residual</span> <span class="o">=</span> <span class="mf">0.</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">memory_window</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">"memory_window"</span><span class="p">,</span><span class="mi">5</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="p">{}</span>
|
||||
|
||||
<div class="viewcode-block" id="ConditionalVarianceFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS.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">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
|
||||
<span class="n">tmpdata</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">fuzzySeries</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">sets</span><span class="p">,</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_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">const_t</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">flrs</span> <span class="o">=</span> <span class="n">FLR</span><span class="o">.</span><span class="n">generate_non_recurrent_flrs</span><span class="p">(</span><span class="n">tmpdata</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg</span><span class="p">(</span><span class="n">flrs</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">no_update</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">residuals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="mi">1</span><span class="p">:])</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">variance_residual</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">)</span> <span class="c1"># np.max(self.residuals</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">mean_residual</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">residuals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">memory_window</span><span class="p">:]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">memory_window</span><span class="p">:]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">inputs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">memory_window</span><span class="p">:])</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ConditionalVarianceFTS.generate_flrg"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS.generate_flrg">[docs]</a> <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">name</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="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">name</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">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">nsfts</span><span class="o">.</span><span class="n">ConventionalNonStationaryFLRG</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">name</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">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">name</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_smooth</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">a</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="o">.</span><span class="mi">1</span> <span class="o">*</span> <span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="o">.</span><span class="mi">3</span> <span class="o">*</span> <span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="o">.</span><span class="mi">6</span> <span class="o">*</span> <span class="n">a</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
|
||||
|
||||
<div class="viewcode-block" id="ConditionalVarianceFTS.perturbation_factors"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS.perturbation_factors">[docs]</a> <span class="k">def</span> <span class="nf">perturbation_factors</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>
|
||||
<span class="n">npart</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
|
||||
<span class="n">_max</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="n">_min</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="k">if</span> <span class="n">data</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span><span class="p">:</span>
|
||||
<span class="n">_min</span> <span class="o">=</span> <span class="n">data</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="k">if</span> <span class="n">data</span> <span class="o"><</span> <span class="mi">0</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="o">-</span> <span class="n">data</span>
|
||||
<span class="k">elif</span> <span class="n">data</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span><span class="p">:</span>
|
||||
<span class="n">_max</span> <span class="o">=</span> <span class="n">data</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="k">if</span> <span class="n">data</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="o">-</span> <span class="n">data</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">min_stack</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">min_stack</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">_min</span><span class="p">)</span>
|
||||
<span class="n">_min</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">min_stack</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_stack</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_stack</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">_max</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="bp">self</span><span class="o">.</span><span class="n">max_stack</span><span class="p">)</span>
|
||||
|
||||
<span class="n">_range</span> <span class="o">=</span> <span class="p">(</span><span class="n">_max</span> <span class="o">-</span> <span class="n">_min</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span>
|
||||
|
||||
<span class="n">translate</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="n">_min</span><span class="p">,</span> <span class="n">_max</span><span class="p">,</span> <span class="n">npart</span><span class="p">)</span>
|
||||
|
||||
<span class="n">var</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">)</span>
|
||||
|
||||
<span class="n">var</span> <span class="o">=</span> <span class="mi">0</span> <span class="k">if</span> <span class="n">var</span> <span class="o"><</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">var</span>
|
||||
|
||||
<span class="n">loc</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mean_residual</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">))</span>
|
||||
|
||||
<span class="n">location</span> <span class="o">=</span> <span class="p">[</span><span class="n">_range</span> <span class="o">+</span> <span class="n">w</span> <span class="o">+</span> <span class="n">loc</span> <span class="o">+</span> <span class="n">k</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">linspace</span><span class="p">(</span><span class="o">-</span><span class="n">var</span><span class="p">,</span><span class="n">var</span><span class="p">,</span> <span class="n">npart</span><span class="p">)</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">translate</span><span class="p">]</span>
|
||||
|
||||
<span class="n">scale</span> <span class="o">=</span> <span class="p">[</span><span class="nb">abs</span><span class="p">(</span><span class="n">location</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">location</span><span class="p">[</span><span class="mi">2</span><span class="p">])]</span>
|
||||
<span class="n">scale</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="nb">abs</span><span class="p">(</span><span class="n">location</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">location</span><span class="p">[</span><span class="n">k</span> <span class="o">+</span> <span class="mi">1</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="mi">1</span><span class="p">,</span> <span class="n">npart</span><span class="p">)])</span>
|
||||
<span class="n">scale</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">location</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">location</span><span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">]))</span>
|
||||
|
||||
<span class="n">perturb</span> <span class="o">=</span> <span class="p">[[</span><span class="n">location</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">scale</span><span class="p">[</span><span class="n">k</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">npart</span><span class="p">)]</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">perturb</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ConditionalVarianceFTS.perturbation_factors__old"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS.perturbation_factors__old">[docs]</a> <span class="k">def</span> <span class="nf">perturbation_factors__old</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="n">npart</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
|
||||
<span class="n">_max</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="n">_min</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="k">if</span> <span class="n">data</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span><span class="p">:</span>
|
||||
<span class="n">_min</span> <span class="o">=</span> <span class="n">data</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="k">if</span> <span class="n">data</span> <span class="o"><</span> <span class="mi">0</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="o">-</span> <span class="n">data</span>
|
||||
<span class="k">elif</span> <span class="n">data</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span><span class="p">:</span>
|
||||
<span class="n">_max</span> <span class="o">=</span> <span class="n">data</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="k">if</span> <span class="n">data</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="o">-</span> <span class="n">data</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">min_stack</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">min_stack</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">_min</span><span class="p">)</span>
|
||||
<span class="n">_min</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">min_stack</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_stack</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_stack</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">_max</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="bp">self</span><span class="o">.</span><span class="n">max_stack</span><span class="p">)</span>
|
||||
|
||||
<span class="n">location</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="n">_min</span><span class="p">,</span> <span class="n">_max</span><span class="p">,</span> <span class="n">npart</span><span class="p">)</span>
|
||||
<span class="n">scale</span> <span class="o">=</span> <span class="p">[</span><span class="nb">abs</span><span class="p">(</span><span class="n">location</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">location</span><span class="p">[</span><span class="mi">2</span><span class="p">])]</span>
|
||||
<span class="n">scale</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="nb">abs</span><span class="p">(</span><span class="n">location</span><span class="p">[</span><span class="n">k</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">location</span><span class="p">[</span><span class="n">k</span><span class="o">+</span><span class="mi">1</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="mi">1</span><span class="p">,</span> <span class="n">npart</span><span class="p">)])</span>
|
||||
<span class="n">scale</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">location</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">location</span><span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">]))</span>
|
||||
|
||||
<span class="n">perturb</span> <span class="o">=</span> <span class="p">[[</span><span class="n">location</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">scale</span><span class="p">[</span><span class="n">k</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="mi">0</span><span class="p">,</span> <span class="n">npart</span><span class="p">)]</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">perturb</span></div>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_fsset_key</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ix</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_affected_sets</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="n">perturb</span><span class="p">):</span>
|
||||
|
||||
<span class="n">affected_sets</span> <span class="o">=</span> <span class="p">[[</span><span class="n">ct</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_fsset_key</span><span class="p">(</span><span class="n">ct</span><span class="p">)]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">perturb</span><span class="p">[</span><span class="n">ct</span><span class="p">])]</span>
|
||||
<span class="k">for</span> <span class="n">ct</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="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">))</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_fsset_key</span><span class="p">(</span><span class="n">ct</span><span class="p">)]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">perturb</span><span class="p">[</span><span class="n">ct</span><span class="p">])</span> <span class="o">></span> <span class="mf">0.0</span><span class="p">]</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">affected_sets</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">sample</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">lower_set</span><span class="p">()</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
|
||||
<span class="n">affected_sets</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
|
||||
<span class="k">elif</span> <span class="n">sample</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">upper_set</span><span class="p">()</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
|
||||
<span class="n">affected_sets</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">affected_sets</span>
|
||||
|
||||
<div class="viewcode-block" id="ConditionalVarianceFTS.forecast"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS.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">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">no_update</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">"no_update"</span><span class="p">,</span><span class="kc">False</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="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
|
||||
|
||||
<span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
|
||||
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">no_update</span><span class="p">:</span>
|
||||
<span class="n">perturb</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">perturbation_factors</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">perturb</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</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="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">))]</span>
|
||||
|
||||
<span class="n">affected_sets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_affected_sets</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">perturb</span><span class="p">)</span>
|
||||
|
||||
<span class="n">numerator</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">denominator</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">affected_sets</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">ix</span> <span class="o">=</span> <span class="n">affected_sets</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">aset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
|
||||
<span class="k">if</span> <span class="n">aset</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">numerator</span><span class="o">.</span><span class="n">append</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">aset</span><span class="p">]</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">perturb</span><span class="p">[</span><span class="n">ix</span><span class="p">]))</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">fuzzy_set</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">aset</span><span class="p">]</span>
|
||||
<span class="n">numerator</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fuzzy_set</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="n">ix</span><span class="p">]))</span>
|
||||
<span class="n">denominator</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">aset</span> <span class="ow">in</span> <span class="n">affected_sets</span><span class="p">:</span>
|
||||
<span class="n">ix</span> <span class="o">=</span> <span class="n">aset</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
|
||||
<span class="n">fs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
|
||||
<span class="n">tdisp</span> <span class="o">=</span> <span class="n">perturb</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
|
||||
<span class="k">if</span> <span class="n">fs</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">numerator</span><span class="o">.</span><span class="n">append</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">fs</span><span class="p">]</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">tdisp</span><span class="p">)</span> <span class="o">*</span> <span class="n">aset</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">fuzzy_set</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">fs</span><span class="p">]</span>
|
||||
<span class="n">numerator</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fuzzy_set</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="n">tdisp</span><span class="p">)</span> <span class="o">*</span> <span class="n">aset</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="n">denominator</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">aset</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">sum</span><span class="p">(</span><span class="n">denominator</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">pto</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">numerator</span><span class="p">)</span> <span class="o">/</span><span class="nb">sum</span><span class="p">(</span><span class="n">denominator</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">pto</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">numerator</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pto</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">no_update</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pto</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ConditionalVarianceFTS.forecast_interval"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.ConditionalVarianceFTS.forecast_interval">[docs]</a> <span class="k">def</span> <span class="nf">forecast_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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">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="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
|
||||
|
||||
<span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
|
||||
|
||||
<span class="n">perturb</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">perturbation_factors</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
||||
|
||||
<span class="n">affected_sets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_affected_sets</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">perturb</span><span class="p">)</span>
|
||||
|
||||
<span class="n">upper</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">lower</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">affected_sets</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">ix</span> <span class="o">=</span> <span class="n">affected_sets</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">aset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
|
||||
<span class="k">if</span> <span class="n">aset</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">lower</span><span class="o">.</span><span class="n">append</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">aset</span><span class="p">]</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="n">ix</span><span class="p">]))</span>
|
||||
<span class="n">upper</span><span class="o">.</span><span class="n">append</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">aset</span><span class="p">]</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="n">ix</span><span class="p">]))</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">fuzzy_set</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">aset</span><span class="p">]</span>
|
||||
<span class="n">lower</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fuzzy_set</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="n">ix</span><span class="p">]))</span>
|
||||
<span class="n">upper</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fuzzy_set</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="n">ix</span><span class="p">]))</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">aset</span> <span class="ow">in</span> <span class="n">affected_sets</span><span class="p">:</span>
|
||||
<span class="n">ix</span> <span class="o">=</span> <span class="n">aset</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
|
||||
<span class="n">fs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
|
||||
<span class="n">tdisp</span> <span class="o">=</span> <span class="n">perturb</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
|
||||
<span class="k">if</span> <span class="n">fs</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">lower</span><span class="o">.</span><span class="n">append</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">fs</span><span class="p">]</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">tdisp</span><span class="p">)</span> <span class="o">*</span> <span class="n">aset</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="n">upper</span><span class="o">.</span><span class="n">append</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">fs</span><span class="p">]</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">tdisp</span><span class="p">)</span> <span class="o">*</span> <span class="n">aset</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">fuzzy_set</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">fs</span><span class="p">]</span>
|
||||
<span class="n">lower</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fuzzy_set</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">tdisp</span><span class="p">)</span> <span class="o">*</span> <span class="n">aset</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="n">upper</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fuzzy_set</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">tdisp</span><span class="p">)</span> <span class="o">*</span> <span class="n">aset</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
|
||||
|
||||
<span class="n">itvl</span> <span class="o">=</span> <span class="p">[</span><span class="nb">sum</span><span class="p">(</span><span class="n">lower</span><span class="p">),</span> <span class="nb">sum</span><span class="p">(</span><span class="n">upper</span><span class="p">)]</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">itvl</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div></div>
|
||||
</pre></div>
|
||||
|
||||
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<h1>Source code for pyFTS.models.nonstationary.honsfts</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 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="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">hofts</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.models.nonstationary</span> <span class="k">import</span> <span class="n">common</span><span class="p">,</span> <span class="n">flrg</span><span class="p">,</span> <span class="n">nsfts</span>
|
||||
<span class="kn">from</span> <span class="nn">itertools</span> <span class="k">import</span> <span class="n">product</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="HighOrderNonStationaryFLRG"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG">[docs]</a><span class="k">class</span> <span class="nc">HighOrderNonStationaryFLRG</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">NonStationaryFLRG</span><span class="p">):</span>
|
||||
<span class="sd">"""First Order NonStationary Fuzzy Logical Relationship Group"""</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">order</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">HighOrderNonStationaryFLRG</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">order</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">LHS</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">=</span> <span class="mf">0.0</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">strlhs</span> <span class="o">=</span> <span class="s2">""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
|
||||
<div class="viewcode-block" id="HighOrderNonStationaryFLRG.append_rhs"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.append_rhs">[docs]</a> <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">fset</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="n">count</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">'count'</span><span class="p">,</span><span class="mf">1.0</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">fset</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">fset</span><span class="p">]</span> <span class="o">=</span> <span class="n">count</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">fset</span><span class="p">]</span> <span class="o">+=</span> <span class="n">count</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">+=</span> <span class="n">count</span></div>
|
||||
|
||||
<div class="viewcode-block" id="HighOrderNonStationaryFLRG.append_lhs"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.append_lhs">[docs]</a> <span class="k">def</span> <span class="nf">append_lhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">c</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="HighOrderNonStationaryFLRG.weights"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.weights">[docs]</a> <span class="k">def</span> <span class="nf">weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span></div>
|
||||
|
||||
<div class="viewcode-block" id="HighOrderNonStationaryFLRG.get_midpoint"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.get_midpoint">[docs]</a> <span class="k">def</span> <span class="nf">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">,</span> <span class="n">perturb</span><span class="p">):</span>
|
||||
<span class="n">mp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">sets</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="n">perturb</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
|
||||
