Updating the documentation
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Petrônio Cândido
parent
1211aa2be7
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934c744139
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docs/build/doctrees/environment.pickle
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docs/build/html/_modules/index.html
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@ -111,6 +111,7 @@
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<li><a href="pyFTS/data/mackey_glass.html">pyFTS.data.mackey_glass</a></li>
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<li><a href="pyFTS/data/rossler.html">pyFTS.data.rossler</a></li>
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<li><a href="pyFTS/data/sunspots.html">pyFTS.data.sunspots</a></li>
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<li><a href="pyFTS/distributed/spark.html">pyFTS.distributed.spark</a></li>
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<li><a href="pyFTS/hyperparam/GridSearch.html">pyFTS.hyperparam.GridSearch</a></li>
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<li><a href="pyFTS/hyperparam/Util.html">pyFTS.hyperparam.Util</a></li>
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<li><a href="pyFTS/models/chen.html">pyFTS.models.chen</a></li>
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@ -120,7 +121,8 @@
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<li><a href="pyFTS/models/hofts.html">pyFTS.models.hofts</a></li>
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<li><a href="pyFTS/models/hwang.html">pyFTS.models.hwang</a></li>
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<li><a href="pyFTS/models/ifts.html">pyFTS.models.ifts</a></li>
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<li><a href="pyFTS/models/incremental/Retrainer.html">pyFTS.models.incremental.Retrainer</a></li>
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<li><a href="pyFTS/models/incremental/IncrementalEnsemble.html">pyFTS.models.incremental.IncrementalEnsemble</a></li>
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<li><a href="pyFTS/models/incremental/TimeVariant.html">pyFTS.models.incremental.TimeVariant</a></li>
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<li><a href="pyFTS/models/ismailefendi.html">pyFTS.models.ismailefendi</a></li>
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<li><a href="pyFTS/models/multivariate/FLR.html">pyFTS.models.multivariate.FLR</a></li>
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<li><a href="pyFTS/models/multivariate/cmvfts.html">pyFTS.models.multivariate.cmvfts</a></li>
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@ -93,7 +93,7 @@
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<span class="sd"> :param k: </span>
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<span class="sd"> :return: </span>
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<span class="sd"> """</span>
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<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">data</span><span class="p">)</span>
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<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>
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<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>
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<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>
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<span class="n">s</span> <span class="o">=</span> <span class="mi">0</span>
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@ -142,7 +142,7 @@
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<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>
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<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>
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<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>
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<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">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">targets</span> <span class="o">-</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>
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<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>
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<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>
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@ -159,11 +159,11 @@
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<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>
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<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>
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<span class="k">if</span> <span class="nb">type</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
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<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">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>
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<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>
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<span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
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<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">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="nb">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>
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<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>
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<span class="k">else</span><span class="p">:</span>
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<span class="k">return</span> <span class="nb">sum</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="nb">sum</span><span class="p">(</span><span class="n">forecasts</span> <span class="o">+</span> <span class="n">targets</span><span class="p">)</span></div>
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<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</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">sum</span><span class="p">(</span><span class="n">forecasts</span> <span class="o">+</span> <span class="n">targets</span><span class="p">)</span></div>
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<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>
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@ -188,9 +188,9 @@
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<span class="n">naive</span> <span class="o">=</span> <span class="p">[]</span>
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<span class="n">y</span> <span class="o">=</span> <span class="p">[]</span>
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<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>
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<span class="n">y</span><span class="o">.</span><span class="n">append</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="o">-</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>
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<span class="n">naive</span><span class="o">.</span><span class="n">append</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="o">-</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>
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<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="nb">sum</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="o">/</span> <span class="nb">sum</span><span class="p">(</span><span class="n">naive</span><span class="p">))</span></div>
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<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>
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<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>
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<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">sum</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">sum</span><span class="p">(</span><span class="n">naive</span><span class="p">)))</span></div>
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<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>
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@ -262,7 +262,7 @@
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<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>
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<span class="k">else</span><span class="p">:</span>
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<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>
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<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">preds</span><span class="p">)</span></div>
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<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>
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<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>
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@ -275,9 +275,9 @@
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<span class="sd"> :return: float, distance of forecast to the tau-quantile of the target</span>
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<span class="sd"> """</span>
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<span class="k">if</span> <span class="n">target</span> <span class="o">>=</span> <span class="n">forecast</span><span class="p">:</span>
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<span class="k">return</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="o">*</span> <span class="n">tau</span>
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<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>
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<span class="k">else</span><span class="p">:</span>
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<span class="k">return</span> <span class="p">(</span><span class="n">forecast</span> <span class="o">-</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>
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<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>
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<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>
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@ -148,7 +148,13 @@
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<span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="o">=</span> <span class="nb">set</span><span class="o">.</span><span class="n">upper</span>
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<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">centroid</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">centroid</span> <span class="o"><</span> <span class="nb">set</span><span class="o">.</span><span class="n">centroid</span><span class="p">:</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">centroid</span> <span class="o">=</span> <span class="nb">set</span><span class="o">.</span><span class="n">centroid</span></div></div>
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<span class="bp">self</span><span class="o">.</span><span class="n">centroid</span> <span class="o">=</span> <span class="nb">set</span><span class="o">.</span><span class="n">centroid</span></div>
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||||
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<span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
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||||
<span class="n">tmp</span> <span class="o">=</span> <span class="nb">str</span><span class="p">([</span><span class="nb">str</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">sets</span><span class="p">])</span>
|
||||
<span class="k">return</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">name</span><span class="p">,</span> <span class="n">tmp</span><span class="p">)</span></div>
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||||
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</pre></div>
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@ -251,8 +251,11 @@
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||||
<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="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></div>
|
||||
<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>
|
||||
|
||||
14
docs/build/html/_modules/pyFTS/common/fts.html
vendored
14
docs/build/html/_modules/pyFTS/common/fts.html
vendored
@ -98,6 +98,8 @@
|
||||
<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="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="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="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="o">=</span> <span class="mi">1</span>
|
||||
@ -174,8 +176,9 @@
|
||||
<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.</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 variables 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.</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>
|
||||
@ -296,13 +299,11 @@
|
||||
<span class="sd"> :return: a list with the forecasted values</span>
|
||||
<span class="sd"> """</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="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="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</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="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>
|
||||
@ -388,6 +389,7 @@
|
||||
<span class="k">if</span> <span class="s1">'partitioner'</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">partitioner</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="k">if</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="k">if</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sets</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">sets</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</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="ow">and</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="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">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>
|
||||
@ -600,7 +602,7 @@
|
||||
<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">model</span><span class="o">.</span><span class="n">flrgs</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>
|
||||
|
||||
|
||||
260
docs/build/html/_modules/pyFTS/data/artificial.html
vendored
260
docs/build/html/_modules/pyFTS/data/artificial.html
vendored
@ -79,6 +79,148 @@
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="SignalEmulator"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.SignalEmulator">[docs]</a><span class="k">class</span> <span class="nc">SignalEmulator</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Emulate a complex signal built from several additive and non-additive components</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">SignalEmulator</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">components</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="sd">"""Components of the signal"""</span>
|
||||
|
||||
<div class="viewcode-block" id="SignalEmulator.stationary_gaussian"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.SignalEmulator.stationary_gaussian">[docs]</a> <span class="k">def</span> <span class="nf">stationary_gaussian</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">sigma</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"> Creates a continuous Gaussian signal with mean mu and variance sigma.</span>
|
||||
|
||||
<span class="sd"> :param mu: mean</span>
|
||||
<span class="sd"> :param sigma: variance</span>
|
||||
<span class="sd"> :keyword additive: If False it cancels the previous signal and start this one, if True</span>
|
||||
<span class="sd"> this signal is added to the previous one</span>
|
||||
<span class="sd"> :keyword start: lag index to start this signal, the default value is 0</span>
|
||||
<span class="sd"> :keyword it: Number of iterations, the default value is 1</span>
|
||||
<span class="sd"> :keyword length: Number of samples generated on each iteration, the default value is 100</span>
|
||||
<span class="sd"> :keyword vmin: Lower bound value of generated data, the default value is None</span>
|
||||
<span class="sd"> :keyword vmax: Upper bound value of generated data, the default value is None</span>
|
||||
<span class="sd"> :return: the current SignalEmulator instance, for method chaining</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">parameters</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'mu'</span><span class="p">:</span> <span class="n">mu</span><span class="p">,</span> <span class="s1">'sigma'</span><span class="p">:</span> <span class="n">sigma</span><span class="p">}</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">components</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="s1">'dist'</span><span class="p">:</span> <span class="s1">'gaussian'</span><span class="p">,</span> <span class="s1">'type'</span><span class="p">:</span> <span class="s1">'constant'</span><span class="p">,</span>
|
||||
<span class="s1">'parameters'</span><span class="p">:</span> <span class="n">parameters</span><span class="p">,</span> <span class="s1">'args'</span><span class="p">:</span> <span class="n">kwargs</span><span class="p">})</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
<div class="viewcode-block" id="SignalEmulator.incremental_gaussian"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.SignalEmulator.incremental_gaussian">[docs]</a> <span class="k">def</span> <span class="nf">incremental_gaussian</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">sigma</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"> Creates an additive gaussian interference on a previous signal</span>
|
||||
|
||||
<span class="sd"> :param mu: increment on mean</span>
|
||||
<span class="sd"> :param sigma: increment on variance</span>
|
||||
<span class="sd"> :keyword start: lag index to start this signal, the default value is 0</span>
|
||||
<span class="sd"> :keyword it: Number of iterations, the default value is 1</span>
|
||||
<span class="sd"> :keyword length: Number of samples generated on each iteration, the default value is 100</span>
|
||||
<span class="sd"> :keyword vmin: Lower bound value of generated data, the default value is None</span>
|
||||
<span class="sd"> :keyword vmax: Upper bound value of generated data, the default value is None</span>
|
||||
<span class="sd"> :return: the current SignalEmulator instance, for method chaining</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">parameters</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'mu'</span><span class="p">:</span> <span class="n">mu</span><span class="p">,</span> <span class="s1">'sigma'</span><span class="p">:</span> <span class="n">sigma</span><span class="p">}</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">components</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="s1">'dist'</span><span class="p">:</span> <span class="s1">'gaussian'</span><span class="p">,</span> <span class="s1">'type'</span><span class="p">:</span> <span class="s1">'incremental'</span><span class="p">,</span>
|
||||
<span class="s1">'parameters'</span><span class="p">:</span> <span class="n">parameters</span><span class="p">,</span> <span class="s1">'args'</span><span class="p">:</span> <span class="n">kwargs</span><span class="p">})</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
<div class="viewcode-block" id="SignalEmulator.periodic_gaussian"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.SignalEmulator.periodic_gaussian">[docs]</a> <span class="k">def</span> <span class="nf">periodic_gaussian</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">type</span><span class="p">,</span> <span class="n">period</span><span class="p">,</span> <span class="n">mu_min</span><span class="p">,</span> <span class="n">sigma_min</span><span class="p">,</span> <span class="n">mu_max</span><span class="p">,</span> <span class="n">sigma_max</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"> Creates an additive periodic gaussian interference on a previous signal</span>
|
||||
|
||||
<span class="sd"> :param type: 'linear' or 'sinoidal'</span>
|
||||
<span class="sd"> :param period: the period of recurrence</span>
|
||||
<span class="sd"> :param mu: increment on mean</span>
|
||||
<span class="sd"> :param sigma: increment on variance</span>
|
||||
<span class="sd"> :keyword start: lag index to start this signal, the default value is 0</span>
|
||||
<span class="sd"> :keyword it: Number of iterations, the default value is 1</span>
|
||||
<span class="sd"> :keyword length: Number of samples generated on each iteration, the default value is 100</span>
|
||||
<span class="sd"> :keyword vmin: Lower bound value of generated data, the default value is None</span>
|
||||
<span class="sd"> :keyword vmax: Upper bound value of generated data, the default value is None</span>
|
||||
<span class="sd"> :return: the current SignalEmulator instance, for method chaining</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">parameters</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'type'</span><span class="p">:</span><span class="nb">type</span><span class="p">,</span> <span class="s1">'period'</span><span class="p">:</span><span class="n">period</span><span class="p">,</span>
|
||||
<span class="s1">'mu_min'</span><span class="p">:</span> <span class="n">mu_min</span><span class="p">,</span> <span class="s1">'sigma_min'</span><span class="p">:</span> <span class="n">sigma_min</span><span class="p">,</span> <span class="s1">'mu_max'</span><span class="p">:</span> <span class="n">mu_max</span><span class="p">,</span> <span class="s1">'sigma_max'</span><span class="p">:</span> <span class="n">sigma_max</span><span class="p">}</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">components</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="s1">'dist'</span><span class="p">:</span> <span class="s1">'gaussian'</span><span class="p">,</span> <span class="s1">'type'</span><span class="p">:</span> <span class="s1">'periodic'</span><span class="p">,</span>
|
||||
<span class="s1">'parameters'</span><span class="p">:</span> <span class="n">parameters</span><span class="p">,</span> <span class="s1">'args'</span><span class="p">:</span> <span class="n">kwargs</span><span class="p">})</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
<div class="viewcode-block" id="SignalEmulator.blip"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.SignalEmulator.blip">[docs]</a> <span class="k">def</span> <span class="nf">blip</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"> Creates an outlier greater than the maximum or lower then the minimum previous values of the signal,</span>
|
||||
<span class="sd"> and insert it on a random location of the signal.</span>
|
||||
|
||||