<span class="n">midpoint</span> <span class="o">=</span> <span class="n">mp</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">())</span>
|
||||
<span class="k">return</span> <span class="n">midpoint</span></div>
|
||||
|
||||
<div class="viewcode-block" id="HighOrderNonStationaryFLRG.get_lower"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.get_lower">[docs]</a> <span class="k">def</span> <span class="nf">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">,</span> <span class="n">perturb</span><span class="p">):</span>
|
||||
<span class="n">lw</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">perturb</span><span class="p">)</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
|
||||
<span class="n">lower</span> <span class="o">=</span> <span class="n">lw</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">())</span>
|
||||
<span class="k">return</span> <span class="n">lower</span></div>
|
||||
|
||||
<div class="viewcode-block" id="HighOrderNonStationaryFLRG.get_upper"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG.get_upper">[docs]</a> <span class="k">def</span> <span class="nf">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">,</span> <span class="n">perturb</span><span class="p">):</span>
|
||||
<span class="n">up</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">perturb</span><span class="p">)</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
|
||||
<span class="n">upper</span> <span class="o">=</span> <span class="n">up</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">())</span>
|
||||
<span class="k">return</span> <span class="n">upper</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="n">_str</span> <span class="o">=</span> <span class="s2">""</span>
|
||||
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
|
||||
<span class="n">_str</span> <span class="o">+=</span> <span class="s2">", "</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">_str</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="s2">""</span>
|
||||
<span class="n">_str</span> <span class="o">+=</span> <span class="n">k</span> <span class="o">+</span> <span class="s2">" ("</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="o">+</span> <span class="s2">")"</span>
|
||||
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="o">+</span> <span class="s2">" -> "</span> <span class="o">+</span> <span class="n">_str</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="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="HighOrderNonStationaryFTS"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS">[docs]</a><span class="k">class</span> <span class="nc">HighOrderNonStationaryFTS</span><span class="p">(</span><span class="n">nsfts</span><span class="o">.</span><span class="n">NonStationaryFTS</span><span class="p">):</span>
|
||||
<span class="sd">"""NonStationaryFTS Fuzzy Time Series"""</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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">HighOrderNonStationaryFTS</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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"High Order Non Stationary FTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">"HONSFTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">order</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">"order"</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">configure_lags</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="HighOrderNonStationaryFTS.configure_lags"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS.configure_lags">[docs]</a> <span class="k">def</span> <span class="nf">configure_lags</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>
|
||||
<span class="k">if</span> <span class="s2">"order"</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">order</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">"order"</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="s2">"lags"</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">lags</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">"lags"</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lags</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lags</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">max_lag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">lags</span> <span class="o">=</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="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="HighOrderNonStationaryFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS.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>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">'conditional'</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">no_update</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">residuals</span> <span class="o">=</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="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:])</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">variance_residual</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">)</span> <span class="c1"># np.max(self.residuals</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">mean_residual</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">residuals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">memory_window</span><span class="p">:]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">memory_window</span><span class="p">:]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">inputs</span> <span class="o">=</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="bp">self</span><span class="o">.</span><span class="n">memory_window</span><span class="p">:])</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span></div>
|
||||
|
||||
<div class="viewcode-block" id="HighOrderNonStationaryFTS.generate_flrg"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS.generate_flrg">[docs]</a> <span class="k">def</span> <span class="nf">generate_flrg</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>
|
||||
<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>
|
||||
<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="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dump</span><span class="p">:</span> <span class="nb">print</span><span class="p">(</span><span class="s2">"FLR: "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">k</span><span class="p">))</span>
|
||||
|
||||
<span class="n">sample</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">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
|
||||
|
||||
<span class="n">rhs</span> <span class="o">=</span> <span class="p">[</span><span class="n">key</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">])</span> <span class="o">></span> <span class="mf">0.0</span><span class="p">]</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">rhs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">rhs</span> <span class="o">=</span> <span class="p">[</span><span class="n">common</span><span class="o">.</span><span class="n">check_bounds</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">])</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>
|
||||
|
||||
<span class="n">lags</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">o</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">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
|
||||
<span class="n">tdisp</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="n">lhs</span> <span class="o">=</span> <span class="p">[</span><span class="n">key</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">sample</span><span class="p">[</span><span class="n">o</span><span class="p">],</span> <span class="n">tdisp</span><span class="p">)</span> <span class="o">></span> <span class="mf">0.0</span><span class="p">]</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">lhs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">lhs</span> <span class="o">=</span> <span class="p">[</span><span class="n">common</span><span class="o">.</span><span class="n">check_bounds</span><span class="p">(</span><span class="n">sample</span><span class="p">[</span><span class="n">o</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="p">,</span> <span class="n">tdisp</span><span class="p">)</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>
|
||||
|
||||
<span class="n">lags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">lhs</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># Trace the possible paths</span>
|
||||
<span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">lags</span><span class="p">):</span>
|
||||
<span class="n">flrg</span> <span class="o">=</span> <span class="n">HighOrderNonStationaryFLRG</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">c</span><span class="p">,</span> <span class="n">e</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">path</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">flrg</span><span class="o">.</span><span class="n">append_lhs</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</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="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span> <span class="o">=</span> <span class="n">flrg</span><span class="p">;</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">st</span> <span class="ow">in</span> <span class="n">rhs</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">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">st</span><span class="p">)</span></div>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_affected_flrgs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="n">perturb</span><span class="p">):</span>
|
||||
|
||||
<span class="n">affected_flrgs</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">affected_flrgs_memberships</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">lags</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">dat</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sample</span><span class="p">):</span>
|
||||
<span class="n">affected_sets</span> <span class="o">=</span> <span class="p">[</span><span class="n">key</span> <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">dat</span><span class="p">,</span> <span class="n">perturb</span><span class="p">[</span><span class="n">ct</span><span class="p">])</span> <span class="o">></span> <span class="mf">0.0</span><span class="p">]</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">affected_sets</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">dat</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">lower_set</span><span class="p">()</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
|
||||
<span class="n">affected_sets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">lower_set</span><span class="p">()</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">dat</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">upper_set</span><span class="p">()</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
|
||||
<span class="n">affected_sets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">upper_set</span><span class="p">()</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
|
||||
|
||||
<span class="n">lags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">affected_sets</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># Build the tree with all possible paths</span>
|
||||
|
||||
<span class="c1"># Trace the possible paths</span>
|
||||
<span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">lags</span><span class="p">):</span>
|
||||
|
||||
<span class="n">flrg</span> <span class="o">=</span> <span class="n">HighOrderNonStationaryFLRG</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">path</span><span class="p">:</span>
|
||||
<span class="n">flrg</span><span class="o">.</span><span class="n">append_lhs</span><span class="p">(</span><span class="n">kk</span><span class="p">)</span>
|
||||
|
||||
<span class="n">affected_flrgs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span>
|
||||
<span class="n">mv</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">dat</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sample</span><span class="p">):</span>
|
||||
<span class="n">fset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="n">ct</span><span class="p">]]</span>
|
||||
<span class="n">ix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="n">ct</span><span class="p">])</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">dat</span><span class="p">,</span> <span class="n">perturb</span><span class="p">[</span><span class="n">ix</span><span class="p">])</span>
|
||||
|
||||
<span class="n">mv</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
|
||||
|
||||
<span class="n">affected_flrgs_memberships</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">prod</span><span class="p">(</span><span class="n">mv</span><span class="p">))</span>
|
||||
|
||||
<span class="k">return</span> <span class="p">[</span><span class="n">affected_flrgs</span><span class="p">,</span> <span class="n">affected_flrgs_memberships</span><span class="p">]</span>
|
||||
|
||||
<div class="viewcode-block" id="HighOrderNonStationaryFTS.forecast"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS.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">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
|
||||
<span class="n">explain</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">'explain'</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
|
||||
|
||||
<span class="n">time_displacement</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">"time_displacement"</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
|
||||
|
||||
<span class="n">window_size</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">"window_size"</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="n">no_update</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">"no_update"</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</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="k">if</span> <span class="ow">not</span> <span class="n">explain</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">+</span> <span class="mi">1</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">l</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">ndata</span>
|
||||
<span class="k">elif</span> <span class="n">l</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span>
|
||||
<span class="n">l</span> <span class="o">+=</span> <span class="mi">1</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="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
|
||||
|
||||
<span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</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">max_lag</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">method</span> <span class="o">==</span> <span class="s1">'unconditional'</span><span class="p">:</span>
|
||||
<span class="n">perturb</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">window_index</span><span class="p">(</span><span class="n">k</span> <span class="o">+</span> <span class="n">time_displacement</span><span class="p">,</span> <span class="n">window_size</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">'conditional'</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">no_update</span><span class="p">:</span>
|
||||
<span class="n">perturb</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</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="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">partitions</span><span class="p">)]</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">perturb</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conditional_perturbation_factors</span><span class="p">(</span><span class="n">sample</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
|
||||
|
||||
<span class="n">affected_flrgs</span><span class="p">,</span> <span class="n">affected_flrgs_memberships</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_affected_flrgs</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">perturb</span><span class="p">)</span>
|
||||
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">perturb2</span> <span class="o">=</span> <span class="n">perturb</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">affected_flrgs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">common</span><span class="o">.</span><span class="n">check_bounds</span><span class="p">(</span><span class="n">sample</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">perturb2</span><span class="p">))</span>
|
||||
<span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">affected_flrgs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">flrg</span> <span class="o">=</span> <span class="n">affected_flrgs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
|
||||
<span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</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">append</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">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">perturb2</span><span class="p">))</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">fset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span>
|
||||
<span class="n">ix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fset</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="n">ix</span><span class="p">]))</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">aset</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">affected_flrgs</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">aset</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</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">append</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">aset</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">perturb2</span><span class="p">)</span> <span class="o">*</span>
|
||||
<span class="n">affected_flrgs_memberships</span><span class="p">[</span><span class="n">ct</span><span class="p">])</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">fset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">aset</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span>
|
||||
<span class="n">ix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">aset</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fset</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="n">ix</span><span class="p">])</span><span class="o">*</span><span class="n">affected_flrgs_memberships</span><span class="p">[</span><span class="n">ct</span><span class="p">])</span>
|
||||
<span class="n">pto</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pto</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">'conditional'</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">no_update</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pto</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">g</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</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="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="s2">"</span><span class="si">{0}{1}</span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</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="k">return</span> <span class="n">tmp</span></div>
|
||||
</pre></div>
|
||||
|
||||
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|
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<h1>Source code for pyFTS.models.nonstationary.nsfts</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 class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">fts</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.models.nonstationary</span> <span class="k">import</span> <span class="n">common</span><span class="p">,</span> <span class="n">flrg</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ConventionalNonStationaryFLRG"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.ConventionalNonStationaryFLRG">[docs]</a><span class="k">class</span> <span class="nc">ConventionalNonStationaryFLRG</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">NonStationaryFLRG</span><span class="p">):</span>
|
||||
<span class="sd">"""First Order NonStationary Fuzzy Logical Relationship Group"""</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">LHS</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">ConventionalNonStationaryFLRG</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="mi">1</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">LHS</span> <span class="o">=</span> <span class="n">LHS</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
|
||||
|
||||
<div class="viewcode-block" id="ConventionalNonStationaryFLRG.get_key"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.ConventionalNonStationaryFLRG.get_key">[docs]</a> <span class="k">def</span> <span class="nf">get_key</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ConventionalNonStationaryFLRG.append_rhs"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.ConventionalNonStationaryFLRG.append_rhs">[docs]</a> <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</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="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">c</span><span class="p">)</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="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span> <span class="o">+</span> <span class="s2">" -> "</span>
|
||||
<span class="n">tmp2</span> <span class="o">=</span> <span class="s2">""</span>
|
||||
<span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">tmp2</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="s2">","</span>
|
||||
<span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="n">c</span>
|
||||
<span class="k">return</span> <span class="n">tmp</span> <span class="o">+</span> <span class="n">tmp2</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="WeightedNonStationaryFLRG"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG">[docs]</a><span class="k">class</span> <span class="nc">WeightedNonStationaryFLRG</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">NonStationaryFLRG</span><span class="p">):</span>
|
||||
<span class="sd">"""First Order NonStationary Fuzzy Logical Relationship Group"""</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">LHS</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">WeightedNonStationaryFLRG</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="mi">1</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">LHS</span> <span class="o">=</span> <span class="n">LHS</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">=</span> <span class="mf">0.0</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">strlhs</span> <span class="o">=</span> <span class="s2">""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
|
||||
<div class="viewcode-block" id="WeightedNonStationaryFLRG.get_key"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.get_key">[docs]</a> <span class="k">def</span> <span class="nf">get_key</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span></div>
|
||||
|
||||
<div class="viewcode-block" id="WeightedNonStationaryFLRG.append_rhs"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.append_rhs">[docs]</a> <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</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="k">if</span> <span class="n">c</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span></div>
|
||||
|
||||
<div class="viewcode-block" id="WeightedNonStationaryFLRG.weights"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.weights">[docs]</a> <span class="k">def</span> <span class="nf">weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span></div>
|
||||
|
||||
<div class="viewcode-block" id="WeightedNonStationaryFLRG.get_midpoint"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFLRG.get_midpoint">[docs]</a> <span class="k">def</span> <span class="nf">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">,</span> <span class="n">perturb</span><span class="p">):</span>
|
||||
<span class="n">mp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">sets</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="n">perturb</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
|
||||
<span class="n">midpoint</span> <span class="o">=</span> <span class="n">mp</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">())</span>
|
||||
<span class="k">return</span> <span class="n">midpoint</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="n">_str</span> <span class="o">=</span> <span class="s2">""</span>
|
||||
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
|
||||
<span class="n">_str</span> <span class="o">+=</span> <span class="s2">", "</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">_str</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="s2">""</span>
|
||||
<span class="n">_str</span> <span class="o">+=</span> <span class="n">k</span> <span class="o">+</span> <span class="s2">" ("</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="o">+</span> <span class="s2">")"</span>
|
||||
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="o">+</span> <span class="s2">" -> "</span> <span class="o">+</span> <span class="n">_str</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="NonStationaryFTS"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.NonStationaryFTS">[docs]</a><span class="k">class</span> <span class="nc">NonStationaryFTS</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
|
||||
<span class="sd">"""NonStationaryFTS Fuzzy Time Series"""</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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">NonStationaryFTS</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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"Non Stationary FTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">"NSFTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">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">'method'</span><span class="p">,</span><span class="s1">'conditional'</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">append_transformation</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">transformation</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">'conditional'</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">min_stack</span> <span class="o">=</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="mi">0</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_stack</span> <span class="o">=</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="mi">0</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">inputs</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">residuals</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">variance_residual</span> <span class="o">=</span> <span class="mf">0.</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">mean_residual</span> <span class="o">=</span> <span class="mf">0.</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">memory_window</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">"memory_window"</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="NonStationaryFTS.generate_flrg"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.NonStationaryFTS.generate_flrg">[docs]</a> <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">name</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="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">name</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">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">ConventionalNonStationaryFLRG</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">name</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">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">name</span><span class="p">)</span></div>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_smooth</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">a</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="o">.</span><span class="mi">1</span> <span class="o">*</span> <span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="o">.</span><span class="mi">3</span> <span class="o">*</span> <span class="n">a</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="o">.</span><span class="mi">6</span> <span class="o">*</span> <span class="n">a</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
|
||||
|
||||
<div class="viewcode-block" id="NonStationaryFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.NonStationaryFTS.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>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">'unconditional'</span><span class="p">:</span>
|
||||
<span class="n">window_size</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">'parameters'</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">tmpdata</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">fuzzySeries</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">,</span>
|
||||
<span class="n">window_size</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="k">else</span><span class="p">:</span>
|
||||
<span class="n">tmpdata</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">fuzzySeries</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_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">const_t</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
|
||||
<span class="n">flrs</span> <span class="o">=</span> <span class="n">FLR</span><span class="o">.</span><span class="n">generate_non_recurrent_flrs</span><span class="p">(</span><span class="n">tmpdata</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg</span><span class="p">(</span><span class="n">flrs</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">'conditional'</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">no_update</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">residuals</span> <span class="o">=</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="mi">1</span><span class="p">:])</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">variance_residual</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">)</span> <span class="c1"># np.max(self.residuals</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">mean_residual</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">residuals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">memory_window</span><span class="p">:]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">memory_window</span><span class="p">:]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">inputs</span> <span class="o">=</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="bp">self</span><span class="o">.</span><span class="n">memory_window</span><span class="p">:])</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span></div>
|
||||
|
||||
<div class="viewcode-block" id="NonStationaryFTS.conditional_perturbation_factors"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.NonStationaryFTS.conditional_perturbation_factors">[docs]</a> <span class="k">def</span> <span class="nf">conditional_perturbation_factors</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>
|
||||
<span class="n">npart</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
|
||||
<span class="n">_max</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="n">_min</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="k">if</span> <span class="n">data</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span><span class="p">:</span>
|
||||
<span class="n">_min</span> <span class="o">=</span> <span class="n">data</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="k">if</span> <span class="n">data</span> <span class="o"><</span> <span class="mi">0</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="o">-</span> <span class="n">data</span>
|
||||
<span class="k">elif</span> <span class="n">data</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span><span class="p">:</span>
|
||||
<span class="n">_max</span> <span class="o">=</span> <span class="n">data</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="k">if</span> <span class="n">data</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="o">-</span> <span class="n">data</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">min_stack</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">min_stack</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">_min</span><span class="p">)</span>
|
||||
<span class="n">_min</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">min_stack</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_stack</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_stack</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">_max</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="bp">self</span><span class="o">.</span><span class="n">max_stack</span><span class="p">)</span>
|
||||
|
||||
<span class="n">_range</span> <span class="o">=</span> <span class="p">(</span><span class="n">_max</span> <span class="o">-</span> <span class="n">_min</span><span class="p">)</span><span class="o">/</span><span class="mi">2</span>
|
||||
|
||||
<span class="n">translate</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="n">_min</span><span class="p">,</span> <span class="n">_max</span><span class="p">,</span> <span class="n">npart</span><span class="p">)</span>
|
||||
|
||||
<span class="n">var</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">)</span>
|
||||
|
||||
<span class="n">var</span> <span class="o">=</span> <span class="mi">0</span> <span class="k">if</span> <span class="n">var</span> <span class="o"><</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">var</span>
|
||||
|
||||
<span class="n">loc</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mean_residual</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">))</span>
|
||||
|
||||
<span class="n">location</span> <span class="o">=</span> <span class="p">[</span><span class="n">_range</span> <span class="o">+</span> <span class="n">w</span> <span class="o">+</span> <span class="n">loc</span> <span class="o">+</span> <span class="n">k</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">linspace</span><span class="p">(</span><span class="o">-</span><span class="n">var</span><span class="p">,</span><span class="n">var</span><span class="p">,</span> <span class="n">npart</span><span class="p">)</span> <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">translate</span><span class="p">]</span>
|
||||
|
||||
<span class="n">scale</span> <span class="o">=</span> <span class="p">[</span><span class="nb">abs</span><span class="p">(</span><span class="n">location</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">location</span><span class="p">[</span><span class="mi">2</span><span class="p">])]</span>
|
||||
<span class="n">scale</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="nb">abs</span><span class="p">(</span><span class="n">location</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">location</span><span class="p">[</span><span class="n">k</span> <span class="o">+</span> <span class="mi">1</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="mi">1</span><span class="p">,</span> <span class="n">npart</span><span class="p">)])</span>
|
||||
<span class="n">scale</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">location</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">location</span><span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">]))</span>
|
||||
|
||||
<span class="n">perturb</span> <span class="o">=</span> <span class="p">[[</span><span class="n">location</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">scale</span><span class="p">[</span><span class="n">k</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">npart</span><span class="p">)]</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">perturb</span></div>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_affected_sets</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="n">perturb</span><span class="p">):</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">'conditional'</span><span class="p">:</span>
|
||||
|
||||
<span class="n">affected_sets</span> <span class="o">=</span> <span class="p">[[</span><span class="n">ct</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">perturb</span><span class="p">[</span><span class="n">ct</span><span class="p">])]</span>
|
||||
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">perturb</span><span class="p">[</span><span class="n">ct</span><span class="p">])</span> <span class="o">></span> <span class="mf">0.0</span><span class="p">]</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">affected_sets</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">sample</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">lower_set</span><span class="p">()</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
|
||||
<span class="n">affected_sets</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
|
||||
<span class="k">elif</span> <span class="n">sample</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">upper_set</span><span class="p">()</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
|
||||
<span class="n">affected_sets</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
|
||||
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">affected_sets</span> <span class="o">=</span> <span class="p">[[</span><span class="n">ct</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">perturb</span><span class="p">)]</span>
|
||||
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">perturb</span><span class="p">)</span> <span class="o">></span> <span class="mf">0.0</span><span class="p">]</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">affected_sets</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">sample</span> <span class="o"><</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">lower_set</span><span class="p">()</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">perturb</span><span class="p">):</span>
|
||||
<span class="n">affected_sets</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
|
||||
<span class="k">elif</span> <span class="n">sample</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">upper_set</span><span class="p">()</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">perturb</span><span class="p">):</span>
|
||||
<span class="n">affected_sets</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">affected_sets</span>
|
||||
|
||||
<div class="viewcode-block" id="NonStationaryFTS.forecast"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.NonStationaryFTS.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">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
|
||||
<span class="n">time_displacement</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">"time_displacement"</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
|
||||
|
||||
<span class="n">window_size</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">"window_size"</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="n">no_update</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">"no_update"</span><span class="p">,</span> <span class="kc">False</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">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="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
|
||||
|
||||
<span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</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">method</span> <span class="o">==</span> <span class="s1">'unconditional'</span><span class="p">:</span>
|
||||
<span class="n">perturb</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">window_index</span><span class="p">(</span><span class="n">k</span> <span class="o">+</span> <span class="n">time_displacement</span><span class="p">,</span> <span class="n">window_size</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">'conditional'</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">no_update</span><span class="p">:</span>
|
||||
<span class="n">perturb</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conditional_perturbation_factors</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">perturb</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</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="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">))]</span>
|
||||
|
||||
<span class="n">affected_sets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_affected_sets</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">perturb</span><span class="p">)</span>
|
||||
|
||||
<span class="n">numerator</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">denominator</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">affected_sets</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">ix</span> <span class="o">=</span> <span class="n">affected_sets</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">aset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
|
||||
<span class="k">if</span> <span class="n">aset</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">numerator</span><span class="o">.</span><span class="n">append</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">aset</span><span class="p">]</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">perturb</span><span class="p">[</span><span class="n">ix</span><span class="p">]))</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">fuzzy_set</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">aset</span><span class="p">]</span>
|
||||
<span class="n">numerator</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fuzzy_set</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="n">ix</span><span class="p">]))</span>
|
||||
<span class="n">denominator</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">aset</span> <span class="ow">in</span> <span class="n">affected_sets</span><span class="p">:</span>
|
||||
<span class="n">ix</span> <span class="o">=</span> <span class="n">aset</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
|
||||
<span class="n">fs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
|
||||
<span class="n">tdisp</span> <span class="o">=</span> <span class="n">perturb</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
|
||||
<span class="k">if</span> <span class="n">fs</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">numerator</span><span class="o">.</span><span class="n">append</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">fs</span><span class="p">]</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">tdisp</span><span class="p">)</span> <span class="o">*</span> <span class="n">aset</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">fuzzy_set</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">fs</span><span class="p">]</span>
|
||||
<span class="n">numerator</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fuzzy_set</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="n">tdisp</span><span class="p">)</span> <span class="o">*</span> <span class="n">aset</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="n">denominator</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">aset</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">sum</span><span class="p">(</span><span class="n">denominator</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">pto</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">numerator</span><span class="p">)</span> <span class="o">/</span> <span class="nb">sum</span><span class="p">(</span><span class="n">denominator</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">pto</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">numerator</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pto</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">'conditional'</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">no_update</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pto</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">inputs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="NonStationaryFTS.forecast_interval"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.NonStationaryFTS.forecast_interval">[docs]</a> <span class="k">def</span> <span class="nf">forecast_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
|
||||
<span class="n">time_displacement</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">"time_displacement"</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
|
||||
|
||||
<span class="n">window_size</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">"window_size"</span><span class="p">,</span> <span class="mi">1</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">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="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
|
||||
|
||||
<span class="c1"># print("input: " + str(ndata[k]))</span>
|
||||
|
||||
<span class="n">tdisp</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">window_index</span><span class="p">(</span><span class="n">k</span> <span class="o">+</span> <span class="n">time_displacement</span><span class="p">,</span> <span class="n">window_size</span><span class="p">)</span>
|
||||
|
||||
<span class="n">affected_sets</span> <span class="o">=</span> <span class="p">[[</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">tdisp</span><span class="p">)]</span>
|
||||
<span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">tdisp</span><span class="p">)</span> <span class="o">></span> <span class="mf">0.0</span><span class="p">]</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">affected_sets</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">affected_sets</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">common</span><span class="o">.</span><span class="n">check_bounds</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="p">,</span> <span class="n">tdisp</span><span class="p">),</span> <span class="mf">1.0</span><span class="p">])</span>
|
||||
|
||||
<span class="n">upper</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">lower</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">affected_sets</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">aset</span> <span class="o">=</span> <span class="n">affected_sets</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="k">if</span> <span class="n">aset</span><span class="o">.</span><span class="n">name</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">lower</span><span class="o">.</span><span class="n">append</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">aset</span><span class="o">.</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">tdisp</span><span class="p">))</span>
|
||||
<span class="n">upper</span><span class="o">.</span><span class="n">append</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">aset</span><span class="o">.</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">tdisp</span><span class="p">))</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">lower</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">aset</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">tdisp</span><span class="p">))</span>
|
||||
<span class="n">upper</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">aset</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">tdisp</span><span class="p">))</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">aset</span> <span class="ow">in</span> <span class="n">affected_sets</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">aset</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">name</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">lower</span><span class="o">.</span><span class="n">append</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">aset</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">tdisp</span><span class="p">)</span> <span class="o">*</span> <span class="n">aset</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="n">upper</span><span class="o">.</span><span class="n">append</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">aset</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">tdisp</span><span class="p">)</span> <span class="o">*</span> <span class="n">aset</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">lower</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">aset</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">tdisp</span><span class="p">)</span> <span class="o">*</span> <span class="n">aset</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="n">upper</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">aset</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">tdisp</span><span class="p">)</span> <span class="o">*</span> <span class="n">aset</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
|
||||
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="nb">sum</span><span class="p">(</span><span class="n">lower</span><span class="p">),</span> <span class="nb">sum</span><span class="p">(</span><span class="n">upper</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="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="s2">"</span><span class="si">{0}{1}</span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</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="k">return</span> <span class="n">tmp</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="WeightedNonStationaryFTS"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFTS">[docs]</a><span class="k">class</span> <span class="nc">WeightedNonStationaryFTS</span><span class="p">(</span><span class="n">NonStationaryFTS</span><span class="p">):</span>
|
||||
<span class="sd">"""Weighted NonStationaryFTS Fuzzy Time Series"""</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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">WeightedNonStationaryFTS</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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"Weighted Non Stationary FTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">"WNSFTS"</span>
|
||||
|
||||
<div class="viewcode-block" id="WeightedNonStationaryFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFTS.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>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">'unconditional'</span><span class="p">:</span>
|
||||
<span class="n">window_size</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">'parameters'</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
||||
<span class="n">tmpdata</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">fuzzySeries</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">,</span>
|
||||
<span class="n">window_size</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="k">else</span><span class="p">:</span>
|
||||
<span class="n">tmpdata</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">fuzzySeries</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_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">const_t</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
|
||||
|
||||
<span class="n">flrs</span> <span class="o">=</span> <span class="n">FLR</span><span class="o">.</span><span class="n">generate_recurrent_flrs</span><span class="p">(</span><span class="n">tmpdata</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg</span><span class="p">(</span><span class="n">flrs</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">'conditional'</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">no_update</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">residuals</span> <span class="o">=</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="mi">1</span><span class="p">:])</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">variance_residual</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">)</span> <span class="c1"># np.max(self.residuals</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">mean_residual</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">residuals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">residuals</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">memory_window</span><span class="p">:]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecasts</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">memory_window</span><span class="p">:]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">inputs</span> <span class="o">=</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="bp">self</span><span class="o">.</span><span class="n">memory_window</span><span class="p">:])</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span></div>
|
||||
|
||||
<div class="viewcode-block" id="WeightedNonStationaryFTS.generate_flrg"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.WeightedNonStationaryFTS.generate_flrg">[docs]</a> <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">name</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="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">name</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">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">WeightedNonStationaryFLRG</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">name</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">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">name</span><span class="p">)</span></div></div>
|
||||
</pre></div>
|
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<h1>Source code for pyFTS.models.nonstationary.util</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 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">matplotlib</span> <span class="k">as</span> <span class="nn">plt</span>
|
||||