<span class="sd"> :return: the current SignalEmulator instance, for method chaining</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">parameters</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">components</span><span class="o">.</span><span class="n">append</span><span class="p">({</span><span class="s1">'dist'</span><span class="p">:</span> <span class="s1">'blip'</span><span class="p">,</span> <span class="s1">'type'</span><span class="p">:</span> <span class="s1">'blip'</span><span class="p">,</span>
|
||||
<span class="s1">'parameters'</span><span class="p">:</span> <span class="n">parameters</span><span class="p">,</span> <span class="s1">'args'</span><span class="p">:</span><span class="n">kwargs</span><span class="p">})</span>
|
||||
<span class="k">return</span> <span class="bp">self</span></div>
|
||||
|
||||
<div class="viewcode-block" id="SignalEmulator.run"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.SignalEmulator.run">[docs]</a> <span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Render the signal</span>
|
||||
|
||||
<span class="sd"> :return: a list of float values</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">signal</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">last_it</span> <span class="o">=</span> <span class="mi">10</span>
|
||||
<span class="n">last_num</span> <span class="o">=</span> <span class="mi">10</span>
|
||||
<span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">component</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">components</span><span class="p">):</span>
|
||||
<span class="n">parameters</span> <span class="o">=</span> <span class="n">component</span><span class="p">[</span><span class="s1">'parameters'</span><span class="p">]</span>
|
||||
<span class="n">kwargs</span> <span class="o">=</span> <span class="n">component</span><span class="p">[</span><span class="s1">'args'</span><span class="p">]</span>
|
||||
<span class="n">additive</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">'additive'</span><span class="p">,</span> <span class="kc">True</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="mi">0</span><span class="p">)</span>
|
||||
<span class="n">it</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">'it'</span><span class="p">,</span> <span class="n">last_it</span><span class="p">)</span>
|
||||
<span class="n">num</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">'length'</span><span class="p">,</span> <span class="n">last_num</span><span class="p">)</span>
|
||||
<span class="n">vmin</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">'vmin'</span><span class="p">,</span><span class="kc">None</span><span class="p">)</span>
|
||||
<span class="n">vmax</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">'vmax'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">component</span><span class="p">[</span><span class="s1">'type'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'constant'</span><span class="p">:</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="n">generate_gaussian_linear</span><span class="p">(</span><span class="n">parameters</span><span class="p">[</span><span class="s1">'mu'</span><span class="p">],</span> <span class="n">parameters</span><span class="p">[</span><span class="s1">'sigma'</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">it</span><span class="o">=</span><span class="n">it</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="n">num</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="n">vmin</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="n">vmax</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">component</span><span class="p">[</span><span class="s1">'type'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'incremental'</span><span class="p">:</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="n">generate_gaussian_linear</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">parameters</span><span class="p">[</span><span class="s1">'mu'</span><span class="p">],</span> <span class="n">parameters</span><span class="p">[</span><span class="s1">'sigma'</span><span class="p">],</span>
|
||||
<span class="n">it</span><span class="o">=</span><span class="n">num</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="n">vmin</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="n">vmax</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">component</span><span class="p">[</span><span class="s1">'type'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'periodic'</span><span class="p">:</span>
|
||||
<span class="n">period</span> <span class="o">=</span> <span class="n">parameters</span><span class="p">[</span><span class="s1">'period'</span><span class="p">]</span>
|
||||
<span class="n">mu_min</span><span class="p">,</span> <span class="n">sigma_min</span> <span class="o">=</span> <span class="n">parameters</span><span class="p">[</span><span class="s1">'mu_min'</span><span class="p">],</span><span class="n">parameters</span><span class="p">[</span><span class="s1">'sigma_min'</span><span class="p">]</span>
|
||||
<span class="n">mu_max</span><span class="p">,</span> <span class="n">sigma_max</span> <span class="o">=</span> <span class="n">parameters</span><span class="p">[</span><span class="s1">'mu_max'</span><span class="p">],</span><span class="n">parameters</span><span class="p">[</span><span class="s1">'sigma_max'</span><span class="p">]</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">parameters</span><span class="p">[</span><span class="s1">'type'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'sinoidal'</span><span class="p">:</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="n">generate_sinoidal_periodic_gaussian</span><span class="p">(</span><span class="n">period</span><span class="p">,</span> <span class="n">mu_min</span><span class="p">,</span> <span class="n">sigma_min</span><span class="p">,</span> <span class="n">mu_max</span><span class="p">,</span> <span class="n">sigma_max</span><span class="p">,</span>
|
||||
<span class="n">it</span><span class="o">=</span><span class="n">num</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="n">vmin</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="n">vmax</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">generate_linear_periodic_gaussian</span><span class="p">(</span><span class="n">period</span><span class="p">,</span> <span class="n">mu_min</span><span class="p">,</span> <span class="n">sigma_min</span><span class="p">,</span> <span class="n">mu_max</span><span class="p">,</span> <span class="n">sigma_max</span><span class="p">,</span>
|
||||
<span class="n">it</span><span class="o">=</span><span class="n">num</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="n">vmin</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="n">vmax</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">component</span><span class="p">[</span><span class="s1">'type'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'blip'</span><span class="p">:</span>
|
||||
<span class="n">_mx</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">signal</span><span class="p">)</span>
|
||||
<span class="n">_mn</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">signal</span><span class="p">)</span>
|
||||
|
||||
<span class="n">_mx</span> <span class="o">+=</span> <span class="mi">2</span><span class="o">*</span><span class="n">_mx</span> <span class="k">if</span> <span class="n">_mx</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="o">-</span><span class="mi">2</span><span class="o">*</span><span class="n">_mx</span>
|
||||
<span class="n">_mn</span> <span class="o">+=</span> <span class="o">-</span><span class="mi">2</span><span class="o">*</span><span class="n">_mn</span> <span class="k">if</span> <span class="n">_mn</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">2</span><span class="o">*</span><span class="n">_mn</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">vmax</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">_mx</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">_mx</span><span class="p">,</span> <span class="n">vmax</span><span class="p">)</span> <span class="k">if</span> <span class="n">vmax</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="nb">max</span><span class="p">(</span><span class="n">_mx</span><span class="p">,</span> <span class="n">vmax</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">vmin</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">_mn</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">_mn</span><span class="p">,</span> <span class="n">vmin</span><span class="p">)</span> <span class="k">if</span> <span class="n">vmin</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="nb">min</span><span class="p">(</span><span class="n">_mn</span><span class="p">,</span> <span class="n">vmin</span><span class="p">)</span>
|
||||
|
||||
<span class="n">start</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">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">signal</span><span class="p">))</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="n">_mx</span><span class="p">]</span> <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">()</span> <span class="o">>=</span> <span class="o">.</span><span class="mi">5</span> <span class="k">else</span> <span class="p">[</span><span class="o">-</span><span class="n">_mn</span><span class="p">]</span>
|
||||
|
||||
<span class="n">last_num</span> <span class="o">=</span> <span class="n">num</span>
|
||||
<span class="n">last_it</span> <span class="o">=</span> <span class="n">it</span>
|
||||
|
||||
<span class="n">signal</span> <span class="o">=</span> <span class="n">_append</span><span class="p">(</span><span class="n">additive</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">signal</span><span class="p">,</span> <span class="n">tmp</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">signal</span></div></div>
|
||||
|
||||
|
||||
|
||||
|
||||
<div class="viewcode-block" id="generate_gaussian_linear"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.generate_gaussian_linear">[docs]</a><span class="k">def</span> <span class="nf">generate_gaussian_linear</span><span class="p">(</span><span class="n">mu_ini</span><span class="p">,</span> <span class="n">sigma_ini</span><span class="p">,</span> <span class="n">mu_inc</span><span class="p">,</span> <span class="n">sigma_inc</span><span class="p">,</span> <span class="n">it</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Generate data sampled from Gaussian distribution, with constant or linear changing parameters</span>
|
||||
@ -108,6 +250,88 @@
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="generate_linear_periodic_gaussian"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.generate_linear_periodic_gaussian">[docs]</a><span class="k">def</span> <span class="nf">generate_linear_periodic_gaussian</span><span class="p">(</span><span class="n">period</span><span class="p">,</span> <span class="n">mu_min</span><span class="p">,</span> <span class="n">sigma_min</span><span class="p">,</span> <span class="n">mu_max</span><span class="p">,</span> <span class="n">sigma_max</span><span class="p">,</span> <span class="n">it</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Generates a periodic linear variation on mean and variance</span>
|
||||
|
||||
<span class="sd"> :param period: the period of recurrence</span>
|
||||
<span class="sd"> :param mu_min: initial (and minimum) mean of each period</span>
|
||||
<span class="sd"> :param sigma_min: initial (and minimum) variance of each period</span>
|
||||
<span class="sd"> :param mu_max: final (and maximum) mean of each period</span>
|
||||
<span class="sd"> :param sigma_max: final (and maximum) variance of each period</span>
|
||||
<span class="sd"> :param it: Number of iterations</span>
|
||||
<span class="sd"> :param num: Number of samples generated on each iteration</span>
|
||||
<span class="sd"> :param vmin: Lower bound value of generated data</span>
|
||||
<span class="sd"> :param vmax: Upper bound value of generated data</span>
|
||||
<span class="sd"> :return: A list of it*num float values</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">period</span> <span class="o">></span> <span class="n">it</span><span class="p">:</span>
|
||||
<span class="k">raise</span><span class="p">(</span><span class="s2">"The 'period' parameter must be lesser than 'it' parameter"</span><span class="p">)</span>
|
||||
|
||||
<span class="n">mu_inc</span> <span class="o">=</span> <span class="p">(</span><span class="n">mu_max</span> <span class="o">-</span> <span class="n">mu_min</span><span class="p">)</span><span class="o">/</span><span class="n">period</span>
|
||||
<span class="n">sigma_inc</span> <span class="o">=</span> <span class="p">(</span><span class="n">sigma_max</span> <span class="o">-</span> <span class="n">sigma_min</span><span class="p">)</span> <span class="o">/</span> <span class="n">period</span>
|
||||
<span class="n">mu</span> <span class="o">=</span> <span class="n">mu_min</span>
|
||||
<span class="n">sigma</span> <span class="o">=</span> <span class="n">sigma_min</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">signal</span> <span class="o">=</span> <span class="kc">True</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">it</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">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">sigma</span><span class="p">,</span> <span class="n">num</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">vmin</span> <span class="ow">is</span> <span class="ow">not</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">maximum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">num</span><span class="p">,</span> <span class="n">vmin</span><span class="p">),</span> <span class="n">tmp</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">vmax</span> <span class="ow">is</span> <span class="ow">not</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">minimum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">num</span><span class="p">,</span> <span class="n">vmax</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">extend</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">k</span> <span class="o">%</span> <span class="n">period</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">signal</span> <span class="o">=</span> <span class="ow">not</span> <span class="n">signal</span>
|
||||
|
||||
<span class="n">mu</span> <span class="o">+=</span> <span class="p">(</span><span class="n">mu_inc</span> <span class="k">if</span> <span class="n">signal</span> <span class="k">else</span> <span class="o">-</span><span class="n">mu_inc</span><span class="p">)</span>
|
||||
<span class="n">sigma</span> <span class="o">+=</span> <span class="p">(</span><span class="n">sigma_inc</span> <span class="k">if</span> <span class="n">signal</span> <span class="k">else</span> <span class="o">-</span><span class="n">sigma_inc</span><span class="p">)</span>
|
||||
|
||||
<span class="n">sigma</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">sigma</span><span class="p">,</span> <span class="mf">0.005</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="generate_sinoidal_periodic_gaussian"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.generate_sinoidal_periodic_gaussian">[docs]</a><span class="k">def</span> <span class="nf">generate_sinoidal_periodic_gaussian</span><span class="p">(</span><span class="n">period</span><span class="p">,</span> <span class="n">mu_min</span><span class="p">,</span> <span class="n">sigma_min</span><span class="p">,</span> <span class="n">mu_max</span><span class="p">,</span> <span class="n">sigma_max</span><span class="p">,</span> <span class="n">it</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Generates a periodic sinoidal variation on mean and variance</span>
|
||||
|
||||
<span class="sd"> :param period: the period of recurrence</span>
|
||||
<span class="sd"> :param mu_min: initial (and minimum) mean of each period</span>
|
||||
<span class="sd"> :param sigma_min: initial (and minimum) variance of each period</span>
|
||||
<span class="sd"> :param mu_max: final (and maximum) mean of each period</span>
|
||||
<span class="sd"> :param sigma_max: final (and maximum) variance of each period</span>
|
||||
<span class="sd"> :param it: Number of iterations</span>
|
||||
<span class="sd"> :param num: Number of samples generated on each iteration</span>
|
||||
<span class="sd"> :param vmin: Lower bound value of generated data</span>
|
||||
<span class="sd"> :param vmax: Upper bound value of generated data</span>
|
||||
<span class="sd"> :return: A list of it*num float values</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">mu_range</span> <span class="o">=</span> <span class="n">mu_max</span> <span class="o">-</span> <span class="n">mu_min</span>
|
||||
<span class="n">sigma_range</span> <span class="o">=</span> <span class="n">sigma_max</span> <span class="o">-</span> <span class="n">sigma_min</span>
|
||||
<span class="n">mu</span> <span class="o">=</span> <span class="n">mu_min</span>
|
||||
<span class="n">sigma</span> <span class="o">=</span> <span class="n">sigma_min</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">it</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">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">sigma</span><span class="p">,</span> <span class="n">num</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">vmin</span> <span class="ow">is</span> <span class="ow">not</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">maximum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">num</span><span class="p">,</span> <span class="n">vmin</span><span class="p">),</span> <span class="n">tmp</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">vmax</span> <span class="ow">is</span> <span class="ow">not</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">minimum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">num</span><span class="p">,</span> <span class="n">vmax</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">extend</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
|
||||
|
||||
<span class="n">mu</span> <span class="o">+=</span> <span class="n">mu_range</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">period</span> <span class="o">*</span> <span class="n">k</span><span class="p">)</span>
|
||||
<span class="n">sigma</span> <span class="o">+=</span> <span class="n">sigma_range</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">period</span> <span class="o">*</span> <span class="n">k</span><span class="p">)</span>
|
||||
|
||||
<span class="n">sigma</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">sigma</span><span class="p">,</span> <span class="mf">0.005</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="generate_uniform_linear"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.generate_uniform_linear">[docs]</a><span class="k">def</span> <span class="nf">generate_uniform_linear</span><span class="p">(</span><span class="n">min_ini</span><span class="p">,</span> <span class="n">max_ini</span><span class="p">,</span> <span class="n">min_inc</span><span class="p">,</span> <span class="n">max_inc</span><span class="p">,</span> <span class="n">it</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Generate data sampled from Uniform distribution, with constant or linear changing bounds</span>
|
||||
@ -138,10 +362,22 @@
|
||||
|
||||
|
||||
<div class="viewcode-block" id="white_noise"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.white_noise">[docs]</a><span class="k">def</span> <span class="nf">white_noise</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">500</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Simple Gaussian noise signal</span>
|
||||
<span class="sd"> :param n: number of samples</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</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="n">n</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="random_walk"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.random_walk">[docs]</a><span class="k">def</span> <span class="nf">random_walk</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s1">'gaussian'</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Simple random walk</span>
|
||||
<span class="sd"> </span>
|
||||
<span class="sd"> :param n: number of samples</span>
|
||||
<span class="sd"> :param type: 'gaussian' or 'uniform'</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">'gaussian'</span><span class="p">:</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="n">generate_gaussian_linear</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="mi">0</span><span class="p">,</span> <span class="n">it</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="n">n</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
@ -152,6 +388,30 @@
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">_append</span><span class="p">(</span><span class="n">additive</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">before</span><span class="p">,</span> <span class="n">new</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">additive</span><span class="p">:</span>
|
||||
<span class="n">before</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="k">return</span> <span class="n">before</span>
|
||||
<span class="k">else</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">start</span><span class="p">):</span>
|
||||
<span class="n">new</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="mi">0</span><span class="p">)</span>
|
||||
|
||||
<span class="n">l1</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">before</span><span class="p">)</span>
|
||||
<span class="n">l2</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">new</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">l2</span> <span class="o"><</span> <span class="n">l1</span><span class="p">:</span>
|
||||
<span class="n">new</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">l1</span> <span class="o">-</span> <span class="n">l2</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">())</span>
|
||||
<span class="k">elif</span> <span class="mi">0</span> <span class="o"><</span> <span class="n">l1</span> <span class="o"><</span> <span class="n">l2</span><span class="p">:</span>
|
||||
<span class="n">new</span> <span class="o">=</span> <span class="n">new</span><span class="p">[:</span><span class="n">l1</span><span class="p">]</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">before</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">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">new</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">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">before</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">new</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">tmp</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