<span class="kn">import</span> <span class="nn">matplotlib.colors</span> <span class="k">as</span> <span class="nn">pltcolors</span>
|
||||
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">Membership</span><span class="p">,</span> <span class="n">Util</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="plot_sets"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.util.plot_sets">[docs]</a><span class="k">def</span> <span class="nf">plot_sets</span><span class="p">(</span><span class="n">partitioner</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">end</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">tam</span><span class="o">=</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="n">colors</span><span class="o">=</span><span class="kc">None</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">axes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">window_size</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">only_lines</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">legend</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
|
||||
<span class="nb">range</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">start</span><span class="p">,</span><span class="n">end</span><span class="p">,</span><span class="n">step</span><span class="p">)</span>
|
||||
<span class="n">ticks</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">if</span> <span class="n">axes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">tam</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">):</span>
|
||||
<span class="n">fset</span> <span class="o">=</span> <span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">only_lines</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">:</span>
|
||||
<span class="n">tdisp</span> <span class="o">=</span> <span class="n">t</span> <span class="o">-</span> <span class="p">(</span><span class="n">t</span> <span class="o">%</span> <span class="n">window_size</span><span class="p">)</span>
|
||||
<span class="n">fset</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">tdisp</span><span class="p">)</span>
|
||||
<span class="n">param</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">perturbated_parameters</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">tdisp</span><span class="p">)]</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">fset</span><span class="o">.</span><span class="n">mf</span> <span class="o">==</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">t</span> <span class="o">==</span> <span class="n">start</span><span class="p">:</span>
|
||||
<span class="n">line</span> <span class="o">=</span> <span class="n">axes</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">t</span><span class="p">,</span> <span class="n">t</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">t</span><span class="p">],</span> <span class="n">param</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">fset</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
|
||||
<span class="n">fset</span><span class="o">.</span><span class="n">metadata</span><span class="p">[</span><span class="s1">'color'</span><span class="p">]</span> <span class="o">=</span> <span class="n">line</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">get_color</span><span class="p">()</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">axes</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">t</span><span class="p">,</span> <span class="n">t</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">t</span><span class="p">],</span> <span class="n">param</span><span class="p">,</span><span class="n">c</span><span class="o">=</span><span class="n">fset</span><span class="o">.</span><span class="n">metadata</span><span class="p">[</span><span class="s1">'color'</span><span class="p">])</span>
|
||||
|
||||
<span class="n">ticks</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="s2">"t+"</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">t</span><span class="p">),</span><span class="s2">""</span><span class="p">])</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">:</span>
|
||||
<span class="n">tdisp</span> <span class="o">=</span> <span class="n">t</span> <span class="o">-</span> <span class="p">(</span><span class="n">t</span> <span class="o">%</span> <span class="n">window_size</span><span class="p">)</span>
|
||||
<span class="n">fset</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">tdisp</span><span class="p">)</span>
|
||||
<span class="n">param</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">perturbated_parameters</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">tdisp</span><span class="p">)]</span>
|
||||
<span class="n">tmp</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">polyval</span><span class="p">(</span><span class="n">param</span><span class="p">,</span> <span class="n">tdisp</span><span class="p">))</span>
|
||||
<span class="n">axes</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">,</span> <span class="n">tmp</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="s2">"--"</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s2">"blue"</span><span class="p">)</span>
|
||||
|
||||
<span class="n">axes</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"Universe of Discourse"</span><span class="p">)</span>
|
||||
<span class="n">axes</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"Time"</span><span class="p">)</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">([</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">],</span> <span class="n">ticks</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="s1">'vertical'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">legend</span><span class="p">:</span>
|
||||
<span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span> <span class="o">=</span> <span class="n">axes</span><span class="o">.</span><span class="n">get_legend_handles_labels</span><span class="p">()</span>
|
||||
<span class="n">lgd</span> <span class="o">=</span> <span class="n">axes</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">bbox_to_anchor</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="k">if</span> <span class="n">data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">axes</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">start</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="mi">1</span><span class="p">),</span> <span class="n">data</span><span class="p">,</span><span class="n">c</span><span class="o">=</span><span class="s2">"black"</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">file</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
|
||||
<span class="n">Util</span><span class="o">.</span><span class="n">show_and_save_image</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">save</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="plot_sets_conditional"><a class="viewcode-back" href="../../../../pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.util.plot_sets_conditional">[docs]</a><span class="k">def</span> <span class="nf">plot_sets_conditional</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="n">colors</span><span class="o">=</span><span class="kc">None</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">axes</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fig</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="nb">range</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="n">step</span><span class="p">)</span>
|
||||
<span class="n">ticks</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">if</span> <span class="n">axes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">size</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">:</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">([</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">]])</span>
|
||||
<span class="n">perturb</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">conditional_perturbation_factors</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">])</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">):</span>
|
||||
<span class="nb">set</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
|
||||
<span class="nb">set</span><span class="o">.</span><span class="n">perturbate_parameters</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="n">ct</span><span class="p">])</span>
|
||||
<span class="n">param</span> <span class="o">=</span> <span class="nb">set</span><span class="o">.</span><span class="n">perturbated_parameters</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">perturb</span><span class="p">[</span><span class="n">ct</span><span class="p">])]</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">set</span><span class="o">.</span><span class="n">mf</span> <span class="o">==</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">t</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">line</span> <span class="o">=</span> <span class="n">axes</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">t</span><span class="p">,</span> <span class="n">t</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">t</span><span class="p">],</span> <span class="n">param</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="nb">set</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
|
||||
<span class="nb">set</span><span class="o">.</span><span class="n">metadata</span><span class="p">[</span><span class="s1">'color'</span><span class="p">]</span> <span class="o">=</span> <span class="n">line</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">get_color</span><span class="p">()</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">axes</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">t</span><span class="p">,</span> <span class="n">t</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">t</span><span class="p">],</span> <span class="n">param</span><span class="p">,</span><span class="n">c</span><span class="o">=</span><span class="nb">set</span><span class="o">.</span><span class="n">metadata</span><span class="p">[</span><span class="s1">'color'</span><span class="p">])</span>
|
||||
|
||||
<span class="c1">#ticks.extend(["t+"+str(t),""])</span>
|
||||
|
||||
<span class="n">axes</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"Universe of Discourse"</span><span class="p">)</span>
|
||||
<span class="n">axes</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">"Time"</span><span class="p">)</span>
|
||||
<span class="c1">#plt.xticks([k for k in range], ticks, rotation='vertical')</span>
|
||||
|
||||
<span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span> <span class="o">=</span> <span class="n">axes</span><span class="o">.</span><span class="n">get_legend_handles_labels</span><span class="p">()</span>
|
||||
<span class="n">lgd</span> <span class="o">=</span> <span class="n">axes</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">bbox_to_anchor</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="k">if</span> <span class="n">data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">axes</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="mi">1</span><span class="p">),</span> <span class="n">data</span><span class="p">,</span><span class="n">c</span><span class="o">=</span><span class="s2">"black"</span><span class="p">)</span>
|
||||
|
||||
<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
|
||||
|
||||
<span class="n">Util</span><span class="o">.</span><span class="n">show_and_save_image</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">save</span><span class="p">)</span></div>
|
||||
</pre></div>
|
||||
|
||||
<div class="clearer"></div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="sphinxsidebar" role="navigation" aria-label="main navigation">
|
||||
<div class="sphinxsidebarwrapper">
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<div id="searchbox" style="display: none" role="search">
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<h3 id="searchlabel">Quick search</h3>
|
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<div class="searchformwrapper">
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<form class="search" action="../../../../search.html" method="get">
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<h3>Navigation</h3>
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<li class="right" style="margin-right: 10px">
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<a href="../../../../genindex.html" title="General Index"
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>index</a></li>
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<li class="right" >
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>modules</a> |</li>
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<li class="nav-item nav-item-0"><a href="../../../../index.html">pyFTS 1.7 documentation</a> »</li>
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© Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
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793
docs/build/html/_modules/pyFTS/models/pwfts.html
vendored
793
docs/build/html/_modules/pyFTS/models/pwfts.html
vendored
@ -1,793 +0,0 @@
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<link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
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|
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<h1>Source code for pyFTS.models.pwfts</h1><div class="highlight"><pre>
|
||||
<span></span><span class="ch">#!/usr/bin/python</span>
|
||||
<span class="c1"># -*- coding: utf8 -*-</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">math</span>
|
||||
<span class="kn">from</span> <span class="nn">operator</span> <span class="k">import</span> <span class="n">itemgetter</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">FuzzySet</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">hofts</span><span class="p">,</span> <span class="n">ifts</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="k">import</span> <span class="n">ProbabilityDistribution</span>
|
||||
<span class="kn">from</span> <span class="nn">itertools</span> <span class="k">import</span> <span class="n">product</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFLRG"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG">[docs]</a><span class="k">class</span> <span class="nc">ProbabilisticWeightedFLRG</span><span class="p">(</span><span class="n">hofts</span><span class="o">.</span><span class="n">HighOrderFLRG</span><span class="p">):</span>
|
||||
<span class="sd">"""High Order Probabilistic Weighted Fuzzy Logical Relationship Group"""</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">order</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">ProbabilisticWeightedFLRG</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">order</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">frequency_count</span> <span class="o">=</span> <span class="mf">0.0</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">Z</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFLRG.get_membership"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_membership">[docs]</a> <span class="k">def</span> <span class="nf">get_membership</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="n">sets</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">,</span> <span class="nb">set</span><span class="p">)):</span>
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nanprod</span><span class="p">([</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">count</span><span class="p">])</span>
|
||||
<span class="k">for</span> <span class="n">count</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">LHS</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">else</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">sets</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">data</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFLRG.append_rhs"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.append_rhs">[docs]</a> <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</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">count</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">'count'</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">frequency_count</span> <span class="o">+=</span> <span class="n">count</span>
|
||||
<span class="k">if</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">+=</span> <span class="n">count</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">count</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFLRG.lhs_conditional_probability"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.lhs_conditional_probability">[docs]</a> <span class="k">def</span> <span class="nf">lhs_conditional_probability</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="n">sets</span><span class="p">,</span> <span class="n">norm</span><span class="p">,</span> <span class="n">uod</span><span class="p">,</span> <span class="n">nbins</span><span class="p">):</span>
|
||||
<span class="n">pk</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">frequency_count</span> <span class="o">/</span> <span class="n">norm</span>
|
||||
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="n">pk</span> <span class="o">*</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_membership</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">sets</span><span class="p">)</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">partition_function</span><span class="p">(</span><span class="n">sets</span><span class="p">,</span> <span class="n">uod</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="n">nbins</span><span class="p">))</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">tmp</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFLRG.lhs_conditional_probability_fuzzyfied"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.lhs_conditional_probability_fuzzyfied">[docs]</a> <span class="k">def</span> <span class="nf">lhs_conditional_probability_fuzzyfied</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lhs_mv</span><span class="p">,</span> <span class="n">sets</span><span class="p">,</span> <span class="n">norm</span><span class="p">,</span> <span class="n">uod</span><span class="p">,</span> <span class="n">nbins</span><span class="p">):</span>
|
||||
<span class="n">pk</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">frequency_count</span> <span class="o">/</span> <span class="n">norm</span>
|
||||
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="n">pk</span> <span class="o">*</span> <span class="p">(</span><span class="n">lhs_mv</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">partition_function</span><span class="p">(</span><span class="n">sets</span><span class="p">,</span> <span class="n">uod</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="n">nbins</span><span class="p">))</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">tmp</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFLRG.rhs_unconditional_probability"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.rhs_unconditional_probability">[docs]</a> <span class="k">def</span> <span class="nf">rhs_unconditional_probability</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">frequency_count</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFLRG.rhs_conditional_probability"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.rhs_conditional_probability">[docs]</a> <span class="k">def</span> <span class="nf">rhs_conditional_probability</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="n">sets</span><span class="p">,</span> <span class="n">uod</span><span class="p">,</span> <span class="n">nbins</span><span class="p">):</span>
|
||||
<span class="n">total</span> <span class="o">=</span> <span class="mf">0.0</span>
|
||||
<span class="k">for</span> <span class="n">rhs</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
|
||||
<span class="nb">set</span> <span class="o">=</span> <span class="n">sets</span><span class="p">[</span><span class="n">rhs</span><span class="p">]</span>
|
||||
<span class="n">wi</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rhs_unconditional_probability</span><span class="p">(</span><span class="n">rhs</span><span class="p">)</span>
|
||||
<span class="n">mv</span> <span class="o">=</span> <span class="nb">set</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">/</span> <span class="nb">set</span><span class="o">.</span><span class="n">partition_function</span><span class="p">(</span><span class="n">uod</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="n">nbins</span><span class="p">)</span>
|
||||
<span class="n">total</span> <span class="o">+=</span> <span class="n">wi</span> <span class="o">*</span> <span class="n">mv</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">total</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFLRG.partition_function"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.partition_function">[docs]</a> <span class="k">def</span> <span class="nf">partition_function</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">,</span> <span class="n">uod</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">100</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">Z</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">Z</span> <span class="o">=</span> <span class="mf">0.0</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">linspace</span><span class="p">(</span><span class="n">uod</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">uod</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">nbins</span><span class="p">):</span>
|
||||
<span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">Z</span> <span class="o">+=</span> <span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">Z</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFLRG.get_midpoint"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_midpoint">[docs]</a> <span class="k">def</span> <span class="nf">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
|
||||
<span class="sd">'''Return the expectation of the PWFLRG, the weighted sum'''</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">midpoint</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">midpoint</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</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="bp">self</span><span class="o">.</span><span class="n">rhs_unconditional_probability</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">*</span> <span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span>
|
||||
<span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()]))</span>
|
||||
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">midpoint</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFLRG.get_upper"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_upper">[docs]</a> <span class="k">def</span> <span class="nf">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</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="bp">self</span><span class="o">.</span><span class="n">rhs_unconditional_probability</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">*</span> <span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">upper</span>
|
||||
<span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()]))</span>
|
||||
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFLRG.get_lower"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFLRG.get_lower">[docs]</a> <span class="k">def</span> <span class="nf">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</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="bp">self</span><span class="o">.</span><span class="n">rhs_unconditional_probability</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">*</span> <span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span>
|
||||
<span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()]))</span>
|
||||
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</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="n">tmp2</span> <span class="o">=</span> <span class="s2">""</span>
|
||||
<span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">tmp2</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="s2">", "</span>
|
||||
<span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="s2">"("</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">frequency_count</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="o">+</span> <span class="s2">")"</span> <span class="o">+</span> <span class="n">c</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="o">+</span> <span class="s2">" -> "</span> <span class="o">+</span> <span class="n">tmp2</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS">[docs]</a><span class="k">class</span> <span class="nc">ProbabilisticWeightedFTS</span><span class="p">(</span><span class="n">ifts</span><span class="o">.</span><span class="n">IntervalFTS</span><span class="p">):</span>
|
||||
<span class="sd">"""High Order Probabilistic Weighted Fuzzy Time Series"""</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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">ProbabilisticWeightedFTS</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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">"PWFTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"Probabilistic FTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">"Silva, P.; Guimarães, F.; Sadaei, H."</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">global_frequency_count</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">auto_update</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">'update'</span><span class="p">,</span><span class="kc">False</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">configure_lags</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFTS.train"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.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>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">configure_lags</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'fuzzyfied'</span><span class="p">,</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg2</span><span class="p">(</span><span class="n">data</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">generate_flrg_fuzzyfied</span><span class="p">(</span><span class="n">data</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFTS.generate_flrg2"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_flrg2">[docs]</a> <span class="k">def</span> <span class="nf">generate_flrg2</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="n">fuzz</span> <span class="o">=</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">data</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="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
|
||||
<span class="n">fuzz</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'both'</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="bp">self</span><span class="o">.</span><span class="n">alpha_cut</span><span class="p">))</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg_fuzzyfied</span><span class="p">(</span><span class="n">fuzz</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFTS.generate_flrg_fuzzyfied"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_flrg_fuzzyfied">[docs]</a> <span class="k">def</span> <span class="nf">generate_flrg_fuzzyfied</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="n">_tmp_steps</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">standard_horizon</span> <span class="o">-</span> <span class="mi">1</span>
|
||||
<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>
|
||||
<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="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span> <span class="o">-</span> <span class="n">_tmp_steps</span><span class="p">):</span>
|
||||
<span class="n">sample</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">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
|
||||
<span class="n">set_sample</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">instance</span> <span class="ow">in</span> <span class="n">sample</span><span class="p">:</span>
|
||||
<span class="n">set_sample</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">instance</span><span class="p">])</span>
|
||||
|
||||
<span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg_fuzzyfied</span><span class="p">(</span><span class="n">set_sample</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="n">flrgs</span><span class="p">:</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</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="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span> <span class="o">=</span> <span class="n">flrg</span><span class="p">;</span>
|
||||
|
||||
<span class="n">lhs_mv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pwflrg_lhs_memberhip_fuzzyfied</span><span class="p">(</span><span class="n">flrg</span><span class="p">,</span> <span class="n">sample</span><span class="p">)</span>
|
||||
|
||||
<span class="n">mvs</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">rhs</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">_tmp_steps</span><span class="p">]</span>
|
||||
<span class="k">for</span> <span class="nb">set</span><span class="p">,</span> <span class="n">mv</span> <span class="ow">in</span> <span class="n">rhs</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">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="nb">set</span><span class="p">,</span> <span class="n">count</span><span class="o">=</span><span class="n">lhs_mv</span> <span class="o">*</span> <span class="n">mv</span><span class="p">)</span>
|
||||
<span class="n">mvs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
|
||||
|
||||
<span class="n">tmp_fq</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="n">lhs_mv</span> <span class="o">*</span> <span class="n">kk</span> <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">mvs</span> <span class="k">if</span> <span class="n">kk</span> <span class="o">></span> <span class="mi">0</span><span class="p">])</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">global_frequency_count</span> <span class="o">+=</span> <span class="n">tmp_fq</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFTS.pwflrg_lhs_memberhip_fuzzyfied"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.pwflrg_lhs_memberhip_fuzzyfied">[docs]</a> <span class="k">def</span> <span class="nf">pwflrg_lhs_memberhip_fuzzyfied</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">,</span> <span class="n">sample</span><span class="p">):</span>
|
||||
<span class="n">vals</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">ct</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)):</span> <span class="c1"># fuzz in enumerate(sample):</span>
|
||||
<span class="n">vals</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">mv</span> <span class="k">for</span> <span class="n">fset</span><span class="p">,</span> <span class="n">mv</span> <span class="ow">in</span> <span class="n">sample</span><span class="p">[</span><span class="n">ct</span><span class="p">]</span> <span class="k">if</span> <span class="n">fset</span> <span class="o">==</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="n">ct</span><span class="p">]])</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nanprod</span><span class="p">(</span><span class="n">vals</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFTS.generate_lhs_flrg"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_lhs_flrg">[docs]</a> <span class="k">def</span> <span class="nf">generate_lhs_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="n">explain</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
|
||||
<span class="n">sample</span> <span class="o">=</span> <span class="p">[</span><span class="n">sample</span><span class="p">]</span>
|
||||
|
||||
<span class="n">nsample</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">"sets"</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha_cut</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">sample</span><span class="p">]</span>
|
||||
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg_fuzzyfied</span><span class="p">(</span><span class="n">nsample</span><span class="p">,</span> <span class="n">explain</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFTS.generate_lhs_flrg_fuzzyfied"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_lhs_flrg_fuzzyfied">[docs]</a> <span class="k">def</span> <span class="nf">generate_lhs_flrg_fuzzyfied</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="n">explain</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="n">lags</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">flrgs</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">o</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lags</span><span class="p">):</span>
|
||||
<span class="n">lhs</span> <span class="o">=</span> <span class="n">sample</span><span class="p">[</span><span class="n">o</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span>
|
||||
<span class="n">lags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> <span class="n">lhs</span> <span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\t</span><span class="s2"> (Lag </span><span class="si">{}</span><span class="s2">) </span><span class="si">{}</span><span class="s2"> -> </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="n">sample</span><span class="p">[</span><span class="n">o</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">lhs</span><span class="p">))</span>
|
||||
|
||||
<span class="c1"># Trace the possible paths</span>
|
||||
<span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">lags</span><span class="p">):</span>
|
||||
<span class="n">flrg</span> <span class="o">=</span> <span class="n">ProbabilisticWeightedFLRG</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">lhs</span> <span class="ow">in</span> <span class="n">path</span><span class="p">:</span>
|
||||
<span class="n">flrg</span><span class="o">.</span><span class="n">append_lhs</span><span class="p">(</span><span class="n">lhs</span><span class="p">)</span>
|
||||
|
||||
<span class="n">flrgs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">flrgs</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFTS.generate_flrg"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.generate_flrg">[docs]</a> <span class="k">def</span> <span class="nf">generate_flrg</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="n">_tmp_steps</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">standard_horizon</span> <span class="o">-</span> <span class="mi">1</span>
|
||||
<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>
|
||||
<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="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span> <span class="o">-</span> <span class="n">_tmp_steps</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dump</span><span class="p">:</span> <span class="nb">print</span><span class="p">(</span><span class="s2">"FLR: "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">k</span><span class="p">))</span>
|
||||
|
||||
<span class="n">sample</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">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
|
||||
|
||||
<span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="n">flrgs</span><span class="p">:</span>
|
||||
|
||||
<span class="n">lhs_mv</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_membership</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</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="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span> <span class="o">=</span> <span class="n">flrg</span><span class="p">;</span>
|
||||
|
||||
<span class="n">fuzzyfied</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="o">+</span><span class="n">_tmp_steps</span><span class="p">],</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'both'</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="bp">self</span><span class="o">.</span><span class="n">alpha_cut</span><span class="p">)</span>
|
||||
|
||||
<span class="n">mvs</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="nb">set</span><span class="p">,</span> <span class="n">mv</span> <span class="ow">in</span> <span class="n">fuzzyfied</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">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="nb">set</span><span class="p">,</span> <span class="n">count</span><span class="o">=</span><span class="n">lhs_mv</span> <span class="o">*</span> <span class="n">mv</span><span class="p">)</span>
|
||||
<span class="n">mvs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
|
||||
|
||||
<span class="n">tmp_fq</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="n">lhs_mv</span><span class="o">*</span><span class="n">kk</span> <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">mvs</span> <span class="k">if</span> <span class="n">kk</span> <span class="o">></span> <span class="mi">0</span><span class="p">])</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">global_frequency_count</span> <span class="o">+=</span> <span class="n">tmp_fq</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFTS.update_model"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.update_model">[docs]</a> <span class="k">def</span> <span class="nf">update_model</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="k">pass</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFTS.add_new_PWFLGR"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.add_new_PWFLGR">[docs]</a> <span class="k">def</span> <span class="nf">add_new_PWFLGR</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</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">ProbabilisticWeightedFLRG</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">fs</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">:</span> <span class="n">tmp</span><span class="o">.</span><span class="n">append_lhs</span><span class="p">(</span><span class="n">fs</span><span class="p">)</span>
|
||||
<span class="n">tmp</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</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">tmp</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">;</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">global_frequency_count</span> <span class="o">+=</span> <span class="mi">1</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFTS.flrg_lhs_unconditional_probability"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_lhs_unconditional_probability">[docs]</a> <span class="k">def</span> <span class="nf">flrg_lhs_unconditional_probability</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</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="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">frequency_count</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_frequency_count</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="mf">1.0</span></div>
|
||||
<span class="c1">#self.add_new_PWFLGR(flrg)</span>
|
||||
<span class="c1">#return self.flrg_lhs_unconditional_probability(flrg)</span>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFTS.flrg_lhs_conditional_probability"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_lhs_conditional_probability">[docs]</a> <span class="k">def</span> <span class="nf">flrg_lhs_conditional_probability</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="n">flrg</span><span class="p">):</span>
|
||||
<span class="n">mv</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_membership</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">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
|
||||
<span class="n">pb</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrg_lhs_unconditional_probability</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">mv</span> <span class="o">*</span> <span class="n">pb</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFTS.flrg_lhs_conditional_probability_fuzzyfied"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_lhs_conditional_probability_fuzzyfied">[docs]</a> <span class="k">def</span> <span class="nf">flrg_lhs_conditional_probability_fuzzyfied</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="n">flrg</span><span class="p">):</span>
|
||||
<span class="n">mv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pwflrg_lhs_memberhip_fuzzyfied</span><span class="p">(</span><span class="n">flrg</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
|
||||
<span class="n">pb</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrg_lhs_unconditional_probability</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">mv</span> <span class="o">*</span> <span class="n">pb</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFTS.get_midpoint"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_midpoint">[docs]</a> <span class="k">def</span> <span class="nf">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</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="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span> <span class="c1">#sum(np.array([tmp.rhs_unconditional_probability(s) * self.setsDict[s].centroid for s in tmp.RHS]))</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">pi</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</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">pi</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">]))</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFTS.flrg_rhs_conditional_probability"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.flrg_rhs_conditional_probability">[docs]</a> <span class="k">def</span> <span class="nf">flrg_rhs_conditional_probability</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="n">flrg</span><span class="p">):</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</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">_flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
|
||||
<span class="n">cond</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">_flrg</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
|
||||
<span class="n">_set</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="n">_flrg</span><span class="o">.</span><span class="n">rhs_unconditional_probability</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">_set</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">/</span> <span class="n">_set</span><span class="o">.</span><span class="n">partition_function</span><span class="p">(</span><span class="n">uod</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">get_UoD</span><span class="p">()))</span>
|
||||
<span class="n">cond</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</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">cond</span><span class="p">))</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">pi</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</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">pi</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">]))</span>
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFTS.get_upper"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_upper">[docs]</a> <span class="k">def</span> <span class="nf">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</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="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFTS.get_lower"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_lower">[docs]</a> <span class="k">def</span> <span class="nf">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</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="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFTS.forecast"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.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>
|
||||
<span class="n">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">'method'</span><span class="p">,</span><span class="s1">'heuristic'</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">data</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span>
|
||||
|
||||
<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="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
|
||||
<span class="n">sample</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">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">'heuristic'</span><span class="p">:</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">point_heuristic</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">))</span>
|
||||
<span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">'expected_value'</span><span class="p">:</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">point_expected_value</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">))</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Unknown point forecasting method!"</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="ow">and</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="o">+</span><span class="mi">1</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">update_model</span><span class="p">(</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="o">-</span> <span class="mi">1</span> <span class="p">:</span> <span class="n">k</span><span class="p">])</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFTS.point_heuristic"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.point_heuristic">[docs]</a> <span class="k">def</span> <span class="nf">point_heuristic</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
|
||||
<span class="n">explain</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">'explain'</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
|
||||
<span class="n">fuzzyfied</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">'fuzzyfied'</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Fuzzyfication </span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">fuzzyfied</span><span class="p">:</span>
|
||||
<span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">explain</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">fsets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_sets_from_both_fuzzyfication</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
||||
<span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg_fuzzyfied</span><span class="p">(</span><span class="n">fsets</span><span class="p">,</span> <span class="n">explain</span><span class="p">)</span>
|
||||
|
||||
<span class="n">mp</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">norms</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Rules:</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="n">flrgs</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">fuzzyfied</span><span class="p">:</span>
|
||||
<span class="n">norm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrg_lhs_conditional_probability</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">flrg</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">norm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrg_lhs_conditional_probability_fuzzyfied</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">flrg</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">norm</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">norm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrg_lhs_unconditional_probability</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\t</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> </span><span class="se">\t</span><span class="s2"> Midpoint: </span><span class="si">{}</span><span class="se">\t</span><span class="s2"> Norm: </span><span class="si">{}</span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</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">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]),</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="n">flrg</span><span class="p">),</span> <span class="n">norm</span><span class="p">))</span>
|
||||
<span class="n">mp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">norm</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="n">flrg</span><span class="p">))</span>
|
||||
<span class="n">norms</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">norm</span><span class="p">)</span>
|
||||
|
||||
<span class="n">norm</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">norms</span><span class="p">)</span>
|
||||
|
||||
<span class="n">final</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">mp</span><span class="p">)</span> <span class="o">/</span> <span class="n">norm</span> <span class="k">if</span> <span class="n">norm</span> <span class="o">!=</span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">0</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Deffuzyfied value: </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">final</span><span class="p">))</span>
|
||||
<span class="k">return</span> <span class="n">final</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFTS.get_sets_from_both_fuzzyfication"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_sets_from_both_fuzzyfication">[docs]</a> <span class="k">def</span> <span class="nf">get_sets_from_both_fuzzyfication</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="p">[[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">inst</span><span class="p">]</span> <span class="k">for</span> <span class="n">inst</span> <span class="ow">in</span> <span class="n">sample</span><span class="p">]</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFTS.point_expected_value"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.point_expected_value">[docs]</a> <span class="k">def</span> <span class="nf">point_expected_value</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="n">explain</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">'explain'</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
|
||||
|
||||
<span class="n">dist</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_distribution</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
|
||||
|
||||
<span class="n">final</span> <span class="o">=</span> <span class="n">dist</span><span class="o">.</span><span class="n">expected_value</span><span class="p">()</span>
|
||||
<span class="k">return</span> <span class="n">final</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFTS.forecast_interval"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_interval">[docs]</a> <span class="k">def</span> <span class="nf">forecast_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
|
||||
<span class="n">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">'method'</span><span class="p">,</span><span class="s1">'heuristic'</span><span class="p">)</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="s1">'alpha'</span><span class="p">,</span> <span class="mf">0.05</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">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="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
|
||||
|
||||
<span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span> <span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">'heuristic'</span><span class="p">:</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interval_heuristic</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">))</span>
|
||||
<span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">'quantile'</span><span class="p">:</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">interval_quantile</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">alpha</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">))</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Unknown interval forecasting method!"</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFTS.interval_quantile"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.interval_quantile">[docs]</a> <span class="k">def</span> <span class="nf">interval_quantile</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="n">alpha</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="n">dist</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_distribution</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="n">itvl</span> <span class="o">=</span> <span class="n">dist</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">quantile</span><span class="p">([</span><span class="n">alpha</span><span class="p">,</span> <span class="mf">1.0</span> <span class="o">-</span> <span class="n">alpha</span><span class="p">])</span>
|
||||
<span class="k">return</span> <span class="n">itvl</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFTS.interval_heuristic"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.interval_heuristic">[docs]</a> <span class="k">def</span> <span class="nf">interval_heuristic</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="n">fuzzyfied</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">'fuzzyfied'</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">fuzzyfied</span><span class="p">:</span>
|
||||
<span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">fsets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_sets_from_both_fuzzyfication</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
||||
<span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg_fuzzyfied</span><span class="p">(</span><span class="n">fsets</span><span class="p">)</span>
|
||||
|
||||
<span class="n">up</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">lo</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">norms</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="n">flrgs</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">fuzzyfied</span><span class="p">:</span>