|
||||
|
||||
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
|
||||
432
docs/build/html/_modules/pyFTS/distributed/spark.html
vendored
Normal file
432
docs/build/html/_modules/pyFTS/distributed/spark.html
vendored
Normal file
@ -0,0 +1,432 @@
|
||||
|
||||
|
||||
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|
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|
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<h1>Source code for pyFTS.distributed.spark</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.data</span> <span class="k">import</span> <span class="n">Enrollments</span><span class="p">,</span> <span class="n">TAIEX</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">Simple</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.models.multivariate</span> <span class="k">import</span> <span class="n">partitioner</span> <span class="k">as</span> <span class="n">mv_partitioner</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">pyspark</span> <span class="k">import</span> <span class="n">SparkConf</span>
|
||||
<span class="kn">from</span> <span class="nn">pyspark</span> <span class="k">import</span> <span class="n">SparkContext</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">os</span>
|
||||
<span class="c1"># make sure pyspark tells workers to use python3 not 2 if both are installed</span>
|
||||
<span class="n">SPARK_ADDR</span> <span class="o">=</span> <span class="s1">'spark://192.168.0.110:7077'</span>
|
||||
|
||||
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'PYSPARK_PYTHON'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'/usr/bin/python3'</span>
|
||||
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">'PYSPARK_DRIVER_PYTHON'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'/usr/bin/python3'</span>
|
||||
|
||||
<div class="viewcode-block" id="create_spark_conf"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.spark.create_spark_conf">[docs]</a><span class="k">def</span> <span class="nf">create_spark_conf</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<span class="n">spark_executor_memory</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">"spark_executor_memory"</span><span class="p">,</span> <span class="s2">"2g"</span><span class="p">)</span>
|
||||
<span class="n">spark_driver_memory</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">"spark_driver_memory"</span><span class="p">,</span> <span class="s2">"2g"</span><span class="p">)</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="s2">"url"</span><span class="p">,</span> <span class="n">SPARK_ADDR</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="s2">"app"</span><span class="p">,</span> <span class="s1">'pyFTS'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">conf</span> <span class="o">=</span> <span class="n">SparkConf</span><span class="p">()</span>
|
||||
<span class="n">conf</span><span class="o">.</span><span class="n">setMaster</span><span class="p">(</span><span class="n">url</span><span class="p">)</span>
|
||||
<span class="n">conf</span><span class="o">.</span><span class="n">setAppName</span><span class="p">(</span><span class="n">app</span><span class="p">)</span>
|
||||
<span class="n">conf</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="s2">"spark.executor.memory"</span><span class="p">,</span> <span class="n">spark_executor_memory</span><span class="p">)</span>
|
||||
<span class="n">conf</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="s2">"spark.driver.memory"</span><span class="p">,</span> <span class="n">spark_driver_memory</span><span class="p">)</span>
|
||||
<span class="n">conf</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="s2">"spark.memory.offHeap.enabled"</span><span class="p">,</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">conf</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="s2">"spark.memory.offHeap.size"</span><span class="p">,</span><span class="s2">"16g"</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">conf</span></div>
|
||||
|
||||
<div class="viewcode-block" id="get_partitioner"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.spark.get_partitioner">[docs]</a><span class="k">def</span> <span class="nf">get_partitioner</span><span class="p">(</span><span class="n">shared_partitioner</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s1">'common'</span><span class="p">,</span> <span class="n">variables</span><span class="o">=</span><span class="p">[]):</span>
|
||||
<span class="sd">"""</span>
|
||||
|
||||
<span class="sd"> :param part:</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="nb">type</span><span class="o">==</span><span class="s1">'common'</span><span class="p">:</span>
|
||||
<span class="n">fs_tmp</span> <span class="o">=</span> <span class="n">Simple</span><span class="o">.</span><span class="n">SimplePartitioner</span><span class="p">()</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">fset</span> <span class="ow">in</span> <span class="n">shared_partitioner</span><span class="o">.</span><span class="n">value</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
|
||||
<span class="n">fz</span> <span class="o">=</span> <span class="n">shared_partitioner</span><span class="o">.</span><span class="n">value</span><span class="p">[</span><span class="n">fset</span><span class="p">]</span>
|
||||
<span class="k">if</span> <span class="nb">type</span><span class="o">==</span><span class="s1">'common'</span><span class="p">:</span>
|
||||
<span class="n">fs_tmp</span><span class="o">.</span><span class="n">append_complex</span><span class="p">(</span><span class="n">fz</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">fs_tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fz</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">fs_tmp</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="get_clustered_partitioner"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.spark.get_clustered_partitioner">[docs]</a><span class="k">def</span> <span class="nf">get_clustered_partitioner</span><span class="p">(</span><span class="n">explanatory_variables</span><span class="p">,</span> <span class="n">target_variable</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">):</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.models.multivariate.common</span> <span class="k">import</span> <span class="n">MultivariateFuzzySet</span>
|
||||
<span class="n">fs_tmp</span> <span class="o">=</span> <span class="n">mv_partitioner</span><span class="o">.</span><span class="n">MultivariatePartitioner</span><span class="p">(</span><span class="n">explanatory_variables</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="n">target_variable</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">tmp</span> <span class="ow">in</span> <span class="n">parameters</span><span class="p">[</span><span class="s1">'partitioner_names'</span><span class="p">]</span><span class="o">.</span><span class="n">value</span><span class="p">:</span>
|
||||
<span class="n">fs</span> <span class="o">=</span> <span class="n">MultivariateFuzzySet</span><span class="p">(</span><span class="n">target_variable</span><span class="o">=</span><span class="n">target_variable</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">var</span><span class="p">,</span> <span class="n">fset</span> <span class="ow">in</span> <span class="n">parameters</span><span class="p">[</span><span class="s1">'partitioner_</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">tmp</span><span class="p">)]</span><span class="o">.</span><span class="n">value</span><span class="p">:</span>
|
||||
<span class="n">fs</span><span class="o">.</span><span class="n">append_set</span><span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">fset</span><span class="p">)</span>
|
||||
<span class="n">fs_tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fs</span><span class="p">)</span>
|
||||
|
||||
<span class="n">fs_tmp</span><span class="o">.</span><span class="n">build_index</span><span class="p">()</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">fs_tmp</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="get_variables"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.spark.get_variables">[docs]</a><span class="k">def</span> <span class="nf">get_variables</span><span class="p">(</span><span class="o">**</span><span class="n">parameters</span><span class="p">):</span>
|
||||
<span class="n">explanatory_variables</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">target_variable</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">parameters</span><span class="p">[</span><span class="s1">'variables'</span><span class="p">]</span><span class="o">.</span><span class="n">value</span><span class="p">:</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.models.multivariate</span> <span class="k">import</span> <span class="n">common</span><span class="p">,</span> <span class="n">variable</span>
|
||||
<span class="n">var</span> <span class="o">=</span> <span class="n">variable</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">name</span><span class="p">,</span>
|
||||
<span class="nb">type</span><span class="o">=</span><span class="n">parameters</span><span class="p">[</span><span class="s1">'</span><span class="si">{}</span><span class="s1">_type'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="p">)]</span><span class="o">.</span><span class="n">value</span><span class="p">,</span>
|
||||
<span class="n">data_label</span><span class="o">=</span><span class="n">parameters</span><span class="p">[</span><span class="s1">'</span><span class="si">{}</span><span class="s1">_label'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="p">)]</span><span class="o">.</span><span class="n">value</span><span class="p">,</span>
|
||||
<span class="n">alpha_cut</span><span class="o">=</span><span class="n">parameters</span><span class="p">[</span><span class="s1">'</span><span class="si">{}</span><span class="s1">_alpha'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="p">)]</span><span class="o">.</span><span class="n">value</span><span class="p">,</span>
|
||||
<span class="c1">#data_type=parameters['{}_data_type'.format(name)].value,</span>
|
||||
<span class="c1">#mask=parameters['{}_mask'.format(name)].value,</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">get_partitioner</span><span class="p">(</span><span class="n">parameters</span><span class="p">[</span><span class="s1">'</span><span class="si">{}</span><span class="s1">_partitioner'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</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">type</span> <span class="o">=</span> <span class="n">parameters</span><span class="p">[</span><span class="s1">'</span><span class="si">{}</span><span class="s1">_partitioner_type'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">name</span><span class="p">)]</span><span class="o">.</span><span class="n">value</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>
|
||||
|
||||
<span class="k">if</span> <span class="n">var</span><span class="o">.</span><span class="n">name</span> <span class="o">==</span> <span class="n">parameters</span><span class="p">[</span><span class="s1">'target'</span><span class="p">]</span><span class="o">.</span><span class="n">value</span><span class="p">:</span>
|
||||
<span class="n">target_variable</span> <span class="o">=</span> <span class="n">var</span>
|
||||
|
||||
<span class="k">return</span> <span class="p">(</span><span class="n">explanatory_variables</span><span class="p">,</span> <span class="n">target_variable</span><span class="p">)</span></div>
|
||||
|
||||
<div class="viewcode-block" id="create_univariate_model"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.spark.create_univariate_model">[docs]</a><span class="k">def</span> <span class="nf">create_univariate_model</span><span class="p">(</span><span class="o">**</span><span class="n">parameters</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="n">parameters</span><span class="p">[</span><span class="s1">'order'</span><span class="p">]</span><span class="o">.</span><span class="n">value</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">parameters</span><span class="p">[</span><span class="s1">'method'</span><span class="p">]</span><span class="o">.</span><span class="n">value</span><span class="p">(</span><span class="n">partitioner</span><span class="o">=</span><span class="n">get_partitioner</span><span class="p">(</span><span class="n">parameters</span><span class="p">[</span><span class="s1">'partitioner'</span><span class="p">]),</span>
|
||||
<span class="n">order</span><span class="o">=</span><span class="n">parameters</span><span class="p">[</span><span class="s1">'order'</span><span class="p">]</span><span class="o">.</span><span class="n">value</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="o">=</span><span class="n">parameters</span><span class="p">[</span><span class="s1">'alpha_cut'</span><span class="p">]</span><span class="o">.</span><span class="n">value</span><span class="p">,</span>
|
||||
<span class="n">lags</span><span class="o">=</span><span class="n">parameters</span><span class="p">[</span><span class="s1">'lags'</span><span class="p">]</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="n">model</span> <span class="o">=</span> <span class="n">parameters</span><span class="p">[</span><span class="s1">'method'</span><span class="p">]</span><span class="o">.</span><span class="n">value</span><span class="p">(</span><span class="n">partitioner</span><span class="o">=</span><span class="n">get_partitioner</span><span class="p">(</span><span class="n">parameters</span><span class="p">[</span><span class="s1">'partitioner'</span><span class="p">]),</span>
|
||||
<span class="n">alpha_cut</span><span class="o">=</span><span class="n">parameters</span><span class="p">[</span><span class="s1">'alpha_cut'</span><span class="p">]</span><span class="o">.</span><span class="n">value</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">model</span></div>
|
||||
|
||||
<div class="viewcode-block" id="slave_train_univariate"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.spark.slave_train_univariate">[docs]</a><span class="k">def</span> <span class="nf">slave_train_univariate</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="sd">"""</span>
|
||||
|
||||
<span class="sd"> :param data:</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">create_univariate_model</span><span class="p">(</span><span class="o">**</span><span class="n">parameters</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ndata</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">data</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">ndata</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="p">[(</span><span class="n">k</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">model</span><span class="o">.</span><span class="n">flrgs</span><span class="o">.</span><span class="n">keys</span><span class="p">()]</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="slave_forecast_univariate"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.spark.slave_forecast_univariate">[docs]</a><span class="k">def</span> <span class="nf">slave_forecast_univariate</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="sd">"""</span>
|
||||
|
||||
<span class="sd"> :param data:</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">create_univariate_model</span><span class="p">(</span><span class="o">**</span><span class="n">parameters</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ndata</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">data</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="k">return</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">forecasts</span><span class="p">]</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="create_multivariate_model"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.spark.create_multivariate_model">[docs]</a><span class="k">def</span> <span class="nf">create_multivariate_model</span><span class="p">(</span><span class="o">**</span><span class="n">parameters</span><span class="p">):</span>
|
||||
<span class="n">explanatory_variables</span><span class="p">,</span> <span class="n">target_variable</span> <span class="o">=</span> <span class="n">get_variables</span><span class="p">(</span><span class="o">**</span><span class="n">parameters</span><span class="p">)</span>
|
||||
<span class="c1">#vars = [(v.name, v.name) for v in explanatory_variables]</span>
|
||||
|
||||
<span class="c1">#return [('vars', vars), ('target',[target_variable.name])]</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">parameters</span><span class="p">[</span><span class="s1">'type'</span><span class="p">]</span><span class="o">.</span><span class="n">value</span> <span class="o">==</span> <span class="s1">'clustered'</span><span class="p">:</span>
|
||||
<span class="n">fs</span> <span class="o">=</span> <span class="n">get_clustered_partitioner</span><span class="p">(</span><span class="n">explanatory_variables</span><span class="p">,</span> <span class="n">target_variable</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">)</span>
|
||||
<span class="n">model</span> <span class="o">=</span> <span class="n">parameters</span><span class="p">[</span><span class="s1">'method'</span><span class="p">]</span><span class="o">.</span><span class="n">value</span><span class="p">(</span><span class="n">explanatory_variables</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="n">target_variable</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">parameters</span><span class="p">[</span><span class="s1">'order'</span><span class="p">]</span><span class="o">.</span><span class="n">value</span><span class="p">,</span>
|
||||
<span class="n">alpha_cut</span><span class="o">=</span><span class="n">parameters</span><span class="p">[</span><span class="s1">'alpha_cut'</span><span class="p">]</span><span class="o">.</span><span class="n">value</span><span class="p">,</span>
|
||||
<span class="n">lags</span><span class="o">=</span><span class="n">parameters</span><span class="p">[</span><span class="s1">'lags'</span><span class="p">]</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="k">if</span> <span class="n">parameters</span><span class="p">[</span><span class="s1">'order'</span><span class="p">]</span><span class="o">.</span><span class="n">value</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">parameters</span><span class="p">[</span><span class="s1">'method'</span><span class="p">]</span><span class="o">.</span><span class="n">value</span><span class="p">(</span><span class="n">explanatory_variables</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="n">target_variable</span><span class="p">,</span>
|
||||
<span class="n">order</span><span class="o">=</span><span class="n">parameters</span><span class="p">[</span><span class="s1">'order'</span><span class="p">]</span><span class="o">.</span><span class="n">value</span><span class="p">,</span>
|
||||
<span class="n">alpha_cut</span><span class="o">=</span><span class="n">parameters</span><span class="p">[</span><span class="s1">'alpha_cut'</span><span class="p">]</span><span class="o">.</span><span class="n">value</span><span class="p">,</span>
|
||||
<span class="n">lags</span><span class="o">=</span><span class="n">parameters</span><span class="p">[</span><span class="s1">'lags'</span><span class="p">]</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="n">model</span> <span class="o">=</span> <span class="n">parameters</span><span class="p">[</span><span class="s1">'method'</span><span class="p">]</span><span class="o">.</span><span class="n">value</span><span class="p">(</span><span class="n">explanatory_variables</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="n">target_variable</span><span class="p">,</span>
|
||||
<span class="n">alpha_cut</span><span class="o">=</span><span class="n">parameters</span><span class="p">[</span><span class="s1">'alpha_cut'</span><span class="p">]</span><span class="o">.</span><span class="n">value</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">model</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="slave_train_multivariate"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.spark.slave_train_multivariate">[docs]</a><span class="k">def</span> <span class="nf">slave_train_multivariate</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">model</span> <span class="o">=</span> <span class="n">create_multivariate_model</span><span class="p">(</span><span class="o">**</span><span class="n">parameters</span><span class="p">)</span>
|
||||
|
||||
<span class="n">rows</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">data</span><span class="p">]</span>
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_records</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">parameters</span><span class="p">[</span><span class="s1">'columns'</span><span class="p">]</span><span class="o">.</span><span class="n">value</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">ndata</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">parameters</span><span class="p">[</span><span class="s1">'type'</span><span class="p">]</span><span class="o">.</span><span class="n">value</span> <span class="o">==</span> <span class="s1">'clustered'</span><span class="p">:</span>
|
||||
<span class="n">counts</span> <span class="o">=</span> <span class="p">[(</span><span class="n">fset</span><span class="p">,</span> <span class="n">count</span><span class="p">)</span> <span class="k">for</span> <span class="n">fset</span><span class="p">,</span><span class="n">count</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">count</span><span class="o">.</span><span class="n">items</span><span class="p">()]</span>
|
||||
<span class="n">flrgs</span> <span class="o">=</span> <span class="p">[(</span><span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">)</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">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="k">return</span> <span class="p">[(</span><span class="s1">'counts'</span><span class="p">,</span> <span class="n">counts</span><span class="p">),</span> <span class="p">(</span><span class="s1">'flrgs'</span><span class="p">,</span> <span class="n">flrgs</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="n">k</span><span class="p">,</span> <span class="n">v</span><span class="p">)</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">model</span><span class="o">.</span><span class="n">flrgs</span><span class="o">.</span><span class="n">items</span><span class="p">()]</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="slave_forecast_multivariate"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.spark.slave_forecast_multivariate">[docs]</a><span class="k">def</span> <span class="nf">slave_forecast_multivariate</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">model</span> <span class="o">=</span> <span class="n">create_multivariate_model</span><span class="p">(</span><span class="o">**</span><span class="n">parameters</span><span class="p">)</span>
|
||||
|
||||
<span class="n">rows</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">data</span><span class="p">]</span>
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_records</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">parameters</span><span class="p">[</span><span class="s1">'columns'</span><span class="p">]</span><span class="o">.</span><span class="n">value</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="k">return</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">forecasts</span><span class="p">]</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="share_parameters"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.spark.share_parameters">[docs]</a><span class="k">def</span> <span class="nf">share_parameters</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">context</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
|
||||
<span class="n">parameters</span> <span class="o">=</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">parameters</span><span class="p">[</span><span class="s1">'type'</span><span class="p">]</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">broadcast</span><span class="p">(</span><span class="s1">'common'</span><span class="p">)</span>
|
||||
<span class="n">parameters</span><span class="p">[</span><span class="s1">'partitioner'</span><span class="p">]</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">broadcast</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">sets</span><span class="p">)</span>
|
||||
<span class="n">parameters</span><span class="p">[</span><span class="s1">'alpha_cut'</span><span class="p">]</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">broadcast</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">alpha_cut</span><span class="p">)</span>
|
||||
<span class="n">parameters</span><span class="p">[</span><span class="s1">'order'</span><span class="p">]</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">broadcast</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">parameters</span><span class="p">[</span><span class="s1">'method'</span><span class="p">]</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">broadcast</span><span class="p">(</span><span class="nb">type</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="s1">'lags'</span><span class="p">]</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">broadcast</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="n">parameters</span><span class="p">[</span><span class="s1">'max_lag'</span><span class="p">]</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">broadcast</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="k">else</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_clustered</span><span class="p">:</span>
|
||||
<span class="n">parameters</span><span class="p">[</span><span class="s1">'type'</span><span class="p">]</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">broadcast</span><span class="p">(</span><span class="s1">'clustered'</span><span class="p">)</span>
|
||||
<span class="n">names</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">fset</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">sets</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
|
||||
<span class="n">names</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
|
||||
<span class="n">parameters</span><span class="p">[</span><span class="s1">'partitioner_</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">name</span><span class="p">)]</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">broadcast</span><span class="p">([(</span><span class="n">k</span><span class="p">,</span><span class="n">v</span><span class="p">)</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">fset</span><span class="o">.</span><span class="n">sets</span><span class="o">.</span><span class="n">items</span><span class="p">()])</span>
|
||||
|
||||
<span class="n">parameters</span><span class="p">[</span><span class="s1">'partitioner_names'</span><span class="p">]</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">broadcast</span><span class="p">(</span><span class="n">names</span><span class="p">)</span>
|
||||
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">parameters</span><span class="p">[</span><span class="s1">'type'</span><span class="p">]</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">broadcast</span><span class="p">(</span><span class="s1">'multivariate'</span><span class="p">)</span>
|
||||
<span class="n">names</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="n">model</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">:</span>
|
||||
<span class="c1">#if var.data_type is None:</span>
|
||||
<span class="c1"># raise Exception("It is mandatory to inform the data_type parameter for each variable when the training is distributed! ")</span>
|
||||
<span class="n">names</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">name</span><span class="p">)</span>
|
||||
<span class="n">parameters</span><span class="p">[</span><span class="s1">'</span><span class="si">{}</span><span class="s1">_type'</span><span class="o">.</span><span class="n">format</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">context</span><span class="o">.</span><span class="n">broadcast</span><span class="p">(</span><span class="n">var</span><span class="o">.</span><span class="n">type</span><span class="p">)</span>
|
||||
<span class="c1">#parameters['{}_data_type'.format(var.name)] = context.broadcast(var.data_type)</span>
|
||||
<span class="c1">#parameters['{}_mask'.format(var.name)] = context.broadcast(var.mask)</span>
|
||||
<span class="n">parameters</span><span class="p">[</span><span class="s1">'</span><span class="si">{}</span><span class="s1">_label'</span><span class="o">.</span><span class="n">format</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">context</span><span class="o">.</span><span class="n">broadcast</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">parameters</span><span class="p">[</span><span class="s1">'</span><span class="si">{}</span><span class="s1">_alpha'</span><span class="o">.</span><span class="n">format</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">context</span><span class="o">.</span><span class="n">broadcast</span><span class="p">(</span><span class="n">var</span><span class="o">.</span><span class="n">alpha_cut</span><span class="p">)</span>
|
||||
<span class="n">parameters</span><span class="p">[</span><span class="s1">'</span><span class="si">{}</span><span class="s1">_partitioner'</span><span class="o">.</span><span class="n">format</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">context</span><span class="o">.</span><span class="n">broadcast</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">sets</span><span class="p">)</span>
|
||||
<span class="n">parameters</span><span class="p">[</span><span class="s1">'</span><span class="si">{}</span><span class="s1">_partitioner_type'</span><span class="o">.</span><span class="n">format</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">context</span><span class="o">.</span><span class="n">broadcast</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">type</span><span class="p">)</span>
|
||||
|
||||
<span class="n">parameters</span><span class="p">[</span><span class="s1">'variables'</span><span class="p">]</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">broadcast</span><span class="p">(</span><span class="n">names</span><span class="p">)</span>
|
||||
<span class="n">parameters</span><span class="p">[</span><span class="s1">'target'</span><span class="p">]</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">broadcast</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">name</span><span class="p">)</span>
|
||||
|
||||
<span class="n">parameters</span><span class="p">[</span><span class="s1">'columns'</span><span class="p">]</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">broadcast</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
|
||||
|
||||
<span class="n">parameters</span><span class="p">[</span><span class="s1">'alpha_cut'</span><span class="p">]</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">broadcast</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">alpha_cut</span><span class="p">)</span>
|
||||
<span class="n">parameters</span><span class="p">[</span><span class="s1">'order'</span><span class="p">]</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">broadcast</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">parameters</span><span class="p">[</span><span class="s1">'method'</span><span class="p">]</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">broadcast</span><span class="p">(</span><span class="nb">type</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="s1">'lags'</span><span class="p">]</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">broadcast</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="n">parameters</span><span class="p">[</span><span class="s1">'max_lag'</span><span class="p">]</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">broadcast</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="k">return</span> <span class="n">parameters</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="distributed_train"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.spark.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">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"> :param model:</span>
|
||||
<span class="sd"> :param data:</span>
|
||||
<span class="sd"> :param url:</span>
|
||||
<span class="sd"> :param app:</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">num_batches</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_batches"</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
|
||||
|
||||
<span class="n">conf</span> <span class="o">=</span> <span class="n">create_spark_conf</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<span class="k">with</span> <span class="n">SparkContext</span><span class="p">(</span><span class="n">conf</span><span class="o">=</span><span class="n">conf</span><span class="p">)</span> <span class="k">as</span> <span class="n">context</span><span class="p">:</span>
|
||||
|
||||
<span class="n">nodes</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">defaultParallelism</span>
|
||||
|
||||
<span class="n">parameters</span> <span class="o">=</span> <span class="n">share_parameters</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">context</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">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
|
||||
<span class="n">func</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">slave_train_univariate</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">)</span>
|
||||
|
||||
<span class="n">flrgs</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">parallelize</span><span class="p">(</span><span class="n">data</span><span class="p">)</span><span class="o">.</span><span class="n">repartition</span><span class="p">(</span><span class="n">nodes</span><span class="o">*</span><span class="n">num_batches</span><span class="p">)</span><span class="o">.</span><span class="n">mapPartitions</span><span class="p">(</span><span class="n">func</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">flrgs</span><span class="o">.</span><span class="n">collect</span><span class="p">():</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">append_rule</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">else</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">to_dict</span><span class="p">(</span><span class="n">orient</span><span class="o">=</span><span class="s1">'records'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">func</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">slave_train_multivariate</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">)</span>
|
||||
|
||||
<span class="n">flrgs</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">parallelize</span><span class="p">(</span><span class="n">data</span><span class="p">)</span><span class="o">.</span><span class="n">mapPartitions</span><span class="p">(</span><span class="n">func</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">flrgs</span><span class="o">.</span><span class="n">collect</span><span class="p">():</span>
|
||||
<span class="k">if</span> <span class="n">parameters</span><span class="p">[</span><span class="s1">'type'</span><span class="p">]</span><span class="o">.</span><span class="n">value</span> <span class="o">==</span> <span class="s1">'clustered'</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">k</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'counts'</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">fset</span><span class="p">,</span> <span class="n">count</span> <span class="ow">in</span> <span class="n">k</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">partitioner</span><span class="o">.</span><span class="n">count</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">elif</span> <span class="n">k</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'flrgs'</span><span class="p">:</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">append_rule</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">else</span><span class="p">:</span>
|
||||
<span class="n">model</span><span class="o">.</span><span class="n">append_rule</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">return</span> <span class="n">model</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="distributed_predict"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.spark.distributed_predict">[docs]</a><span class="k">def</span> <span class="nf">distributed_predict</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"> :param model:</span>
|
||||
<span class="sd"> :param data:</span>
|
||||
<span class="sd"> :param url:</span>
|
||||
<span class="sd"> :param app:</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="n">num_batches</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_batches"</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
|
||||
|
||||
<span class="n">conf</span> <span class="o">=</span> <span class="n">create_spark_conf</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">with</span> <span class="n">SparkContext</span><span class="p">(</span><span class="n">conf</span><span class="o">=</span><span class="n">conf</span><span class="p">)</span> <span class="k">as</span> <span class="n">context</span><span class="p">:</span>
|
||||
|
||||
<span class="n">nodes</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">defaultParallelism</span>
|
||||
|
||||
<span class="n">parameters</span> <span class="o">=</span> <span class="n">share_parameters</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">context</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">func</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">slave_forecast_univariate</span><span class="p">(</span><span class="n">x</span><span class="p">,</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">context</span><span class="o">.</span><span class="n">parallelize</span><span class="p">(</span><span class="n">data</span><span class="p">)</span><span class="o">.</span><span class="n">repartition</span><span class="p">(</span><span class="n">nodes</span> <span class="o">*</span> <span class="n">num_batches</span><span class="p">)</span><span class="o">.</span><span class="n">mapPartitions</span><span class="p">(</span><span class="n">func</span><span class="p">)</span>
|
||||
|
||||
<span class="k">else</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">to_dict</span><span class="p">(</span><span class="n">orient</span><span class="o">=</span><span class="s1">'records'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">func</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">slave_forecast_multivariate</span><span class="p">(</span><span class="n">x</span><span class="p">,</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">context</span><span class="o">.</span><span class="n">parallelize</span><span class="p">(</span><span class="n">data</span><span class="p">)</span><span class="o">.</span><span class="n">repartition</span><span class="p">(</span><span class="n">nodes</span> <span class="o">*</span> <span class="n">num_batches</span><span class="p">)</span><span class="o">.</span><span class="n">mapPartitions</span><span class="p">(</span><span class="n">func</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">forecasts</span><span class="o">.</span><span class="n">collect</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">k</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">ret</span></div>
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="clearer"></div>
|
||||
</div>
|
||||
<div class="related" role="navigation" aria-label="related navigation">
|
||||
<h3>Navigation</h3>
|
||||
<ul>
|
||||
<li class="right" style="margin-right: 10px">
|
||||
<a href="../../../genindex.html" title="General Index"
|
||||
>index</a></li>
|
||||
<li class="right" >
|
||||
<a href="../../../py-modindex.html" title="Python Module Index"
|
||||
>modules</a> |</li>
|
||||
<li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.4 documentation</a> »</li>
|
||||
<li class="nav-item nav-item-1"><a href="../../index.html" >Module code</a> »</li>
|
||||
</ul>
|
||||
</div>
|
||||
<div class="footer" role="contentinfo">
|
||||
© Copyright 2018, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
|
||||
Created using <a href="http://sphinx-doc.org/">Sphinx</a> 1.7.2.
|
||||
</div>
|
||||
</body>
|
||||
</html>
|
||||
@ -105,6 +105,7 @@
|
||||
<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>
|
||||
@ -209,7 +210,7 @@
|
||||
<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">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>
|
||||
|
||||
35
docs/build/html/_modules/pyFTS/models/hofts.html
vendored
35
docs/build/html/_modules/pyFTS/models/hofts.html
vendored
@ -83,6 +83,7 @@
|
||||
<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>
|
||||
@ -258,33 +259,44 @@
|
||||
|
||||
<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="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">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="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">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">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">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">explain</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">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">explain</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="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="n">tmp</span> <span class="o">=</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">tmp</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">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="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="si">{}</span><span class="s2"> (Naïve)</span><span class="se">\t</span><span class="s2"> Midpoint: </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="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>
|
||||
@ -292,12 +304,19 @@
|
||||
<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">tmp</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">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="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">\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">"</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"> Membership: </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="n">flrg</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">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="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">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">final</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
|
||||
|
||||
191
docs/build/html/_modules/pyFTS/models/incremental/IncrementalEnsemble.html
vendored
Normal file
191
docs/build/html/_modules/pyFTS/models/incremental/IncrementalEnsemble.html
vendored
Normal file
@ -0,0 +1,191 @@
|
||||
|
||||
|
||||
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|
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|
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<html xmlns="http://www.w3.org/1999/xhtml">
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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">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="c1">#self.max_lag = self.window_length + self.max_lag</span>
|
||||
|
||||
<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="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="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>
|
||||
<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></div>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_point_smoothing</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">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="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">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="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 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">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">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">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="k">if</span> <span class="n">k</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">k</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="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">_point_smoothing</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>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="clearer"></div>
|
||||
</div>
|
||||
<div class="related" role="navigation" aria-label="related navigation">
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<h3>Navigation</h3>
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<a href="../../../../genindex.html" title="General Index"
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>index</a></li>
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<li class="nav-item nav-item-0"><a href="../../../../index.html">pyFTS 1.4 documentation</a> »</li>
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© Copyright 2018, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
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Created using <a href="http://sphinx-doc.org/">Sphinx</a> 1.7.2.