|
||||
<span class="n">norm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrg_lhs_conditional_probability</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">flrg</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">norm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrg_lhs_conditional_probability_fuzzyfied</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">flrg</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">norm</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">norm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrg_lhs_unconditional_probability</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span>
|
||||
<span class="n">up</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">norm</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">flrg</span><span class="p">))</span>
|
||||
<span class="n">lo</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">norm</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">flrg</span><span class="p">))</span>
|
||||
<span class="n">norms</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">norm</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># gerar o intervalo</span>
|
||||
<span class="n">norm</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">norms</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">norm</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="k">return</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="k">else</span><span class="p">:</span>
|
||||
<span class="n">lo_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">lo</span><span class="p">)</span> <span class="o">/</span> <span class="n">norm</span>
|
||||
<span class="n">up_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">up</span><span class="p">)</span> <span class="o">/</span> <span class="n">norm</span>
|
||||
<span class="k">return</span> <span class="p">[</span><span class="n">lo_</span><span class="p">,</span> <span class="n">up_</span><span class="p">]</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFTS.forecast_distribution"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_distribution">[docs]</a> <span class="k">def</span> <span class="nf">forecast_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
|
||||
<span class="n">smooth</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">"smooth"</span><span class="p">,</span> <span class="s2">"none"</span><span class="p">)</span>
|
||||
|
||||
<span class="n">from_distribution</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">'from_distribution'</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
|
||||
|
||||
<span class="n">fuzzyfied</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">'fuzzyfied'</span><span class="p">,</span> <span class="kc">False</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">uod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_UoD</span><span class="p">()</span>
|
||||
|
||||
<span class="k">if</span> <span class="s1">'bins'</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
|
||||
<span class="n">_bins</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'bins'</span><span class="p">)</span>
|
||||
<span class="n">nbins</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">_bins</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">nbins</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">"num_bins"</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
|
||||
<span class="n">_bins</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="n">uod</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">uod</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">nbins</span><span class="p">)</span>
|
||||
|
||||
<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="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
|
||||
<span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span> <span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">from_distribution</span><span class="p">:</span>
|
||||
<span class="n">dist</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_distribution_from_distribution</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span><span class="n">smooth</span><span class="p">,</span><span class="n">uod</span><span class="p">,</span><span class="n">_bins</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">fuzzyfied</span><span class="p">:</span>
|
||||
<span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">fsets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_sets_from_both_fuzzyfication</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
|
||||
<span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg_fuzzyfied</span><span class="p">(</span><span class="n">fsets</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="s1">'type'</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
|
||||
<span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'type'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="n">smooth</span><span class="p">,</span> <span class="n">uod</span><span class="o">=</span><span class="n">uod</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">_bins</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="nb">bin</span> <span class="ow">in</span> <span class="n">_bins</span><span class="p">:</span>
|
||||
<span class="n">num</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">den</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">flrgs</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">s</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</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">flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">s</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
|
||||
<span class="n">wi</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">rhs_conditional_probability</span><span class="p">(</span><span class="nb">bin</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">uod</span><span class="p">,</span> <span class="n">nbins</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">fuzzyfied</span><span class="p">:</span>
|
||||
<span class="n">pk</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">lhs_conditional_probability</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_frequency_count</span><span class="p">,</span> <span class="n">uod</span><span class="p">,</span> <span class="n">nbins</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">lhs_mv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pwflrg_lhs_memberhip_fuzzyfied</span><span class="p">(</span><span class="n">flrg</span><span class="p">,</span> <span class="n">sample</span><span class="p">)</span>
|
||||
<span class="n">pk</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">lhs_conditional_probability_fuzzyfied</span><span class="p">(</span><span class="n">lhs_mv</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">global_frequency_count</span><span class="p">,</span> <span class="n">uod</span><span class="p">,</span> <span class="n">nbins</span><span class="p">)</span>
|
||||
|
||||
<span class="n">num</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">wi</span> <span class="o">*</span> <span class="n">pk</span><span class="p">)</span>
|
||||
<span class="n">den</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pk</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">num</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mf">0.0</span><span class="p">)</span>
|
||||
<span class="n">den</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mf">0.000000001</span><span class="p">)</span>
|
||||
<span class="n">pf</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">num</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">den</span><span class="p">)</span>
|
||||
|
||||
<span class="n">dist</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="nb">bin</span><span class="p">,</span> <span class="n">pf</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__check_point_bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">point</span><span class="p">):</span>
|
||||
<span class="n">lower_set</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">lower_set</span><span class="p">()</span>
|
||||
<span class="n">upper_set</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">upper_set</span><span class="p">()</span>
|
||||
<span class="k">return</span> <span class="n">point</span> <span class="o"><=</span> <span class="n">lower_set</span><span class="o">.</span><span class="n">lower</span> <span class="ow">or</span> <span class="n">point</span> <span class="o">>=</span> <span class="n">upper_set</span><span class="o">.</span><span class="n">upper</span>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFTS.forecast_ahead"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead</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="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</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">data</span><span class="p">)</span>
|
||||
|
||||
<span class="n">fuzzyfied</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">'fuzzyfied'</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
|
||||
|
||||
<span class="n">start</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">'start_at'</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:</span> <span class="n">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</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="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">steps</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">):</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">__check_point_bounds</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">fuzzyfied</span><span class="p">:</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">mp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">ret</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">max_lag</span><span class="p">:</span> <span class="n">k</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">append</span><span class="p">(</span><span class="n">mp</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__check_interval_bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">interval</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">lower_set</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">lower_set</span><span class="p">()</span>
|
||||
<span class="n">upper_set</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">upper_set</span><span class="p">()</span>
|
||||
<span class="k">return</span> <span class="n">interval</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o"><=</span> <span class="n">lower_set</span><span class="o">.</span><span class="n">lower</span> <span class="ow">and</span> <span class="n">interval</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">>=</span> <span class="n">upper_set</span><span class="o">.</span><span class="n">upper</span>
|
||||
<span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">interval</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o"><=</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="ow">and</span> <span class="n">interval</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">>=</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFTS.forecast_ahead_interval"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead_interval">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead_interval</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="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
|
||||
<span class="n">start</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">'start_at'</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="s1">'fuzzyfied'</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
|
||||
<span class="n">fuzzyfied</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'fuzzyfied'</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">fuzzyfied</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
|
||||
<span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:</span> <span class="n">start</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]</span>
|
||||
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">fuzzyfied</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[[</span><span class="n">k</span><span class="p">,</span> <span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">sample</span><span class="p">]</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<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">sample</span><span class="p">:</span>
|
||||
<span class="n">kv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">defuzzyfy</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'both'</span><span class="p">)</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">kv</span><span class="p">,</span> <span class="n">kv</span><span class="p">])</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)[</span><span class="mi">0</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">start</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">steps</span> <span class="o">+</span> <span class="n">start</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">):</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">ret</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">__check_interval_bounds</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">lower</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">([</span><span class="n">ret</span><span class="p">[</span><span class="n">x</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</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">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">k</span><span class="p">)],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
<span class="n">upper</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">([</span><span class="n">ret</span><span class="p">[</span><span class="n">x</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</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">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">k</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">append</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">lower</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">upper</span><span class="p">)])</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFTS.forecast_ahead_distribution"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_ahead_distribution">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="n">steps</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="p">[]</span>
|
||||
|
||||
<span class="k">if</span> <span class="s1">'type'</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
|
||||
<span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'type'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">smooth</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">"smooth"</span><span class="p">,</span> <span class="s2">"none"</span><span class="p">)</span>
|
||||
|
||||
<span class="n">uod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_UoD</span><span class="p">()</span>
|
||||
|
||||
<span class="k">if</span> <span class="s1">'bins'</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
|
||||
<span class="n">_bins</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'bins'</span><span class="p">)</span>
|
||||
<span class="n">nbins</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">_bins</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">nbins</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">"num_bins"</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
|
||||
<span class="n">_bins</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="n">uod</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">uod</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">nbins</span><span class="p">)</span>
|
||||
|
||||
<span class="n">start</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">'start_at'</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="s1">'fuzzyfied'</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
|
||||
<span class="n">fuzzyfied</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'fuzzyfied'</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">fuzzyfied</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">fuzzyfied</span><span class="p">:</span>
|
||||
<span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">start</span><span class="p">:</span> <span class="n">start</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">sample</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">ndata</span><span class="p">[</span><span class="n">start</span><span class="p">:</span> <span class="n">start</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]:</span>
|
||||
<span class="n">kv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">defuzzyfy</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'both'</span><span class="p">)</span>
|
||||
<span class="n">sample</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">kv</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">dat</span> <span class="ow">in</span> <span class="n">sample</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dat</span><span class="p">,</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">):</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="n">smooth</span><span class="p">,</span> <span class="n">uod</span><span class="o">=</span><span class="n">uod</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">_bins</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">set</span><span class="p">(</span><span class="n">dat</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dat</span><span class="p">)</span>
|
||||
|
||||
<span class="n">dist</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_distribution_from_distribution</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span> <span class="n">smooth</span><span class="p">,</span><span class="n">uod</span><span class="p">,</span><span class="n">_bins</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">append</span><span class="p">(</span><span class="n">dist</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">start</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">steps</span> <span class="o">+</span> <span class="n">start</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">):</span>
|
||||
<span class="n">dist</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_distribution_from_distribution</span><span class="p">(</span><span class="n">ret</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">max_lag</span><span class="p">:],</span> <span class="n">smooth</span><span class="p">,</span> <span class="n">uod</span><span class="p">,</span> <span class="n">_bins</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">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ProbabilisticWeightedFTS.forecast_distribution_from_distribution"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.forecast_distribution_from_distribution">[docs]</a> <span class="k">def</span> <span class="nf">forecast_distribution_from_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">previous_dist</span><span class="p">,</span> <span class="n">smooth</span><span class="p">,</span> <span class="n">uod</span><span class="p">,</span> <span class="n">bins</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="n">smooth</span><span class="p">,</span> <span class="n">uod</span><span class="o">=</span><span class="n">uod</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">bins</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<span class="n">lags</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="c1"># Find all bins of past distributions with probability greater than zero</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">lag</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lags</span><span class="p">):</span>
|
||||
<span class="n">dd</span> <span class="o">=</span> <span class="n">previous_dist</span><span class="p">[</span><span class="o">-</span><span class="n">lag</span><span class="p">]</span>
|
||||
<span class="n">vals</span> <span class="o">=</span> <span class="p">[</span><span class="nb">float</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">dd</span><span class="o">.</span><span class="n">bins</span> <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">dd</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="n">v</span><span class="p">),</span> <span class="mi">4</span><span class="p">)</span> <span class="o">></span> <span class="mf">0.0</span><span class="p">]</span>
|
||||
<span class="n">lags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">vals</span><span class="p">))</span>
|
||||
|
||||
<span class="c1"># Trace all possible combinations between the bins of past distributions</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">lags</span><span class="p">):</span>
|
||||
|
||||
<span class="c1"># get the combined probabilities for this path</span>
|
||||
<span class="n">pk</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">prod</span><span class="p">([</span><span class="n">previous_dist</span><span class="p">[</span><span class="o">-</span><span class="n">lag</span><span class="p">]</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="n">path</span><span class="p">[</span><span class="n">ct</span><span class="p">])</span>
|
||||
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">lag</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lags</span><span class="p">)])</span>
|
||||
|
||||
<span class="n">d</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_distribution</span><span class="p">(</span><span class="n">path</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
|
||||
|
||||
<span class="k">for</span> <span class="nb">bin</span> <span class="ow">in</span> <span class="n">bins</span><span class="p">:</span>
|
||||
<span class="n">dist</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="nb">bin</span><span class="p">,</span> <span class="n">dist</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="nb">bin</span><span class="p">)</span> <span class="o">+</span> <span class="n">pk</span> <span class="o">*</span> <span class="n">d</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="nb">bin</span><span class="p">))</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">dist</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="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="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
|
||||
<span class="n">p</span> <span class="o">=</span> <span class="nb">round</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="n">frequency_count</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_frequency_count</span><span class="p">,</span> <span class="mi">3</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="s2">"("</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="o">+</span> <span class="s2">") "</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>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="highorder_fuzzy_markov_chain"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.highorder_fuzzy_markov_chain">[docs]</a><span class="k">def</span> <span class="nf">highorder_fuzzy_markov_chain</span><span class="p">(</span><span class="n">model</span><span class="p">):</span>
|
||||
<span class="n">ordered_sets</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span>
|
||||
|
||||
<span class="n">ftpg_keys</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="o">.</span><span class="n">keys</span><span class="p">(),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">x</span><span class="p">]</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">))</span>
|
||||
|
||||
<span class="n">lhs_probs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">model</span><span class="o">.</span><span class="n">flrg_lhs_unconditional_probability</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
|
||||
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">ftpg_keys</span><span class="p">])</span>
|
||||
|
||||
<span class="n">mat</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">ftpg_keys</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">ordered_sets</span><span class="p">)))</span>
|
||||
<span class="k">for</span> <span class="n">row</span><span class="p">,</span> <span class="n">w</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ftpg_keys</span><span class="p">):</span>
|
||||
<span class="k">for</span> <span class="n">col</span><span class="p">,</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ordered_sets</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">w</span><span class="p">]</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
|
||||
<span class="n">mat</span><span class="p">[</span><span class="n">row</span><span class="p">,</span> <span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">w</span><span class="p">]</span><span class="o">.</span><span class="n">rhs_unconditional_probability</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ftpg_keys</span><span class="p">,</span> <span class="n">ordered_sets</span><span class="p">,</span> <span class="n">lhs_probs</span><span class="p">,</span> <span class="n">mat</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="visualize_distributions"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.pwfts.visualize_distributions">[docs]</a><span class="k">def</span> <span class="nf">visualize_distributions</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
|
||||
<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="k">import</span> <span class="n">gridspec</span>
|
||||
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
|
||||
|
||||
<span class="n">ftpg_keys</span><span class="p">,</span> <span class="n">ordered_sets</span><span class="p">,</span> <span class="n">lhs_probs</span><span class="p">,</span> <span class="n">mat</span> <span class="o">=</span> <span class="n">highorder_fuzzy_markov_chain</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
|
||||
|
||||
<span class="n">size</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">'size'</span><span class="p">,</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="mi">10</span><span class="p">))</span>
|
||||
|
||||
<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">size</span><span class="p">)</span>
|
||||
|
||||
<span class="n">gs</span> <span class="o">=</span> <span class="n">gridspec</span><span class="o">.</span><span class="n">GridSpec</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">width_ratios</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span>
|
||||
<span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
|
||||