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|
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</html>
|
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@ -17,7 +17,7 @@
|
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var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
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|
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<title>pyFTS.models.incremental.Retrainer — pyFTS 1.4 documentation</title>
|
||||
<title>pyFTS.models.incremental.TimeVariant — pyFTS 1.4 documentation</title>
|
||||
<link rel="stylesheet" href="../../../../_static/bizstyle.css" type="text/css" />
|
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<link rel="stylesheet" href="../../../../_static/pygments.css" type="text/css" />
|
||||
<script type="text/javascript" src="../../../../_static/documentation_options.js"></script>
|
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@ -71,7 +71,7 @@
|
||||
<div class="bodywrapper">
|
||||
<div class="body" role="main">
|
||||
|
||||
<h1>Source code for pyFTS.models.incremental.Retrainer</h1><div class="highlight"><pre>
|
||||
<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 the most recent data</span>
|
||||
<span class="sd">"""</span>
|
||||
@ -81,7 +81,7 @@
|
||||
<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.Retrainer.Retrainer">[docs]</a><span class="k">class</span> <span class="nc">Retrainer</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
|
||||
<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</span>
|
||||
<span class="sd"> """</span>
|
||||
@ -112,7 +112,7 @@
|
||||
<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="Retrainer.train"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.Retrainer.Retrainer.train">[docs]</a> <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<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>
|
||||
@ -121,7 +121,7 @@
|
||||
<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="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.Retrainer.Retrainer.forecast">[docs]</a> <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
<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">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>
|
||||
@ -101,7 +101,7 @@
|
||||
|
||||
<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">0.25</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>
|
||||
@ -112,7 +112,8 @@
|
||||
<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="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="n">common</span><span class="o">.</span><span class="n">fuzzyfy_instance_clustered</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">partitioner</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">ndata</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">fuzzyfy_instance_clustered</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">partitioner</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">return</span> <span class="n">ndata</span></div>
|
||||
|
||||
|
||||
@ -116,9 +116,12 @@
|
||||
<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmin</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span></div></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="fuzzyfy_instance"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.common.fuzzyfy_instance">[docs]</a><span class="k">def</span> <span class="nf">fuzzyfy_instance</span><span class="p">(</span><span class="n">data_point</span><span class="p">,</span> <span class="n">var</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="fuzzyfy_instance"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.common.fuzzyfy_instance">[docs]</a><span class="k">def</span> <span class="nf">fuzzyfy_instance</span><span class="p">(</span><span class="n">data_point</span><span class="p">,</span> <span class="n">var</span><span class="p">,</span> <span class="n">tuples</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="n">fsets</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">data_point</span><span class="p">,</span> <span class="n">var</span><span class="o">.</span><span class="n">partitioner</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">'sets'</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">'fuzzy'</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="o">=</span><span class="n">var</span><span class="o">.</span><span class="n">alpha_cut</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="p">[(</span><span class="n">var</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">fs</span><span class="p">)</span> <span class="k">for</span> <span class="n">fs</span> <span class="ow">in</span> <span class="n">fsets</span><span class="p">]</span></div>
|
||||
<span class="k">if</span> <span class="n">tuples</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="p">[(</span><span class="n">var</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">fs</span><span class="p">)</span> <span class="k">for</span> <span class="n">fs</span> <span class="ow">in</span> <span class="n">fsets</span><span class="p">]</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">fsets</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="fuzzyfy_instance_clustered"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.common.fuzzyfy_instance_clustered">[docs]</a><span class="k">def</span> <span class="nf">fuzzyfy_instance_clustered</span><span class="p">(</span><span class="n">data_point</span><span class="p">,</span> <span class="n">cluster</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
|
||||
|
||||
@ -72,14 +72,28 @@
|
||||
<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="p">,</span> <span class="n">tree</span>
|
||||
<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">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>
|
||||
@ -113,11 +127,15 @@
|
||||
<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">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">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="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">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="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>
|
||||
|
||||
@ -125,23 +143,19 @@
|
||||
<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">data_label</span><span class="p">]</span>
|
||||
<span class="n">lags</span><span class="p">[</span><span class="n">vc</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">root</span> <span class="o">=</span> <span class="n">tree</span><span class="o">.</span><span class="n">FLRGTreeNode</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
|
||||
|
||||
<span class="n">tree</span><span class="o">.</span><span class="n">build_tree_without_order</span><span class="p">(</span><span class="n">root</span><span class="p">,</span> <span class="n">lags</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">root</span><span class="o">.</span><span class="n">paths</span><span class="p">():</span>
|
||||
<span class="n">path</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">reversed</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">filter</span><span class="p">(</span><span class="kc">None</span><span class="o">.</span><span class="fm">__ne__</span><span class="p">,</span> <span class="n">p</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="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">path</span><span class="p">:</span>
|
||||
<span class="n">flr</span><span class="o">.</span><span class="n">set_lhs</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">s</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">var</span><span class="p">,</span> <span class="n">fset</span> <span class="ow">in</span> <span class="n">path</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
|
||||
<span class="n">flr</span><span class="o">.</span><span class="n">set_lhs</span><span class="p">(</span><span class="n">var</span><span class="p">,</span> <span class="n">fset</span><span class="p">)</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">else</span><span class="p">:</span>
|
||||
<span class="nb">print</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>
|
||||
|
||||
@ -149,7 +163,7 @@
|
||||
<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="nb">range</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="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="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>
|
||||
|
||||
@ -184,17 +198,28 @@
|
||||
<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="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="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">row</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">mvs</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_membership</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">row</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">mvs</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_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="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">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>
|
||||
@ -211,9 +236,10 @@
|
||||
|
||||
<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 variables names (except the target_variable) and the values are '</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.'</span><span class="p">)</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>
|
||||
|
||||
@ -228,13 +254,20 @@
|
||||
|
||||
<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">last_data_point</span> <span class="o">=</span> <span class="n">sample</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">sample</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="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="k">if</span> <span class="n">var</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">target_variable</span><span class="o">.</span><span class="n">name</span><span class="p">:</span>
|
||||
<span class="n">new_data_point</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">generators</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">last_data_point</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">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">sample</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">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">ndata</span><span class="o">.</span><span class="n">loc</span><span class="p">[[</span><span class="n">sample</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</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">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>
|
||||
|
||||
|
||||
@ -104,9 +104,13 @@
|
||||
<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>
|
||||
|
||||
@ -77,12 +77,18 @@
|
||||
<span class="kn">from</span> <span class="nn">enum</span> <span class="k">import</span> <span class="n">Enum</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">Membership</span>
|
||||
<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="k">import</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">Grid</span>
|
||||
<span class="kn">from</span> <span class="nn">datetime</span> <span class="k">import</span> <span class="n">date</span> <span class="k">as</span> <span class="n">dt</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">datetime</span> <span class="k">import</span> <span class="n">date</span> <span class="k">as</span> <span class="n">dt</span><span class="p">,</span> <span class="n">datetime</span> <span class="k">as</span> <span class="n">dtm</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="DateTime"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime">[docs]</a><span class="k">class</span> <span class="nc">DateTime</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Data and Time granularity for time granularity and seasonality identification</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">year</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="n">half</span> <span class="o">=</span> <span class="mi">2</span> <span class="c1"># six months</span>
|
||||
<span class="n">third</span> <span class="o">=</span> <span class="mi">3</span> <span class="c1"># four months</span>
|
||||
<span class="n">quarter</span> <span class="o">=</span> <span class="mi">4</span> <span class="c1"># three months</span>
|
||||
<span class="n">sixth</span> <span class="o">=</span> <span class="mi">6</span> <span class="c1"># two months</span>
|
||||
<span class="n">month</span> <span class="o">=</span> <span class="mi">12</span>
|
||||
<span class="n">day_of_month</span> <span class="o">=</span> <span class="mi">30</span>
|
||||
<span class="n">day_of_year</span> <span class="o">=</span> <span class="mi">364</span>
|
||||
@ -104,11 +110,15 @@
|
||||
<span class="n">second_of_day</span> <span class="o">=</span> <span class="mi">86400</span></div>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="strip_datepart"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.strip_datepart">[docs]</a><span class="k">def</span> <span class="nf">strip_datepart</span><span class="p">(</span><span class="n">date</span><span class="p">,</span> <span class="n">date_part</span><span class="p">):</span>
|
||||
<div class="viewcode-block" id="strip_datepart"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.strip_datepart">[docs]</a><span class="k">def</span> <span class="nf">strip_datepart</span><span class="p">(</span><span class="n">date</span><span class="p">,</span> <span class="n">date_part</span><span class="p">,</span> <span class="n">mask</span><span class="o">=</span><span class="s1">''</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">date</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
|
||||
<span class="n">date</span> <span class="o">=</span> <span class="n">dtm</span><span class="o">.</span><span class="n">strptime</span><span class="p">(</span><span class="n">date</span><span class="p">,</span> <span class="n">mask</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">date_part</span> <span class="o">==</span> <span class="n">DateTime</span><span class="o">.</span><span class="n">year</span><span class="p">:</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="n">date</span><span class="o">.</span><span class="n">year</span>
|
||||
<span class="k">elif</span> <span class="n">date_part</span> <span class="o">==</span> <span class="n">DateTime</span><span class="o">.</span><span class="n">month</span><span class="p">:</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="n">date</span><span class="o">.</span><span class="n">month</span>
|
||||
<span class="k">elif</span> <span class="n">date_part</span> <span class="ow">in</span> <span class="p">(</span><span class="n">DateTime</span><span class="o">.</span><span class="n">half</span><span class="p">,</span> <span class="n">DateTime</span><span class="o">.</span><span class="n">third</span><span class="p">,</span> <span class="n">DateTime</span><span class="o">.</span><span class="n">quarter</span><span class="p">,</span> <span class="n">DateTime</span><span class="o">.</span><span class="n">sixth</span><span class="p">):</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="p">(</span><span class="n">date</span><span class="o">.</span><span class="n">month</span> <span class="o">//</span> <span class="n">date_part</span><span class="o">.</span><span class="n">value</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
|
||||
<span class="k">elif</span> <span class="n">date_part</span> <span class="o">==</span> <span class="n">DateTime</span><span class="o">.</span><span class="n">day_of_year</span><span class="p">:</span>
|
||||
<span class="n">tmp</span> <span class="o">=</span> <span class="n">date</span><span class="o">.</span><span class="n">timetuple</span><span class="p">()</span><span class="o">.</span><span class="n">tm_yday</span>
|
||||
<span class="k">elif</span> <span class="n">date_part</span> <span class="o">==</span> <span class="n">DateTime</span><span class="o">.</span><span class="n">day_of_month</span><span class="p">:</span>
|
||||
|
||||
@ -94,6 +94,10 @@
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">TimeGridPartitioner</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">"TimeGrid"</span><span class="p">,</span> <span class="n">preprocess</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="bp">self</span><span class="o">.</span><span class="n">season</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">'seasonality'</span><span class="p">,</span> <span class="n">DateTime</span><span class="o">.</span><span class="n">day_of_year</span><span class="p">)</span>
|
||||
<span class="sd">'''Seasonality, a pyFTS.models.seasonal.common.DateTime object'''</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="s1">'%Y-%m-</span><span class="si">%d</span><span class="s1"> %H:%M:%S'</span><span class="p">)</span>
|
||||
<span class="sd">'''A string with datetime formating mask'''</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="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">season</span> <span class="o">==</span> <span class="n">DateTime</span><span class="o">.</span><span class="n">year</span><span class="p">:</span>
|
||||
<span class="n">ndata</span> <span class="o">=</span> <span class="p">[</span><span class="n">strip_datepart</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">season</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>
|
||||
@ -114,7 +118,7 @@
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">ordered_sets</span> <span class="o">=</span> <span class="n">FS</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="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s1">'seasonal'</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">extractor</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">strip_datepart</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">season</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">extractor</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">strip_datepart</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">season</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask</span><span class="p">)</span>
|
||||
|
||||
<div class="viewcode-block" id="TimeGridPartitioner.build"><a class="viewcode-back" href="../../../../pyFTS.models.seasonal.html#pyFTS.models.seasonal.partitioner.TimeGridPartitioner.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="n">data</span><span class="p">):</span>
|
||||
<span class="n">sets</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
@ -124,6 +128,14 @@
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">season</span> <span class="o">==</span> <span class="n">DateTime</span><span class="o">.</span><span class="n">year</span><span class="p">:</span>
|
||||
<span class="n">dlen</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">min</span><span class="p">)</span>
|
||||
<span class="n">partlen</span> <span class="o">=</span> <span class="n">dlen</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitions</span>
|
||||
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">season</span> <span class="o">==</span> <span class="n">DateTime</span><span class="o">.</span><span class="n">day_of_week</span><span class="p">:</span>
|
||||
<span class="bp">self</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">max</span><span class="p">,</span> <span class="n">partlen</span><span class="p">,</span> <span class="n">pl2</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span>
|
||||
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">season</span> <span class="o">==</span> <span class="n">DateTime</span><span class="o">.</span><span class="n">hour</span><span class="p">:</span>
|
||||
<span class="bp">self</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">max</span><span class="p">,</span> <span class="n">partlen</span><span class="p">,</span> <span class="n">pl2</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">24</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span>
|
||||
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">season</span> <span class="o">==</span> <span class="n">DateTime</span><span class="o">.</span><span class="n">month</span><span class="p">:</span>
|
||||
<span class="bp">self</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">max</span><span class="p">,</span> <span class="n">partlen</span><span class="p">,</span> <span class="n">pl2</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">13</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span>
|
||||
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">season</span> <span class="ow">in</span> <span class="p">(</span><span class="n">DateTime</span><span class="o">.</span><span class="n">half</span><span class="p">,</span> <span class="n">DateTime</span><span class="o">.</span><span class="n">third</span><span class="p">,</span> <span class="n">DateTime</span><span class="o">.</span><span class="n">quarter</span><span class="p">,</span> <span class="n">DateTime</span><span class="o">.</span><span class="n">sixth</span><span class="p">):</span>
|
||||
<span class="bp">self</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">max</span><span class="p">,</span> <span class="n">partlen</span><span class="p">,</span> <span class="n">pl2</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">season</span><span class="o">.</span><span class="n">value</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="mi">1</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">partlen</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">season</span><span class="o">.</span><span class="n">value</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitions</span>
|
||||
<span class="n">pl2</span> <span class="o">=</span> <span class="n">partlen</span> <span class="o">/</span> <span class="mi">2</span>
|
||||
|
||||
@ -157,6 +157,9 @@
|
||||
|
||||
<div class="viewcode-block" id="CMeansPartitioner.build"><a class="viewcode-back" href="../../../pyFTS.partitioners.html#pyFTS.partitioners.CMeans.CMeansPartitioner.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="n">data</span><span class="p">):</span>
|
||||
<span class="n">sets</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
|
||||
<span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'type'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span><span class="p">,</span> <span class="s1">'variable'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">variable</span><span class="p">}</span>
|
||||
|
||||
<span class="n">centroides</span> <span class="o">=</span> <span class="n">c_means</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitions</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">centroides</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">max</span><span class="p">)</span>
|
||||
<span class="n">centroides</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">min</span><span class="p">)</span>
|
||||
@ -166,7 +169,7 @@
|
||||
<span class="n">_name</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_name</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
|
||||
<span class="n">sets</span><span class="p">[</span><span class="n">_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">FuzzySet</span><span class="p">(</span><span class="n">_name</span><span class="p">,</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span><span class="p">,</span>
|
||||
<span class="p">[</span><span class="nb">round</span><span class="p">(</span><span class="n">centroides</span><span class="p">[</span><span class="n">c</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="nb">round</span><span class="p">(</span><span class="n">centroides</span><span class="p">[</span><span class="n">c</span><span class="p">],</span> <span class="mi">3</span><span class="p">),</span> <span class="nb">round</span><span class="p">(</span><span class="n">centroides</span><span class="p">[</span><span class="n">c</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="nb">round</span><span class="p">(</span><span class="n">centroides</span><span class="p">[</span><span class="n">c</span><span class="p">],</span> <span class="mi">3</span><span class="p">))</span>
|
||||
<span class="nb">round</span><span class="p">(</span><span class="n">centroides</span><span class="p">[</span><span class="n">c</span><span class="p">],</span> <span class="mi">3</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">sets</span></div></div>
|
||||
</pre></div>
|
||||
|
||||
@ -161,23 +161,25 @@
|
||||
<div class="viewcode-block" id="EntropyPartitioner.build"><a class="viewcode-back" href="../../../pyFTS.partitioners.html#pyFTS.partitioners.Entropy.EntropyPartitioner.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="n">data</span><span class="p">):</span>
|
||||
<span class="n">sets</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
|
||||
<span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'type'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span><span class="p">,</span> <span class="s1">'variable'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">variable</span><span class="p">}</span>
|
||||
|
||||
<span class="n">partitions</span> <span class="o">=</span> <span class="n">bestSplit</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">partitions</span><span class="p">)</span>
|
||||
<span class="n">partitions</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">min</span><span class="p">)</span>
|
||||
<span class="n">partitions</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">max</span><span class="p">)</span>
|
||||
<span class="n">partitions</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">partitions</span><span class="p">))</span>
|
||||
<span class="n">partitions</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
|
||||
<span class="k">for</span> <span class="n">c</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">partitions</span><span class="p">)</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="bp">self</span><span class="o">.</span><span class="n">get_name</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">c</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">partitions</span><span class="p">)</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="bp">self</span><span class="o">.</span><span class="n">get_name</span><span class="p">(</span><span class="n">c</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">membership_function</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="n">sets</span><span class="p">[</span><span class="n">_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">FuzzySet</span><span class="p">(</span><span class="n">_name</span><span class="p">,</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span><span class="p">,</span>
|
||||
<span class="p">[</span><span class="n">partitions</span><span class="p">[</span><span class="n">c</span> <span class="o">-</span> <span class="mi">1</span><span class="p">],</span> <span class="n">partitions</span><span class="p">[</span><span class="n">c</span><span class="p">],</span> <span class="n">partitions</span><span class="p">[</span><span class="n">c</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]],</span><span class="n">partitions</span><span class="p">[</span><span class="n">c</span><span class="p">])</span>
|
||||
<span class="p">[</span><span class="n">partitions</span><span class="p">[</span><span class="n">c</span> <span class="o">-</span> <span class="mi">1</span><span class="p">],</span> <span class="n">partitions</span><span class="p">[</span><span class="n">c</span><span class="p">],</span> <span class="n">partitions</span><span class="p">[</span><span class="n">c</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]],</span><span class="n">partitions</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">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">membership_function</span> <span class="o">==</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trapmf</span><span class="p">:</span>
|
||||
<span class="n">b1</span> <span class="o">=</span> <span class="p">(</span><span class="n">partitions</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">-</span> <span class="n">partitions</span><span class="p">[</span><span class="n">c</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">b2</span> <span class="o">=</span> <span class="p">(</span><span class="n">partitions</span><span class="p">[</span><span class="n">c</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">partitions</span><span class="p">[</span><span class="n">c</span><span class="p">])</span> <span class="o">/</span> <span class="mi">2</span>
|
||||
<span class="n">sets</span><span class="p">[</span><span class="n">_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">FuzzySet</span><span class="p">(</span><span class="n">_name</span><span class="p">,</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trapmf</span><span class="p">,</span>
|
||||
<span class="p">[</span><span class="n">partitions</span><span class="p">[</span><span class="n">c</span> <span class="o">-</span> <span class="mi">1</span><span class="p">],</span> <span class="n">partitions</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">-</span> <span class="n">b1</span><span class="p">,</span>
|
||||
<span class="n">partitions</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">+</span> <span class="n">b2</span><span class="p">,</span> <span class="n">partitions</span><span class="p">[</span><span class="n">c</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]],</span>
|
||||
<span class="n">partitions</span><span class="p">[</span><span class="n">c</span><span class="p">])</span>
|
||||
<span class="n">partitions</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">sets</span></div></div>
|
||||
</pre></div>
|
||||
|
||||
@ -187,6 +187,8 @@
|
||||
<div class="viewcode-block" id="FCMPartitioner.build"><a class="viewcode-back" href="../../../pyFTS.partitioners.html#pyFTS.partitioners.FCM.FCMPartitioner.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="n">data</span><span class="p">):</span>
|
||||
<span class="n">sets</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
|
||||
<span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'type'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span><span class="p">,</span> <span class="s1">'variable'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">variable</span><span class="p">}</span>
|
||||
|
||||
<span class="n">centroids</span> <span class="o">=</span> <span class="n">fuzzy_cmeans</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitions</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="mi">2</span><span class="p">)</span>
|
||||
<span class="n">centroids</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">max</span><span class="p">)</span>
|
||||
<span class="n">centroids</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">min</span><span class="p">)</span>
|
||||
@ -198,14 +200,14 @@
|
||||
<span class="n">sets</span><span class="p">[</span><span class="n">_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">FuzzySet</span><span class="p">(</span><span class="n">_name</span><span class="p">,</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span><span class="p">,</span>
|
||||
<span class="p">[</span><span class="nb">round</span><span class="p">(</span><span class="n">centroids</span><span class="p">[</span><span class="n">c</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="nb">round</span><span class="p">(</span><span class="n">centroids</span><span class="p">[</span><span class="n">c</span><span class="p">],</span> <span class="mi">3</span><span class="p">),</span>
|
||||
<span class="nb">round</span><span class="p">(</span><span class="n">centroids</span><span class="p">[</span><span class="n">c</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="nb">round</span><span class="p">(</span><span class="n">centroids</span><span class="p">[</span><span class="n">c</span><span class="p">],</span> <span class="mi">3</span><span class="p">))</span>
|
||||
<span class="nb">round</span><span class="p">(</span><span class="n">centroids</span><span class="p">[</span><span class="n">c</span><span class="p">],</span> <span class="mi">3</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="bp">self</span><span class="o">.</span><span class="n">membership_function</span> <span class="o">==</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trapmf</span><span class="p">:</span>
|
||||
<span class="n">q1</span> <span class="o">=</span> <span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">centroids</span><span class="p">[</span><span class="n">c</span><span class="p">],</span> <span class="mi">3</span><span class="p">)</span> <span class="o">-</span> <span class="nb">round</span><span class="p">(</span><span class="n">centroids</span><span class="p">[</span><span class="n">c</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="o">/</span> <span class="mi">2</span>
|
||||
<span class="n">q2</span> <span class="o">=</span> <span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">centroids</span><span class="p">[</span><span class="n">c</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="o">-</span> <span class="nb">round</span><span class="p">(</span><span class="n">centroids</span><span class="p">[</span><span class="n">c</span><span class="p">],</span> <span class="mi">3</span><span class="p">))</span> <span class="o">/</span> <span class="mi">2</span>
|
||||
<span class="n">sets</span><span class="p">[</span><span class="n">_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">FuzzySet</span><span class="p">(</span><span class="n">_name</span><span class="p">,</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span><span class="p">,</span>
|
||||
<span class="p">[</span><span class="nb">round</span><span class="p">(</span><span class="n">centroids</span><span class="p">[</span><span class="n">c</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="nb">round</span><span class="p">(</span><span class="n">centroids</span><span class="p">[</span><span class="n">c</span><span class="p">],</span> <span class="mi">3</span><span class="p">)</span> <span class="o">-</span> <span class="n">q1</span><span class="p">,</span>
|
||||
<span class="nb">round</span><span class="p">(</span><span class="n">centroids</span><span class="p">[</span><span class="n">c</span><span class="p">],</span> <span class="mi">3</span><span class="p">)</span> <span class="o">+</span> <span class="n">q2</span><span class="p">,</span> <span class="nb">round</span><span class="p">(</span><span class="n">centroids</span><span class="p">[</span><span class="n">c</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="nb">round</span><span class="p">(</span><span class="n">centroids</span><span class="p">[</span><span class="n">c</span><span class="p">],</span> <span class="mi">3</span><span class="p">))</span>
|
||||
<span class="nb">round</span><span class="p">(</span><span class="n">centroids</span><span class="p">[</span><span class="n">c</span><span class="p">],</span> <span class="mi">3</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">sets</span></div></div>
|
||||
</pre></div>
|
||||
|
||||
@ -106,6 +106,8 @@
|
||||
|
||||
<span class="n">sets</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
|
||||
<span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'type'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span><span class="p">,</span> <span class="s1">'variable'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">variable</span><span class="p">}</span>
|
||||
|
||||
<span class="n">dlen</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">max</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">min</span>
|
||||
<span class="n">npart</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">dlen</span> <span class="o">/</span> <span class="n">base</span><span class="p">)</span>
|
||||
<span class="n">partition</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">min</span><span class="p">)</span>
|
||||
@ -113,14 +115,14 @@
|
||||
<span class="n">_name</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_name</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">membership_function</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="n">sets</span><span class="p">[</span><span class="n">_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">FuzzySet</span><span class="p">(</span><span class="n">_name</span><span class="p">,</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span><span class="p">,</span>
|
||||
<span class="p">[</span><span class="n">partition</span> <span class="o">-</span> <span class="n">base</span><span class="p">,</span> <span class="n">partition</span><span class="p">,</span> <span class="n">partition</span> <span class="o">+</span> <span class="n">base</span><span class="p">],</span> <span class="n">partition</span><span class="p">)</span>
|
||||
<span class="p">[</span><span class="n">partition</span> <span class="o">-</span> <span class="n">base</span><span class="p">,</span> <span class="n">partition</span><span class="p">,</span> <span class="n">partition</span> <span class="o">+</span> <span class="n">base</span><span class="p">],</span> <span class="n">partition</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="bp">self</span><span class="o">.</span><span class="n">membership_function</span> <span class="o">==</span> <span class="n">Membership</span><span class="o">.</span><span class="n">gaussmf</span><span class="p">:</span>
|
||||
<span class="n">sets</span><span class="p">[</span><span class="n">_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">FuzzySet</span><span class="p">(</span><span class="n">_name</span><span class="p">,</span> <span class="n">Membership</span><span class="o">.</span><span class="n">gaussmf</span><span class="p">,</span>
|
||||
<span class="p">[</span><span class="n">partition</span><span class="p">,</span> <span class="n">base</span><span class="o">/</span><span class="mi">2</span><span class="p">],</span> <span class="n">partition</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">membership_function</span> <span class="o">==</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trapmf</span><span class="p">:</span>
|
||||
<span class="n">sets</span><span class="p">[</span><span class="n">_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">FuzzySet</span><span class="p">(</span><span class="n">_name</span><span class="p">,</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trapmf</span><span class="p">,</span>
|
||||
<span class="p">[</span><span class="n">partition</span> <span class="o">-</span> <span class="n">base</span><span class="p">,</span> <span class="n">partition</span> <span class="o">-</span> <span class="p">(</span><span class="n">base</span><span class="o">/</span><span class="mi">2</span><span class="p">),</span>
|
||||
<span class="n">partition</span> <span class="o">+</span> <span class="p">(</span><span class="n">base</span> <span class="o">/</span> <span class="mi">2</span><span class="p">),</span> <span class="n">partition</span> <span class="o">+</span> <span class="n">base</span><span class="p">],</span> <span class="n">partition</span><span class="p">)</span>
|
||||
<span class="n">partition</span> <span class="o">+</span> <span class="p">(</span><span class="n">base</span> <span class="o">/</span> <span class="mi">2</span><span class="p">),</span> <span class="n">partition</span> <span class="o">+</span> <span class="n">base</span><span class="p">],</span> <span class="n">partition</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
|
||||
|
||||
<span class="n">partition</span> <span class="o">+=</span> <span class="n">base</span>
|
||||
|
||||
|
||||
@ -94,11 +94,11 @@
|
||||
<div class="viewcode-block" id="SingletonPartitioner.build"><a class="viewcode-back" href="../../../pyFTS.partitioners.html#pyFTS.partitioners.Singleton.SingletonPartitioner.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="n">data</span><span class="p">):</span>
|
||||
<span class="n">sets</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
|
||||
<span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'type'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span><span class="p">,</span> <span class="s1">'variable'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">variable</span><span class="p">}</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">count</span><span class="p">,</span> <span class="n">instance</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
|
||||
<span class="n">_name</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_name</span><span class="p">(</span><span class="n">count</span><span class="p">)</span>
|
||||
<span class="n">sets</span><span class="p">[</span><span class="n">_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">FuzzySet</span><span class="p">(</span><span class="n">_name</span><span class="p">,</span> <span class="n">Membership</span><span class="o">.</span><span class="n">singleton</span><span class="p">,</span> <span class="p">[</span><span class="n">instance</span><span class="p">],</span> <span class="n">instance</span><span class="p">)</span>
|
||||
|
||||
<span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'type'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">type</span><span class="p">,</span> <span class="s1">'variable'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">variable</span><span class="p">}</span>
|
||||
<span class="n">sets</span><span class="p">[</span><span class="n">_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">FuzzySet</span><span class="p">(</span><span class="n">_name</span><span class="p">,</span> <span class="n">Membership</span><span class="o">.</span><span class="n">singleton</span><span class="p">,</span> <span class="p">[</span><span class="n">instance</span><span class="p">],</span> <span class="n">instance</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">sets</span></div></div>
|
||||
</pre></div>
|
||||
|
||||
@ -139,7 +139,7 @@
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">sets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">ordered_sets</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">setnames</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">ordered_sets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">setnames</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">ordered_sets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">setnames</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="k">else</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</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>
|
||||
|
||||
|
||||
2
docs/build/html/_sources/modules.rst.txt
vendored
2
docs/build/html/_sources/modules.rst.txt
vendored
@ -2,6 +2,6 @@ pyFTS
|
||||
=====
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 4
|
||||
:maxdepth: 5
|
||||
|
||||
pyFTS
|
||||
|
||||
16
docs/build/html/_sources/pyFTS.data.rst.txt
vendored
16
docs/build/html/_sources/pyFTS.data.rst.txt
vendored
@ -29,6 +29,14 @@ pyFTS.data.common module