<span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s1">'y'</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s1">'x'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'darkblue'</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="p">{</span><span class="s1">'x'</span><span class="p">:</span> <span class="n">ftpg_keys</span><span class="p">,</span> <span class="s1">'y'</span><span class="p">:</span> <span class="n">lhs_probs</span><span class="p">},</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">)</span>
|
||||
<span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"LHS Probabilities"</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ind_sets</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ordered_sets</span><span class="p">))</span>
|
||||
<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">gs</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
|
||||
<span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">mat</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">'Blues'</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">yticklabels</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"RHS probabilities"</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">ind_sets</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">ordered_sets</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_tick_params</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">90</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
</pre></div>
|
||||
|
||||
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|
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|
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203
docs/build/html/_modules/pyFTS/models/sadaei.html
vendored
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<h1>Source code for pyFTS.models.sadaei</h1><div class="highlight"><pre>
|
||||
<span></span><span class="sd">"""</span>
|
||||
<span class="sd">First Order Exponentialy Weighted Fuzzy Time Series by Sadaei et al. (2013)</span>
|
||||
|
||||
<span class="sd">H. J. Sadaei, R. Enayatifar, A. H. Abdullah, and A. Gani, “Short-term load forecasting using a hybrid model with a </span>
|
||||
<span class="sd">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.</span>
|
||||
<span class="sd">"""</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">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>
|
||||
|
||||
<span class="n">default_c</span> <span class="o">=</span> <span class="mf">1.1</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ExponentialyWeightedFLRG"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.sadaei.ExponentialyWeightedFLRG">[docs]</a><span class="k">class</span> <span class="nc">ExponentialyWeightedFLRG</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">):</span>
|
||||
<span class="sd">"""First Order Exponentialy Weighted Fuzzy Logical Relationship Group"""</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">LHS</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">ExponentialyWeightedFLRG</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="mi">1</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">LHS</span> <span class="o">=</span> <span class="n">LHS</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">=</span> <span class="mf">0.0</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">c</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">"c"</span><span class="p">,</span><span class="n">default_c</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
|
||||
<div class="viewcode-block" id="ExponentialyWeightedFLRG.append_rhs"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.sadaei.ExponentialyWeightedFLRG.append_rhs">[docs]</a> <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</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">count</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">'count'</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">+=</span> <span class="n">count</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ExponentialyWeightedFLRG.weights"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.sadaei.ExponentialyWeightedFLRG.weights">[docs]</a> <span class="k">def</span> <span class="nf">weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">wei</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">c</span> <span class="o">**</span> <span class="n">k</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="mf">0.0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)]</span>
|
||||
<span class="n">tot</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">wei</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">k</span> <span class="o">/</span> <span class="n">tot</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">wei</span><span class="p">])</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</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="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span> <span class="o">+</span> <span class="s2">" -> "</span>
|
||||
<span class="n">tmp2</span> <span class="o">=</span> <span class="s2">""</span>
|
||||
<span class="n">cc</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="n">wei</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">c</span> <span class="o">**</span> <span class="n">k</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="mf">0.0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)]</span>
|
||||
<span class="n">tot</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">wei</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">tmp2</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="s2">","</span>
|
||||
<span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="n">c</span> <span class="o">+</span> <span class="s2">"("</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">wei</span><span class="p">[</span><span class="n">cc</span><span class="p">]</span> <span class="o">/</span> <span class="n">tot</span><span class="p">)</span> <span class="o">+</span> <span class="s2">")"</span>
|
||||
<span class="n">cc</span> <span class="o">=</span> <span class="n">cc</span> <span class="o">+</span> <span class="mi">1</span>
|
||||
<span class="k">return</span> <span class="n">tmp</span> <span class="o">+</span> <span class="n">tmp2</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="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ExponentialyWeightedFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.sadaei.ExponentialyWeightedFTS">[docs]</a><span class="k">class</span> <span class="nc">ExponentialyWeightedFTS</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
|
||||
<span class="sd">"""First Order Exponentialy Weighted Fuzzy Time Series"""</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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">ExponentialyWeightedFTS</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">order</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">"EWFTS"</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">name</span> <span class="o">=</span> <span class="s2">"Exponentialy Weighted FTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">"Sadaei"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">c</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">'c'</span><span class="p">,</span> <span class="n">default_c</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="ExponentialyWeightedFTS.generate_flrg"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.sadaei.ExponentialyWeightedFTS.generate_flrg">[docs]</a> <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
|
||||
<span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">flr</span><span class="o">.</span><span class="n">LHS</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="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</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">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span> <span class="o">=</span> <span class="n">ExponentialyWeightedFLRG</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">c</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">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ExponentialyWeightedFTS.train"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.sadaei.ExponentialyWeightedFTS.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>
|
||||
<span class="n">tmpdata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">'maximum'</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">flrs</span> <span class="o">=</span> <span class="n">FLR</span><span class="o">.</span><span class="n">generate_recurrent_flrs</span><span class="p">(</span><span class="n">tmpdata</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg</span><span class="p">(</span><span class="n">flrs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">c</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ExponentialyWeightedFTS.forecast"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.sadaei.ExponentialyWeightedFTS.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">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
|
||||
<span class="n">explain</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">'explain'</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">ordered_sets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ordered_sets</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">set_ordered</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
|
||||
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ndata</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">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="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
|
||||
|
||||
<span class="n">actual</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">get_maximum_membership_fuzzyset</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">ordered_sets</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Fuzzyfication:</span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> -> </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">))</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">actual</span><span class="o">.</span><span class="n">name</span> <span class="ow">not</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">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">actual</span><span class="o">.</span><span class="n">centroid</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Rules:</span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> -> </span><span class="si">{}</span><span class="s2"> (Naïve)</span><span class="se">\t</span><span class="s2"> Midpoint: </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n\n</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">,</span><span class="n">actual</span><span class="o">.</span><span class="n">centroid</span><span class="p">))</span>
|
||||
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>
|
||||
<span class="n">mp</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_midpoints</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
|
||||
|
||||
<span class="n">final</span> <span class="o">=</span> <span class="n">mp</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">weights</span><span class="p">())</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">final</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Rules:</span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n\n</span><span class="s2"> "</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">flrg</span><span class="p">)))</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Midpoints: </span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="se">\n\n</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mp</span><span class="p">))</span>
|
||||
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Deffuzyfied value: </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">final</span><span class="p">))</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div></div>
|
||||
</pre></div>
|
||||
|
||||
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|
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<h1>Source code for pyFTS.models.seasonal.cmsfts</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 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="kn">from</span> <span class="nn">pyFTS.models.seasonal</span> <span class="k">import</span> <span class="n">sfts</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">chen</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ContextualSeasonalFLRG"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.cmsfts.ContextualSeasonalFLRG">[docs]</a><span class="k">class</span> <span class="nc">ContextualSeasonalFLRG</span><span class="p">(</span><span class="n">sfts</span><span class="o">.</span><span class="n">SeasonalFLRG</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Contextual Seasonal Fuzzy Logical Relationship Group</span>
|
||||
<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">seasonality</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">ContextualSeasonalFLRG</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">seasonality</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
|
||||
<div class="viewcode-block" id="ContextualSeasonalFLRG.append_rhs"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.cmsfts.ContextualSeasonalFLRG.append_rhs">[docs]</a> <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flr</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">flr</span><span class="o">.</span><span class="n">LHS</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</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">RHS</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span> <span class="o">=</span> <span class="n">chen</span><span class="o">.</span><span class="n">ConventionalFLRG</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</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="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">LHS</span><span class="p">)</span> <span class="o">+</span> <span class="s2">": </span><span class="se">\n</span><span class="s2"> "</span>
|
||||
<span class="n">tmp2</span> <span class="o">=</span> <span class="s2">"</span><span class="se">\t</span><span class="s2">"</span>
|
||||
<span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">):</span>
|
||||
<span class="n">tmp2</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">RHS</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\t</span><span class="s2">"</span>
|
||||
<span class="k">return</span> <span class="n">tmp</span> <span class="o">+</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="s2">"</span><span class="se">\n</span><span class="s2">"</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ContextualMultiSeasonalFTS"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS">[docs]</a><span class="k">class</span> <span class="nc">ContextualMultiSeasonalFTS</span><span class="p">(</span><span class="n">sfts</span><span class="o">.</span><span class="n">SeasonalFTS</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Contextual Multi-Seasonal Fuzzy Time Series</span>
|
||||
<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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">ContextualMultiSeasonalFTS</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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"Contextual Multi Seasonal FTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">"CMSFTS "</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">seasonality</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">has_seasonality</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<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>
|
||||
<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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
|
||||
<div class="viewcode-block" id="ContextualMultiSeasonalFTS.generate_flrg"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS.generate_flrg">[docs]</a> <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">):</span>
|
||||
<span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">str</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">index</span><span class="p">)</span> <span class="ow">not</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="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">index</span><span class="p">)]</span> <span class="o">=</span> <span class="n">ContextualSeasonalFLRG</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">index</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="nb">str</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">index</span><span class="p">)]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ContextualMultiSeasonalFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS.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>
|
||||
<span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'sets'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</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="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'sets'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'parameters'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">seasonality</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">'parameters'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
<span class="n">flrs</span> <span class="o">=</span> <span class="n">FLR</span><span class="o">.</span><span class="n">generate_indexed_flrs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span>
|
||||
<span class="n">transformation</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">transformation</span><span class="p">,</span>
|
||||
<span class="n">alpha_cut</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha_cut</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg</span><span class="p">(</span><span class="n">flrs</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ContextualMultiSeasonalFTS.get_midpoints"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS.get_midpoints">[docs]</a> <span class="k">def</span> <span class="nf">get_midpoints</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">data</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">RHS</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">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">d</span><span class="p">]</span><span class="o">.</span><span class="n">RHS</span><span class="p">])</span>
|
||||
<span class="k">else</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">sets</span><span class="p">[</span><span class="n">d</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span><span class="p">])</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ret</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ContextualMultiSeasonalFTS.forecast"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS.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>
|
||||
<span class="n">ordered_sets</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">set_ordered</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">index</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_season_of_data</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="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</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="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)):</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">str</span><span class="p">(</span><span class="n">index</span><span class="p">[</span><span class="n">k</span><span class="p">])</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">flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">index</span><span class="p">[</span><span class="n">k</span><span class="p">])]</span>
|
||||
|
||||
<span class="n">d</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">get_fuzzysets</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">ordered_sets</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha_cut</span><span class="p">)</span>
|
||||
|
||||
<span class="n">mp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_midpoints</span><span class="p">(</span><span class="n">flrg</span><span class="p">,</span> <span class="n">d</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">mp</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">mp</span><span class="p">))</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<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">nan</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ContextualMultiSeasonalFTS.forecast_ahead"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.cmsfts.ContextualMultiSeasonalFTS.forecast_ahead">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead</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="n">steps</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="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">steps</span><span class="p">:</span>
|
||||
<span class="n">flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)]</span>
|
||||
|
||||
<span class="n">mp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_midpoints</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">mp</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">mp</span><span class="p">))</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div></div>
|
||||
|
||||
|
||||
</pre></div>
|
||||
|
||||
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|
||||
<h1>Source code for pyFTS.models.seasonal.msfts</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 class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">FLR</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.models.seasonal</span> <span class="k">import</span> <span class="n">sfts</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="MultiSeasonalFTS"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.msfts.MultiSeasonalFTS">[docs]</a><span class="k">class</span> <span class="nc">MultiSeasonalFTS</span><span class="p">(</span><span class="n">sfts</span><span class="o">.</span><span class="n">SeasonalFTS</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Multi-Seasonal Fuzzy Time Series</span>
|
||||
<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">name</span><span class="p">,</span> <span class="n">indexer</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">MultiSeasonalFTS</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="s2">"MSFTS"</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"Multi Seasonal FTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">"MSFTS "</span> <span class="o">+</span> <span class="n">name</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">""</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">seasonality</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">has_seasonality</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">indexer</span> <span class="o">=</span> <span class="n">indexer</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
|
||||
<div class="viewcode-block" id="MultiSeasonalFTS.generate_flrg"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.msfts.MultiSeasonalFTS.generate_flrg">[docs]</a> <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">):</span>
|
||||
<span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">str</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">index</span><span class="p">)</span> <span class="ow">not</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="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">index</span><span class="p">)]</span> <span class="o">=</span> <span class="n">sfts</span><span class="o">.</span><span class="n">SeasonalFLRG</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">index</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="nb">str</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">index</span><span class="p">)]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="MultiSeasonalFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.msfts.MultiSeasonalFTS.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>
|
||||
<span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'sets'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</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="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'sets'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'parameters'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">seasonality</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">'parameters'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
<span class="c1">#ndata = self.indexer.set_data(data,self.doTransformations(self.indexer.get_data(data)))</span>
|
||||
<span class="n">flrs</span> <span class="o">=</span> <span class="n">FLR</span><span class="o">.</span><span class="n">generate_indexed_flrs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="p">,</span> <span class="n">data</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg</span><span class="p">(</span><span class="n">flrs</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="MultiSeasonalFTS.forecast"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.msfts.MultiSeasonalFTS.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>
|
||||
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">index</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_season_of_data</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="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</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="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">index</span><span class="p">)):</span>
|
||||
|
||||
<span class="n">flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">index</span><span class="p">[</span><span class="n">k</span><span class="p">])]</span>
|
||||
|
||||
<span class="n">mp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">getMidpoints</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">mp</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">mp</span><span class="p">))</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="MultiSeasonalFTS.forecast_ahead"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.msfts.MultiSeasonalFTS.forecast_ahead">[docs]</a> <span class="k">def</span> <span class="nf">forecast_ahead</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="n">steps</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="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">steps</span><span class="p">:</span>
|
||||
<span class="n">flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)]</span>
|
||||
|
||||
<span class="n">mp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">getMidpoints</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">mp</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">mp</span><span class="p">))</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div></div>