|
||||
Datasets
|
||||
--------
|
||||
|
||||
Artificial and synthetic data generators
|
||||
----------------------------------------
|
||||
|
||||
.. automodule:: pyFTS.data.artificial
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
AirPassengers dataset
|
||||
-------------------------------
|
||||
|
||||
@ -143,14 +151,6 @@ TAIEX dataset
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
pyFTS.data.artificial module
|
||||
----------------------------
|
||||
|
||||
.. automodule:: pyFTS.data.artificial
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Henon chaotic time series
|
||||
-------------------------
|
||||
|
||||
|
||||
32
docs/build/html/_sources/pyFTS.distributed.rst.txt
vendored
Normal file
32
docs/build/html/_sources/pyFTS.distributed.rst.txt
vendored
Normal file
@ -0,0 +1,32 @@
|
||||
pyFTS.distributed package
|
||||
=========================
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: pyFTS.distributed
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
pyFTS.distributed.dispy module
|
||||
------------------------------
|
||||
|
||||
.. automodule:: pyFTS.distributed.dispy
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
pyFTS.distributed.spark module
|
||||
----------------------------------
|
||||
|
||||
.. automodule:: pyFTS.distributed.spark
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
|
||||
|
||||
@ -28,4 +28,12 @@ pyFTS.hyperparam.GridSearch module
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
pyFTS.hyperparam.Evolutionary module
|
||||
------------------------------------
|
||||
|
||||
.. automodule:: pyFTS.hyperparam.Evolutionary
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
pyFTS.models.incremental package
|
||||
=============================
|
||||
================================
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
@ -13,10 +13,19 @@ Module contents
|
||||
Submodules
|
||||
----------
|
||||
|
||||
pyFTS.models.incremental.Retrainer module
|
||||
-------------------------------------
|
||||
pyFTS.models.incremental.TimeVariant module
|
||||
-------------------------------------------
|
||||
|
||||
.. automodule:: pyFTS.models.incremental.Retrainer
|
||||
.. automodule:: pyFTS.models.incremental.TimeVariant
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
|
||||
pyFTS.models.incremental.IncrementalEnsemble module
|
||||
---------------------------------------------------
|
||||
|
||||
.. automodule:: pyFTS.models.incremental.IncrementalEnsemble
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
1
docs/build/html/_sources/pyFTS.rst.txt
vendored
1
docs/build/html/_sources/pyFTS.rst.txt
vendored
@ -9,6 +9,7 @@ Subpackages
|
||||
pyFTS.benchmarks
|
||||
pyFTS.common
|
||||
pyFTS.data
|
||||
pyFTS.distributed
|
||||
pyFTS.hyperparam
|
||||
pyFTS.models
|
||||
pyFTS.partitioners
|
||||
|
||||
162
docs/build/html/genindex.html
vendored
162
docs/build/html/genindex.html
vendored
@ -123,7 +123,11 @@
|
||||
</li>
|
||||
</ul></li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.alpha_cut">alpha_cut (pyFTS.common.fts.FTS attribute)</a>
|
||||
|
||||
<ul>
|
||||
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.variable.Variable.alpha_cut">(pyFTS.models.multivariate.variable.Variable attribute)</a>
|
||||
</li>
|
||||
</ul></li>
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.analytic_tabular_dataframe">analytic_tabular_dataframe() (in module pyFTS.benchmarks.Util)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.analytical_data_columns">analytical_data_columns() (in module pyFTS.benchmarks.Util)</a>
|
||||
@ -235,7 +239,7 @@
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.auto_update">auto_update (pyFTS.common.fts.FTS attribute)</a>
|
||||
|
||||
<ul>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.Retrainer.Retrainer.auto_update">(pyFTS.models.incremental.Retrainer.Retrainer attribute)</a>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.auto_update">(pyFTS.models.incremental.TimeVariant.Retrainer attribute)</a>
|
||||
</li>
|
||||
</ul></li>
|
||||
<li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.averageloglikelihood">averageloglikelihood() (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)</a>
|
||||
@ -248,8 +252,12 @@
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.base_dataframe_columns">base_dataframe_columns() (in module pyFTS.benchmarks.Util)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.Retrainer.Retrainer.batch_size">batch_size (pyFTS.models.incremental.Retrainer.Retrainer attribute)</a>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.batch_size">batch_size (pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS attribute)</a>
|
||||
|
||||
<ul>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.batch_size">(pyFTS.models.incremental.TimeVariant.Retrainer attribute)</a>
|
||||
</li>
|
||||
</ul></li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.Membership.bellmf">bellmf() (in module pyFTS.common.Membership)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.benchmark_only">benchmark_only (pyFTS.common.fts.FTS attribute)</a>
|
||||
@ -259,6 +267,8 @@
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.SortedCollection.SortedCollection.between">between() (pyFTS.common.SortedCollection.SortedCollection method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.bins">bins (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.data.html#pyFTS.data.artificial.SignalEmulator.blip">blip() (pyFTS.data.artificial.SignalEmulator method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.Transformations.BoxCox">BoxCox (class in pyFTS.common.Transformations)</a>
|
||||
</li>
|
||||
@ -268,6 +278,8 @@
|
||||
</li>
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.brier_score">brier_score() (in module pyFTS.benchmarks.Measures)</a>
|
||||
</li>
|
||||
</ul></td>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.variable.Variable.build">build() (pyFTS.models.multivariate.variable.Variable method)</a>
|
||||
|
||||
<ul>
|
||||
@ -292,8 +304,6 @@
|
||||
<li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.build">(pyFTS.partitioners.partitioner.Partitioner method)</a>
|
||||
</li>
|
||||
</ul></li>
|
||||
</ul></td>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.build_cdf_qtl">build_cdf_qtl() (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.tree.build_tree_without_order">build_tree_without_order() (in module pyFTS.common.tree)</a>
|
||||
@ -352,11 +362,13 @@
|
||||
</li>
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.ResidualAnalysis.compare_residuals">compare_residuals() (in module pyFTS.benchmarks.ResidualAnalysis)</a>
|
||||
</li>
|
||||
</ul></td>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.compareModelsPlot">compareModelsPlot() (in module pyFTS.benchmarks.benchmarks)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.compareModelsTable">compareModelsTable() (in module pyFTS.benchmarks.benchmarks)</a>
|
||||
</li>
|
||||
</ul></td>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="pyFTS.data.html#pyFTS.data.artificial.SignalEmulator.components">components (pyFTS.data.artificial.SignalEmulator attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.nsfts.NonStationaryFTS.conditional_perturbation_factors">conditional_perturbation_factors() (pyFTS.models.nonstationary.nsfts.NonStationaryFTS method)</a>
|
||||
</li>
|
||||
@ -391,6 +403,12 @@
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.create_benchmark_tables">create_benchmark_tables() (in module pyFTS.benchmarks.Util)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.hyperparam.html#pyFTS.hyperparam.Util.create_hyperparam_tables">create_hyperparam_tables() (in module pyFTS.hyperparam.Util)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.distributed.html#pyFTS.distributed.spark.create_multivariate_model">create_multivariate_model() (in module pyFTS.distributed.spark)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.distributed.html#pyFTS.distributed.spark.create_spark_conf">create_spark_conf() (in module pyFTS.distributed.spark)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.distributed.html#pyFTS.distributed.spark.create_univariate_model">create_univariate_model() (in module pyFTS.distributed.spark)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.crossentropy">crossentropy() (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)</a>
|
||||
</li>
|
||||
@ -420,10 +438,10 @@
|
||||
</li>
|
||||
<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.day_of_week">day_of_week (pyFTS.models.seasonal.common.DateTime attribute)</a>
|
||||
</li>
|
||||
</ul></td>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.day_of_year">day_of_year (pyFTS.models.seasonal.common.DateTime attribute)</a>
|
||||
</li>
|
||||
</ul></td>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.density">density() (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.detail">detail (pyFTS.common.fts.FTS attribute)</a>
|
||||
@ -435,6 +453,10 @@
|
||||
<li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.differential_offset">differential_offset() (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.partitioners.html#pyFTS.partitioners.CMeans.distance">distance() (in module pyFTS.partitioners.CMeans)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.distributed.html#pyFTS.distributed.spark.distributed_predict">distributed_predict() (in module pyFTS.distributed.spark)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.distributed.html#pyFTS.distributed.spark.distributed_train">distributed_train() (in module pyFTS.distributed.spark)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.Util.draw_sets_on_axis">draw_sets_on_axis() (in module pyFTS.common.Util)</a>
|
||||
</li>
|
||||
@ -547,7 +569,9 @@
|
||||
</li>
|
||||
<li><a href="pyFTS.models.html#pyFTS.models.hwang.HighOrderFTS.forecast">(pyFTS.models.hwang.HighOrderFTS method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.Retrainer.Retrainer.forecast">(pyFTS.models.incremental.Retrainer.Retrainer method)</a>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.forecast">(pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.forecast">(pyFTS.models.incremental.TimeVariant.Retrainer method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFTS.forecast">(pyFTS.models.ismailefendi.ImprovedWeightedFTS method)</a>
|
||||
</li>
|
||||
@ -662,15 +686,19 @@
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS">FTS (class in pyFTS.common.fts)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.Retrainer.Retrainer.fts_method">fts_method (pyFTS.models.incremental.Retrainer.Retrainer attribute)</a>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.fts_method">fts_method (pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS attribute)</a>
|
||||
|
||||
<ul>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.fts_method">(pyFTS.models.incremental.TimeVariant.Retrainer attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.fts_method">(pyFTS.models.multivariate.cmvfts.ClusteredMVFTS attribute)</a>
|
||||
</li>
|
||||
</ul></li>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.Retrainer.Retrainer.fts_params">fts_params (pyFTS.models.incremental.Retrainer.Retrainer attribute)</a>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.fts_params">fts_params (pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS attribute)</a>
|
||||
|
||||
<ul>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.fts_params">(pyFTS.models.incremental.TimeVariant.Retrainer attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.fts_params">(pyFTS.models.multivariate.cmvfts.ClusteredMVFTS attribute)</a>
|
||||
</li>
|
||||
</ul></li>
|
||||
@ -781,10 +809,14 @@
|
||||
</li>
|
||||
</ul></li>
|
||||
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.generate_lhs_flrs">generate_lhs_flrs() (pyFTS.models.multivariate.mvfts.MVFTS method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.data.html#pyFTS.data.artificial.generate_linear_periodic_gaussian">generate_linear_periodic_gaussian() (in module pyFTS.data.artificial)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.FLR.generate_non_recurrent_flrs">generate_non_recurrent_flrs() (in module pyFTS.common.FLR)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.FLR.generate_recurrent_flrs">generate_recurrent_flrs() (in module pyFTS.common.FLR)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.data.html#pyFTS.data.artificial.generate_sinoidal_periodic_gaussian">generate_sinoidal_periodic_gaussian() (in module pyFTS.data.artificial)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.data.html#pyFTS.data.artificial.generate_uniform_linear">generate_uniform_linear() (in module pyFTS.data.artificial)</a>
|
||||
</li>
|
||||
@ -793,6 +825,8 @@
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.get_benchmark_point_methods">get_benchmark_point_methods() (in module pyFTS.benchmarks.benchmarks)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.get_benchmark_probabilistic_methods">get_benchmark_probabilistic_methods() (in module pyFTS.benchmarks.benchmarks)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.distributed.html#pyFTS.distributed.spark.get_clustered_partitioner">get_clustered_partitioner() (in module pyFTS.distributed.spark)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.data.html#pyFTS.data.AirPassengers.get_data">get_data() (in module pyFTS.data.AirPassengers)</a>
|
||||
|
||||
@ -989,6 +1023,8 @@
|
||||
<li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_models_forecasts">get_models_forecasts() (pyFTS.models.ensemble.ensemble.EnsembleFTS method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.get_name">get_name() (pyFTS.partitioners.partitioner.Partitioner method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.distributed.html#pyFTS.distributed.spark.get_partitioner">get_partitioner() (in module pyFTS.distributed.spark)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_point">get_point() (pyFTS.models.ensemble.ensemble.EnsembleFTS method)</a>
|
||||
</li>
|
||||
@ -1040,6 +1076,8 @@
|
||||
<li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.get_upper">(pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
|
||||
</li>
|
||||
</ul></li>
|
||||
<li><a href="pyFTS.distributed.html#pyFTS.distributed.spark.get_variables">get_variables() (in module pyFTS.distributed.spark)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.tree.FLRGTreeNode.getChildren">getChildren() (pyFTS.common.tree.FLRGTreeNode method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.tree.FLRGTreeNode.getStr">getStr() (pyFTS.common.tree.FLRGTreeNode method)</a>
|
||||
@ -1055,6 +1093,8 @@
|
||||
<table style="width: 100%" class="indextable genindextable"><tr>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.kde.KernelSmoothing.h">h (pyFTS.probabilistic.kde.KernelSmoothing attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.half">half (pyFTS.models.seasonal.common.DateTime attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.has_interval_forecasting">has_interval_forecasting (pyFTS.common.fts.FTS attribute)</a>
|
||||
</li>
|
||||
@ -1070,21 +1110,19 @@
|
||||
</li>
|
||||
<li><a href="pyFTS.models.html#pyFTS.models.hofts.HighOrderFLRG">HighOrderFLRG (class in pyFTS.models.hofts)</a>
|
||||
</li>
|
||||
</ul></td>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS">HighOrderFTS (class in pyFTS.models.hofts)</a>
|
||||
|
||||
<ul>
|
||||
<li><a href="pyFTS.models.html#pyFTS.models.hwang.HighOrderFTS">(class in pyFTS.models.hwang)</a>
|
||||
</li>
|
||||
</ul></li>
|
||||
</ul></td>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.cvfts.HighOrderNonstationaryFLRG">HighOrderNonstationaryFLRG (class in pyFTS.models.nonstationary.cvfts)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFLRG">HighOrderNonStationaryFLRG (class in pyFTS.models.nonstationary.honsfts)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.honsfts.HighOrderNonStationaryFTS">HighOrderNonStationaryFTS (class in pyFTS.models.nonstationary.honsfts)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.hour">hour (pyFTS.models.seasonal.common.DateTime attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.hour_of_day">hour_of_day (pyFTS.models.seasonal.common.DateTime attribute)</a>
|
||||
</li>
|
||||
@ -1105,6 +1143,10 @@
|
||||
<li><a href="pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFLRG">ImprovedWeightedFLRG (class in pyFTS.models.ismailefendi)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFTS">ImprovedWeightedFTS (class in pyFTS.models.ismailefendi)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.data.html#pyFTS.data.artificial.SignalEmulator.incremental_gaussian">incremental_gaussian() (pyFTS.data.artificial.SignalEmulator method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS">IncrementalEnsembleFTS (class in pyFTS.models.incremental.IncrementalEnsemble)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.FLR.IndexedFLR.index">index (pyFTS.common.FLR.IndexedFLR attribute)</a>
|
||||
</li>
|
||||
@ -1159,6 +1201,8 @@
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.is_high_order">is_high_order (pyFTS.common.fts.FTS attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.is_multivariate">is_multivariate (pyFTS.common.fts.FTS attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.is_wrapper">is_wrapper (pyFTS.common.fts.FTS attribute)</a>
|
||||
</li>
|
||||
</ul></td>
|
||||
</tr></table>
|
||||
@ -1233,7 +1277,11 @@
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.mape_interval">mape_interval() (in module pyFTS.benchmarks.Measures)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.variable.Variable.mask">mask (pyFTS.models.multivariate.variable.Variable attribute)</a>
|
||||
|
||||
<ul>
|
||||
<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.partitioner.TimeGridPartitioner.mask">(pyFTS.models.seasonal.partitioner.TimeGridPartitioner attribute)</a>
|
||||
</li>
|
||||
</ul></li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.max_lag">max_lag (pyFTS.common.fts.FTS attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.partitioners.html#pyFTS.partitioners.FCM.membership">membership() (in module pyFTS.partitioners.FCM)</a>
|
||||
@ -1251,11 +1299,11 @@
|
||||
<li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.membership_function">membership_function (pyFTS.partitioners.partitioner.Partitioner attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.merge">merge() (pyFTS.common.fts.FTS method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.FuzzySet.FuzzySet.mf">mf (pyFTS.common.FuzzySet.FuzzySet attribute)</a>
|
||||
</li>
|
||||
</ul></td>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.FuzzySet.FuzzySet.mf">mf (pyFTS.common.FuzzySet.FuzzySet attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.min_order">min_order (pyFTS.common.fts.FTS attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.minute_of_day">minute_of_day (pyFTS.models.seasonal.common.DateTime attribute)</a>
|
||||
@ -1268,7 +1316,7 @@
|
||||
</li>
|
||||
<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.minute_of_year">minute_of_year (pyFTS.models.seasonal.common.DateTime attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.Retrainer.Retrainer.model">model (pyFTS.models.incremental.Retrainer.Retrainer attribute)</a>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.model">model (pyFTS.models.incremental.TimeVariant.Retrainer attribute)</a>
|
||||
|
||||
<ul>
|
||||
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.model">(pyFTS.models.multivariate.cmvfts.ClusteredMVFTS attribute)</a>
|
||||
@ -1369,13 +1417,23 @@
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.partitioner">partitioner (pyFTS.common.fts.FTS attribute)</a>
|
||||
|
||||
<ul>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.Retrainer.Retrainer.partitioner">(pyFTS.models.incremental.Retrainer.Retrainer attribute)</a>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.partitioner">(pyFTS.models.incremental.TimeVariant.Retrainer attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.variable.Variable.partitioner">(pyFTS.models.multivariate.variable.Variable attribute)</a>
|
||||
</li>
|
||||
</ul></li>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.Retrainer.Retrainer.partitioner_method">partitioner_method (pyFTS.models.incremental.Retrainer.Retrainer attribute)</a>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.partitioner_method">partitioner_method (pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS attribute)</a>
|
||||
|
||||
<ul>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.partitioner_method">(pyFTS.models.incremental.TimeVariant.Retrainer attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.Retrainer.Retrainer.partitioner_params">partitioner_params (pyFTS.models.incremental.Retrainer.Retrainer attribute)</a>
|
||||
</ul></li>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.partitioner_params">partitioner_params (pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS attribute)</a>
|
||||
|
||||
<ul>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.partitioner_params">(pyFTS.models.incremental.TimeVariant.Retrainer attribute)</a>
|
||||
</li>
|
||||
</ul></li>
|
||||
<li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.partitions">partitions (pyFTS.partitioners.partitioner.Partitioner attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.tree.FLRGTreeNode.paths">paths() (pyFTS.common.tree.FLRGTreeNode method)</a>
|
||||
@ -1385,6 +1443,8 @@
|
||||