|
||||
</pre></div>
|
||||
|
||||
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<h1>Source code for pyFTS.models.seasonal.sfts</h1><div class="highlight"><pre>
|
||||
<span></span><span class="sd">"""</span>
|
||||
<span class="sd">Simple First Order Seasonal Fuzzy Time Series implementation of Song (1999) based of Conventional FTS by Chen (1996)</span>
|
||||
|
||||
<span class="sd">Q. Song, “Seasonal forecasting in fuzzy time series,” Fuzzy sets Syst., vol. 107, pp. 235–236, 1999.</span>
|
||||
|
||||
<span class="sd">S.-M. Chen, “Forecasting enrollments based on fuzzy time series,” Fuzzy Sets Syst., vol. 81, no. 3, pp. 311–319, 1996.</span>
|
||||
<span class="sd">"""</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">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">flrg</span><span class="p">,</span> <span class="n">fts</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="SeasonalFLRG"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.sfts.SeasonalFLRG">[docs]</a><span class="k">class</span> <span class="nc">SeasonalFLRG</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">):</span>
|
||||
<span class="sd">"""First Order Seasonal Fuzzy Logical Relationship Group"""</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">seasonality</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">SeasonalFLRG</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="mi">1</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">LHS</span> <span class="o">=</span> <span class="n">seasonality</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<div class="viewcode-block" id="SeasonalFLRG.get_key"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.sfts.SeasonalFLRG.get_key">[docs]</a> <span class="k">def</span> <span class="nf">get_key</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span></div>
|
||||
|
||||
<div class="viewcode-block" id="SeasonalFLRG.append_rhs"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.sfts.SeasonalFLRG.append_rhs">[docs]</a> <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</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="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">c</span><span class="p">)</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="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">LHS</span><span class="p">)</span> <span class="o">+</span> <span class="s2">" -> "</span>
|
||||
<span class="n">tmp2</span> <span class="o">=</span> <span class="s2">""</span>
|
||||
<span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">s</span><span class="p">:</span> <span class="nb">str</span><span class="p">(</span><span class="n">s</span><span class="p">)):</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">tmp2</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="s2">","</span>
|
||||
<span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">tmp</span> <span class="o">+</span> <span class="n">tmp2</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="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="SeasonalFTS"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.sfts.SeasonalFTS">[docs]</a><span class="k">class</span> <span class="nc">SeasonalFTS</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
|
||||
<span class="sd">"""First Order Seasonal Fuzzy Time Series"""</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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">SeasonalFTS</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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">"Seasonal FTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">"SFTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">seasonality</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">has_seasonality</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
<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>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
|
||||
<div class="viewcode-block" id="SeasonalFTS.generate_flrg"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.sfts.SeasonalFTS.generate_flrg">[docs]</a> <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">):</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">flr</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">flrs</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
|
||||
|
||||
<span class="n">season</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_season_by_index</span><span class="p">(</span><span class="n">ct</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
|
||||
|
||||
<span class="n">ss</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">season</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">ss</span> <span class="ow">not</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="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">ss</span><span class="p">]</span> <span class="o">=</span> <span class="n">SeasonalFLRG</span><span class="p">(</span><span class="n">season</span><span class="p">)</span>
|
||||
|
||||
<span class="c1">#print(season)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">ss</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="SeasonalFTS.get_midpoints"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.sfts.SeasonalFTS.get_midpoints">[docs]</a> <span class="k">def</span> <span class="nf">get_midpoints</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">RHS</span><span class="p">])</span>
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
<div class="viewcode-block" id="SeasonalFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.sfts.SeasonalFTS.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>
|
||||
<span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'sets'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</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="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'sets'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
<span class="n">tmpdata</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">fuzzyfy_series_old</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
|
||||
<span class="n">flrs</span> <span class="o">=</span> <span class="n">FLR</span><span class="o">.</span><span class="n">generate_non_recurrent_flrs</span><span class="p">(</span><span class="n">tmpdata</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg</span><span class="p">(</span><span class="n">flrs</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="SeasonalFTS.forecast"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.sfts.SeasonalFTS.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>
|
||||
|
||||
<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>
|
||||
|
||||
<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="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
|
||||
|
||||
<span class="n">season</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_season_by_index</span><span class="p">(</span><span class="n">k</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
|
||||
|
||||
<span class="n">flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">season</span><span class="p">)]</span>
|
||||
|
||||
<span class="n">mp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_midpoints</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span>
|
||||
|
||||
<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>
|
||||
|
||||
<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 class="clearer"></div>
|
||||
</div>
|
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|
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</div>
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<h3 id="searchlabel">Quick search</h3>
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>modules</a> |</li>
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172
docs/build/html/_modules/pyFTS/models/song.html
vendored
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<h1>Source code for pyFTS.models.song</h1><div class="highlight"><pre>
|
||||
<span></span><span class="sd">"""</span>
|
||||
<span class="sd">First Order Traditional Fuzzy Time Series method by Song & Chissom (1993)</span>
|
||||
|
||||
<span class="sd">Q. Song and B. S. Chissom, “Fuzzy time series and its models,” Fuzzy Sets Syst., vol. 54, no. 3, pp. 269–277, 1993.</span>
|
||||
<span class="sd">"""</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">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>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ConventionalFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.song.ConventionalFTS">[docs]</a><span class="k">class</span> <span class="nc">ConventionalFTS</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
|
||||
<span class="sd">"""Traditional Fuzzy Time Series"""</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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">ConventionalFTS</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">order</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">"FTS"</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">name</span> <span class="o">=</span> <span class="s2">"Traditional FTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">"Song & Chissom"</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">R</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</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="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">R</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">l</span><span class="p">,</span><span class="n">l</span><span class="p">))</span>
|
||||
|
||||
<div class="viewcode-block" id="ConventionalFTS.flr_membership_matrix"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.song.ConventionalFTS.flr_membership_matrix">[docs]</a> <span class="k">def</span> <span class="nf">flr_membership_matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flr</span><span class="p">):</span>
|
||||
<span class="n">ordered_set</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">set_ordered</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
|
||||
<span class="n">centroids</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">ordered_set</span><span class="p">]</span>
|
||||
<span class="n">lm</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">centroids</span><span class="p">]</span>
|
||||
<span class="n">rm</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">centroids</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">ordered_set</span><span class="p">)</span>
|
||||
<span class="n">r</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">l</span><span class="p">,</span> <span class="n">l</span><span class="p">))</span>
|
||||
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">l</span><span class="p">):</span>
|
||||
<span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
|
||||
<span class="n">r</span><span class="p">[</span><span class="n">k</span><span class="p">][</span><span class="n">l</span><span class="p">]</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">lm</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">rm</span><span class="p">[</span><span class="n">l</span><span class="p">])</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">r</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ConventionalFTS.operation_matrix"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.song.ConventionalFTS.operation_matrix">[docs]</a> <span class="k">def</span> <span class="nf">operation_matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</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="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">R</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">R</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span> <span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">R</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">l</span><span class="p">,</span> <span class="n">l</span><span class="p">))</span>
|
||||
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
|
||||
<span class="n">mm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flr_membership_matrix</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
|
||||
<span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">R</span><span class="p">[</span><span class="n">k</span><span class="p">][</span><span class="n">l</span><span class="p">]</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">R</span><span class="p">[</span><span class="n">k</span><span class="p">][</span><span class="n">l</span><span class="p">],</span> <span class="n">mm</span><span class="p">[</span><span class="n">k</span><span class="p">][</span><span class="n">l</span><span class="p">])</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ConventionalFTS.train"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.song.ConventionalFTS.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>
|
||||
|
||||
<span class="n">tmpdata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">'maximum'</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">flrs</span> <span class="o">=</span> <span class="n">FLR</span><span class="o">.</span><span class="n">generate_non_recurrent_flrs</span><span class="p">(</span><span class="n">tmpdata</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">operation_matrix</span><span class="p">(</span><span class="n">flrs</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="ConventionalFTS.forecast"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.song.ConventionalFTS.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">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">ordered_sets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ordered_sets</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">set_ordered</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</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">npart</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="p">)</span>
|
||||
|
||||
<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="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
|
||||
<span class="n">mv</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">fuzzyfy_instance</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
|
||||
|
||||
<span class="n">r</span> <span class="o">=</span> <span class="p">[</span><span class="nb">max</span><span class="p">([</span> <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">R</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">],</span> <span class="n">mv</span><span class="p">[</span><span class="n">j</span><span class="p">])</span> <span class="k">for</span> <span class="n">j</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">0</span><span class="p">,</span><span class="n">npart</span><span class="p">)</span> <span class="p">])</span> <span class="k">for</span> <span class="n">i</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">0</span><span class="p">,</span><span class="n">npart</span><span class="p">)]</span>
|
||||
|
||||
<span class="n">fs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">argwhere</span><span class="p">(</span><span class="n">r</span> <span class="o">==</span> <span class="nb">max</span><span class="p">(</span><span class="n">r</span><span class="p">)))</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">fs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">ordered_sets</span><span class="p">[</span><span class="n">fs</span><span class="p">[</span><span class="mi">0</span><span class="p">]]]</span><span class="o">.</span><span class="n">centroid</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">mp</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">ordered_sets</span><span class="p">[</span><span class="n">s</span><span class="p">]]</span><span class="o">.</span><span class="n">centroid</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">fs</span><span class="p">]</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> <span class="nb">sum</span><span class="p">(</span><span class="n">mp</span><span class="p">)</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">mp</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="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">return</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">R</span><span class="p">)</span></div>
|
||||
</pre></div>
|
||||
|
||||
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195
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<h1>Source code for pyFTS.models.yu</h1><div class="highlight"><pre>
|
||||
<span></span><span class="sd">"""</span>
|
||||
<span class="sd">First Order Weighted Fuzzy Time Series by Yu(2005)</span>
|
||||
|
||||
<span class="sd">H.-K. Yu, “Weighted fuzzy time series models for TAIEX forecasting,” </span>
|
||||
<span class="sd">Phys. A Stat. Mech. its Appl., vol. 349, no. 3, pp. 609–624, 2005.</span>
|
||||
<span class="sd">"""</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">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>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">chen</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="WeightedFLRG"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.yu.WeightedFLRG">[docs]</a><span class="k">class</span> <span class="nc">WeightedFLRG</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">):</span>
|
||||
<span class="sd">"""First Order Weighted Fuzzy Logical Relationship Group"""</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">LHS</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">WeightedFLRG</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="mi">1</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">LHS</span> <span class="o">=</span> <span class="n">LHS</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">=</span> <span class="mf">1.0</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
|
||||
<div class="viewcode-block" id="WeightedFLRG.append_rhs"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.yu.WeightedFLRG.append_rhs">[docs]</a> <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</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">count</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">'count'</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">+=</span> <span class="n">count</span></div>
|
||||
|
||||
<div class="viewcode-block" id="WeightedFLRG.weights"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.yu.WeightedFLRG.weights">[docs]</a> <span class="k">def</span> <span class="nf">weights</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">tot</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">))</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">k</span> <span class="o">/</span> <span class="n">tot</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="mf">1.0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)])</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</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="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span> <span class="o">+</span> <span class="s2">" -> "</span>
|
||||
<span class="n">tmp2</span> <span class="o">=</span> <span class="s2">""</span>
|
||||
<span class="n">cc</span> <span class="o">=</span> <span class="mf">1.0</span>
|
||||
<span class="n">tot</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">))</span>
|
||||
<span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">tmp2</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="s2">","</span>
|
||||
<span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="n">c</span> <span class="o">+</span> <span class="s2">"("</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">cc</span> <span class="o">/</span> <span class="n">tot</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="o">+</span> <span class="s2">")"</span>
|
||||
<span class="n">cc</span> <span class="o">=</span> <span class="n">cc</span> <span class="o">+</span> <span class="mf">1.0</span>
|
||||
<span class="k">return</span> <span class="n">tmp</span> <span class="o">+</span> <span class="n">tmp2</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="WeightedFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.yu.WeightedFTS">[docs]</a><span class="k">class</span> <span class="nc">WeightedFTS</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
|
||||
<span class="sd">"""First Order Weighted Fuzzy Time Series"""</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="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">WeightedFTS</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">order</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">"WFTS"</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">name</span> <span class="o">=</span> <span class="s2">"Weighted FTS"</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">"Yu"</span>
|
||||
|
||||
<div class="viewcode-block" id="WeightedFTS.generate_FLRG"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.yu.WeightedFTS.generate_FLRG">[docs]</a> <span class="k">def</span> <span class="nf">generate_FLRG</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">):</span>
|
||||
<span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">flr</span><span class="o">.</span><span class="n">LHS</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="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</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">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span> <span class="o">=</span> <span class="n">WeightedFLRG</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</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">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="WeightedFTS.train"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.yu.WeightedFTS.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">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="n">tmpdata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">'maximum'</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">flrs</span> <span class="o">=</span> <span class="n">FLR</span><span class="o">.</span><span class="n">generate_recurrent_flrs</span><span class="p">(</span><span class="n">tmpdata</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">generate_FLRG</span><span class="p">(</span><span class="n">flrs</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="WeightedFTS.forecast"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.yu.WeightedFTS.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">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
|
||||
<span class="n">explain</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">'explain'</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">ordered_sets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ordered_sets</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">set_ordered</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ndata</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="k">if</span> <span class="ow">not</span> <span class="n">explain</span> <span class="k">else</span> <span class="mi">1</span>
|
||||
|
||||
<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="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
|
||||
|
||||
<span class="n">actual</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">get_maximum_membership_fuzzyset</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">ordered_sets</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Fuzzyfication:</span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> -> </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n\n</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">))</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">actual</span><span class="o">.</span><span class="n">name</span> <span class="ow">not</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">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">actual</span><span class="o">.</span><span class="n">centroid</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Rules:</span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> -> </span><span class="si">{}</span><span class="s2"> (Naïve)</span><span class="se">\t</span><span class="s2"> Midpoint: </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n\n</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">,</span><span class="n">actual</span><span class="o">.</span><span class="n">centroid</span><span class="p">))</span>
|
||||
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>
|
||||
<span class="n">mp</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_midpoints</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
|
||||
|
||||
<span class="n">final</span> <span class="o">=</span> <span class="n">mp</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">weights</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">))</span>
|
||||
|
||||
<span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">final</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Rules:</span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n\n</span><span class="s2"> "</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">flrg</span><span class="p">)))</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Midpoints: </span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="se">\n\n</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mp</span><span class="p">))</span>
|
||||
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Deffuzyfied value: </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">final</span><span class="p">))</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div></div>
|
||||
</pre></div>
|
||||
|
||||
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|
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© Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
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|
||||
<img class="logo" src="../../../_static/logo_heading2.png" alt="Logo"/>
|
||||
</a></p>
|
||||
<div id="searchbox" style="display: none" role="search">
|
||||
<h3 id="searchlabel">Quick search</h3>
|
||||
<div class="searchformwrapper">
|
||||
|
||||
@ -161,6 +161,9 @@
|
||||
</div>
|
||||
<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>
|
||||
<div id="searchbox" style="display: none" role="search">
|
||||
<h3 id="searchlabel">Quick search</h3>
|
||||
<div class="searchformwrapper">
|
||||
|
||||
@ -177,6 +177,9 @@
|
||||
</div>
|
||||
<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>
|
||||
<div id="searchbox" style="display: none" role="search">
|
||||
<h3 id="searchlabel">Quick search</h3>
|
||||
<div class="searchformwrapper">
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in New Issue
Block a user