<li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.common.FuzzySet.perform_width">perform_width() (pyFTS.models.nonstationary.common.FuzzySet method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.perturbation.periodic">periodic() (in module pyFTS.models.nonstationary.perturbation)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.data.html#pyFTS.data.artificial.SignalEmulator.periodic_gaussian">periodic_gaussian() (pyFTS.data.artificial.SignalEmulator method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.Util.persist_env">persist_env() (in module pyFTS.common.Util)</a>
|
||||
</li>
|
||||
@ -1505,6 +1565,8 @@
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.process_point_jobs">process_point_jobs() (in module pyFTS.benchmarks.benchmarks)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.process_probabilistic_jobs">process_probabilistic_jobs() (in module pyFTS.benchmarks.benchmarks)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.product_dict">product_dict() (in module pyFTS.models.multivariate.mvfts)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.probabilistic.html#pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution.pseudologlikelihood">pseudologlikelihood() (pyFTS.probabilistic.ProbabilityDistribution.ProbabilityDistribution method)</a>
|
||||
</li>
|
||||
@ -1529,11 +1591,11 @@
|
||||
<li><a href="pyFTS.benchmarks.html#module-pyFTS.benchmarks.Util">pyFTS.benchmarks.Util (module)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#module-pyFTS.common">pyFTS.common (module)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#module-pyFTS.common.Composite">pyFTS.common.Composite (module)</a>
|
||||
</li>
|
||||
</ul></td>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="pyFTS.common.html#module-pyFTS.common.Composite">pyFTS.common.Composite (module)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#module-pyFTS.common.FLR">pyFTS.common.FLR (module)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#module-pyFTS.common.flrg">pyFTS.common.flrg (module)</a>
|
||||
@ -1599,6 +1661,10 @@
|
||||
<li><a href="pyFTS.data.html#module-pyFTS.data.sunspots">pyFTS.data.sunspots (module)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.data.html#module-pyFTS.data.TAIEX">pyFTS.data.TAIEX (module)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.distributed.html#module-pyFTS.distributed">pyFTS.distributed (module)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.distributed.html#module-pyFTS.distributed.spark">pyFTS.distributed.spark (module)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.hyperparam.html#module-pyFTS.hyperparam">pyFTS.hyperparam (module)</a>
|
||||
</li>
|
||||
@ -1626,7 +1692,9 @@
|
||||
</li>
|
||||
<li><a href="pyFTS.models.incremental.html#module-pyFTS.models.incremental">pyFTS.models.incremental (module)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.incremental.html#module-pyFTS.models.incremental.Retrainer">pyFTS.models.incremental.Retrainer (module)</a>
|
||||
<li><a href="pyFTS.models.incremental.html#module-pyFTS.models.incremental.IncrementalEnsemble">pyFTS.models.incremental.IncrementalEnsemble (module)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.incremental.html#module-pyFTS.models.incremental.TimeVariant">pyFTS.models.incremental.TimeVariant (module)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.html#module-pyFTS.models.ismailefendi">pyFTS.models.ismailefendi (module)</a>
|
||||
</li>
|
||||
@ -1725,6 +1793,8 @@
|
||||
</ul></td>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression">QuantileRegression (class in pyFTS.benchmarks.quantreg)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.quarter">quarter (pyFTS.models.seasonal.common.DateTime attribute)</a>
|
||||
</li>
|
||||
</ul></td>
|
||||
</tr></table>
|
||||
@ -1746,7 +1816,7 @@
|
||||
</li>
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.resolution">resolution() (in module pyFTS.benchmarks.Measures)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.Retrainer.Retrainer">Retrainer (class in pyFTS.models.incremental.Retrainer)</a>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer">Retrainer (class in pyFTS.models.incremental.TimeVariant)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.FLR.FLR.RHS">RHS (pyFTS.common.FLR.FLR attribute)</a>
|
||||
|
||||
@ -1765,6 +1835,8 @@
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.rmse_interval">rmse_interval() (in module pyFTS.benchmarks.Measures)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.Transformations.roi">roi() (in module pyFTS.common.Transformations)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.data.html#pyFTS.data.artificial.SignalEmulator.run">run() (pyFTS.data.artificial.SignalEmulator method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.run_interval">run_interval() (in module pyFTS.benchmarks.benchmarks)</a>
|
||||
</li>
|
||||
@ -1795,6 +1867,8 @@
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.scale_params">scale_params() (in module pyFTS.benchmarks.Util)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.partitioners.PolynomialNonStationaryPartitioner.scale_up">scale_up() (pyFTS.models.nonstationary.partitioners.PolynomialNonStationaryPartitioner method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.partitioner.TimeGridPartitioner.season">season (pyFTS.models.seasonal.partitioner.TimeGridPartitioner attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS">SeasonalEnsembleFTS (class in pyFTS.models.ensemble.multiseasonal)</a>
|
||||
</li>
|
||||
@ -1830,8 +1904,6 @@
|
||||
</ul></li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.FuzzySet.set_ordered">set_ordered() (in module pyFTS.common.FuzzySet)</a>
|
||||
</li>
|
||||
</ul></td>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.FLR.FLR.set_rhs">set_rhs() (pyFTS.models.multivariate.FLR.FLR method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.common.MultivariateFuzzySet.set_target_variable">set_target_variable() (pyFTS.models.multivariate.common.MultivariateFuzzySet method)</a>
|
||||
@ -1840,7 +1912,11 @@
|
||||
</li>
|
||||
<li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.setnames">setnames (pyFTS.partitioners.partitioner.Partitioner attribute)</a>
|
||||
</li>
|
||||
</ul></td>
|
||||
<td style="width: 33%; vertical-align: top;"><ul>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.sets">sets (pyFTS.common.fts.FTS attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.distributed.html#pyFTS.distributed.spark.share_parameters">share_parameters() (in module pyFTS.distributed.spark)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.sharpness">sharpness() (in module pyFTS.benchmarks.Measures)</a>
|
||||
</li>
|
||||
@ -1849,6 +1925,8 @@
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.Util.show_and_save_image">show_and_save_image() (in module pyFTS.common.Util)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.Membership.sigmf">sigmf() (in module pyFTS.common.Membership)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.data.html#pyFTS.data.artificial.SignalEmulator">SignalEmulator (class in pyFTS.data.artificial)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.partitioners.simplenonstationary_gridpartitioner_builder">simplenonstationary_gridpartitioner_builder() (in module pyFTS.models.nonstationary.partitioners)</a>
|
||||
</li>
|
||||
@ -1863,6 +1941,16 @@
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.Membership.singleton">singleton() (in module pyFTS.common.Membership)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.partitioners.html#pyFTS.partitioners.Singleton.SingletonPartitioner">SingletonPartitioner (class in pyFTS.partitioners.Singleton)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.sixth">sixth (pyFTS.models.seasonal.common.DateTime attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.distributed.html#pyFTS.distributed.spark.slave_forecast_multivariate">slave_forecast_multivariate() (in module pyFTS.distributed.spark)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.distributed.html#pyFTS.distributed.spark.slave_forecast_univariate">slave_forecast_univariate() (in module pyFTS.distributed.spark)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.distributed.html#pyFTS.distributed.spark.slave_train_multivariate">slave_train_multivariate() (in module pyFTS.distributed.spark)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.distributed.html#pyFTS.distributed.spark.slave_train_univariate">slave_train_univariate() (in module pyFTS.distributed.spark)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.Util.sliding_window">sliding_window() (in module pyFTS.common.Util)</a>
|
||||
</li>
|
||||
@ -1877,6 +1965,8 @@
|
||||
<li><a href="pyFTS.partitioners.html#pyFTS.partitioners.Entropy.splitAbove">splitAbove() (in module pyFTS.partitioners.Entropy)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.partitioners.html#pyFTS.partitioners.Entropy.splitBelow">splitBelow() (in module pyFTS.partitioners.Entropy)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.data.html#pyFTS.data.artificial.SignalEmulator.stationary_gaussian">stationary_gaussian() (pyFTS.data.artificial.SignalEmulator method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.stats">stats() (in module pyFTS.benchmarks.Util)</a>
|
||||
</li>
|
||||
@ -1891,6 +1981,8 @@
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.tabular_dataframe_columns">tabular_dataframe_columns() (in module pyFTS.benchmarks.Util)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.TheilsInequality">TheilsInequality() (in module pyFTS.benchmarks.Measures)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.common.DateTime.third">third (pyFTS.models.seasonal.common.DateTime attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.partitioner.TimeGridPartitioner">TimeGridPartitioner (class in pyFTS.models.seasonal.partitioner)</a>
|
||||
</li>
|
||||
@ -1915,7 +2007,9 @@
|
||||
</li>
|
||||
<li><a href="pyFTS.models.html#pyFTS.models.hwang.HighOrderFTS.train">(pyFTS.models.hwang.HighOrderFTS method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.Retrainer.Retrainer.train">(pyFTS.models.incremental.Retrainer.Retrainer method)</a>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.train">(pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.train">(pyFTS.models.incremental.TimeVariant.Retrainer method)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFTS.train">(pyFTS.models.ismailefendi.ImprovedWeightedFTS method)</a>
|
||||
</li>
|
||||
@ -1958,8 +2052,12 @@
|
||||
</ul></li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.Transformations.Transformation">Transformation (class in pyFTS.common.Transformations)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.transformation">transformation (pyFTS.partitioners.partitioner.Partitioner attribute)</a>
|
||||
<li><a href="pyFTS.models.multivariate.html#pyFTS.models.multivariate.variable.Variable.transformation">transformation (pyFTS.models.multivariate.variable.Variable attribute)</a>
|
||||
|
||||
<ul>
|
||||
<li><a href="pyFTS.partitioners.html#pyFTS.partitioners.partitioner.Partitioner.transformation">(pyFTS.partitioners.partitioner.Partitioner attribute)</a>
|
||||
</li>
|
||||
</ul></li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.transformations">transformations (pyFTS.common.fts.FTS attribute)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.transformations_param">transformations_param (pyFTS.common.fts.FTS attribute)</a>
|
||||
@ -2074,8 +2172,12 @@
|
||||
</li>
|
||||
<li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.common.window_index">window_index() (in module pyFTS.models.nonstationary.common)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.Retrainer.Retrainer.window_length">window_length (pyFTS.models.incremental.Retrainer.Retrainer attribute)</a>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.window_length">window_length (pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS attribute)</a>
|
||||
|
||||
<ul>
|
||||
<li><a href="pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.window_length">(pyFTS.models.incremental.TimeVariant.Retrainer attribute)</a>
|
||||
</li>
|
||||
</ul></li>
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.winkler_mean">winkler_mean() (in module pyFTS.benchmarks.Measures)</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.winkler_score">winkler_score() (in module pyFTS.benchmarks.Measures)</a>
|
||||
|
||||
20
docs/build/html/modules.html
vendored
20
docs/build/html/modules.html
vendored
@ -130,6 +130,7 @@
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.data.html#submodules">Submodules</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.data.html#module-pyFTS.data.common">pyFTS.data.common module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.data.html#datasets">Datasets</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.data.html#module-pyFTS.data.artificial">Artificial and synthetic data generators</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.data.html#module-pyFTS.data.AirPassengers">AirPassengers dataset</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.data.html#module-pyFTS.data.Bitcoin">Bitcoin dataset</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.data.html#module-pyFTS.data.DowJones">DowJones dataset</a></li>
|
||||
@ -144,7 +145,6 @@
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.data.html#module-pyFTS.data.SONDA">SONDA dataset</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.data.html#module-pyFTS.data.SP500">S&P 500 dataset</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.data.html#module-pyFTS.data.TAIEX">TAIEX dataset</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.data.html#module-pyFTS.data.artificial">pyFTS.data.artificial module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.data.html#module-pyFTS.data.henon">Henon chaotic time series</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.data.html#module-pyFTS.data.logistic_map">Logistic_map chaotic time series</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.data.html#module-pyFTS.data.lorentz">Lorentz chaotic time series</a></li>
|
||||
@ -153,16 +153,31 @@
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.data.html#module-pyFTS.data.sunspots">Sunspots dataset</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="pyFTS.distributed.html">pyFTS.distributed package</a><ul>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.distributed.html#module-pyFTS.distributed">Module contents</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.distributed.html#submodules">Submodules</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.distributed.html#pyfts-distributed-dispy-module">pyFTS.distributed.dispy module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.distributed.html#module-pyFTS.distributed.spark">pyFTS.distributed.spark module</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="pyFTS.hyperparam.html">pyFTS.hyperparam package</a><ul>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.hyperparam.html#module-pyFTS.hyperparam">Module contents</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.hyperparam.html#submodules">Submodules</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.hyperparam.html#module-pyFTS.hyperparam.Util">pyFTS.hyperparam.Util module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.hyperparam.html#module-pyFTS.hyperparam.GridSearch">pyFTS.hyperparam.GridSearch module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.hyperparam.html#pyfts-hyperparam-evolutionary-module">pyFTS.hyperparam.Evolutionary module</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l3"><a class="reference internal" href="pyFTS.models.html">pyFTS.models package</a><ul>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.html#module-pyFTS.models">Module contents</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.html#subpackages">Subpackages</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.html#subpackages">Subpackages</a><ul>
|
||||
<li class="toctree-l5"><a class="reference internal" href="pyFTS.models.ensemble.html">pyFTS.models.ensemble package</a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="pyFTS.models.incremental.html">pyFTS.models.incremental package</a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="pyFTS.models.multivariate.html">pyFTS.models.multivariate package</a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="pyFTS.models.nonstationary.html">pyFTS.models.nonstationary package</a></li>
|
||||
<li class="toctree-l5"><a class="reference internal" href="pyFTS.models.seasonal.html">pyFTS.models.seasonal package</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.html#submodules">Submodules</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.html#module-pyFTS.models.song">pyFTS.models.song module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.html#module-pyFTS.models.chen">pyFTS.models.chen module</a></li>
|
||||
@ -186,6 +201,7 @@
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.Grid">pyFTS.partitioners.Grid module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.Huarng">pyFTS.partitioners.Huarng module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.Singleton">pyFTS.partitioners.Singleton module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.Simple">pyFTS.partitioners.Simple module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.Util">pyFTS.partitioners.Util module</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.parallel_util">pyFTS.partitioners.parallel_util module</a></li>
|
||||
</ul>
|
||||
|
||||
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@ -310,6 +310,16 @@
|
||||
<td>   
|
||||
<a href="pyFTS.data.html#module-pyFTS.data.TAIEX"><code class="xref">pyFTS.data.TAIEX</code></a></td><td>
|
||||
<em></em></td></tr>
|
||||
<tr class="cg-1">
|
||||
<td></td>
|
||||
<td>   
|
||||
<a href="pyFTS.distributed.html#module-pyFTS.distributed"><code class="xref">pyFTS.distributed</code></a></td><td>
|
||||
<em></em></td></tr>
|
||||
<tr class="cg-1">
|
||||
<td></td>
|
||||
<td>   
|
||||
<a href="pyFTS.distributed.html#module-pyFTS.distributed.spark"><code class="xref">pyFTS.distributed.spark</code></a></td><td>
|
||||
<em></em></td></tr>
|
||||
<tr class="cg-1">
|
||||
<td></td>
|
||||
<td>   
|
||||
@ -378,7 +388,12 @@
|
||||
<tr class="cg-1">
|
||||
<td></td>
|
||||
<td>   
|
||||
<a href="pyFTS.models.incremental.html#module-pyFTS.models.incremental.Retrainer"><code class="xref">pyFTS.models.incremental.Retrainer</code></a></td><td>
|
||||
<a href="pyFTS.models.incremental.html#module-pyFTS.models.incremental.IncrementalEnsemble"><code class="xref">pyFTS.models.incremental.IncrementalEnsemble</code></a></td><td>
|
||||
<em></em></td></tr>
|
||||
<tr class="cg-1">
|
||||
<td></td>
|
||||
<td>   
|
||||
<a href="pyFTS.models.incremental.html#module-pyFTS.models.incremental.TimeVariant"><code class="xref">pyFTS.models.incremental.TimeVariant</code></a></td><td>
|
||||
<em></em></td></tr>
|
||||
<tr class="cg-1">
|
||||
<td></td>
|
||||
|
||||
11
docs/build/html/pyFTS.common.html
vendored
11
docs/build/html/pyFTS.common.html
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@ -1811,6 +1811,12 @@ a monovariate method, default: False</p>
|
||||
<dd><p>A boolean value indicating if the model support multivariate time series (Pandas DataFrame), default: False</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.common.fts.FTS.is_wrapper">
|
||||
<code class="descname">is_wrapper</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.common.fts.FTS.is_wrapper" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Indicates that this model is a wrapper for other(s) method(s)</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.common.fts.FTS.lags">
|
||||
<code class="descname">lags</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.common.fts.FTS.lags" title="Permalink to this definition">¶</a></dt>
|
||||
@ -1907,8 +1913,9 @@ needed to forecast a single step ahead</p>
|
||||
<li><strong>nodes</strong> – a list with the dispy cluster nodes addresses</li>
|
||||
<li><strong>explain</strong> – try to explain, step by step, the one-step-ahead point forecasting result given the input data.</li>
|
||||
<li><strong>generators</strong> – for multivariate methods on multi step ahead forecasting, generators is a dict where the keys
|
||||
are the variables names (except the target_variable) and the values are lambda functions that
|
||||
accept one value (the actual value of the variable) and return the next value.</li>
|
||||
are the dataframe columun names (except the target_variable) and the values are lambda functions that
|
||||
accept one value (the actual value of the variable) and return the next value or trained FTS
|
||||
models that accept the actual values and forecast new ones.</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
|
||||
347
docs/build/html/pyFTS.data.html
vendored
347
docs/build/html/pyFTS.data.html
vendored
@ -28,7 +28,7 @@
|
||||
<script type="text/javascript" src="_static/bizstyle.js"></script>
|
||||
<link rel="index" title="Index" href="genindex.html" />
|
||||
<link rel="search" title="Search" href="search.html" />
|
||||
<link rel="next" title="pyFTS.hyperparam package" href="pyFTS.hyperparam.html" />
|
||||
<link rel="next" title="pyFTS.distributed package" href="pyFTS.distributed.html" />
|
||||
<link rel="prev" title="pyFTS.common package" href="pyFTS.common.html" />
|
||||
<meta name="viewport" content="width=device-width,initial-scale=1.0">
|
||||
<!--[if lt IE 9]>
|
||||
@ -45,7 +45,7 @@
|
||||
<a href="py-modindex.html" title="Python Module Index"
|
||||
>modules</a> |</li>
|
||||
<li class="right" >
|
||||
<a href="pyFTS.hyperparam.html" title="pyFTS.hyperparam package"
|
||||
<a href="pyFTS.distributed.html" title="pyFTS.distributed package"
|
||||
accesskey="N">next</a> |</li>
|
||||
<li class="right" >
|
||||
<a href="pyFTS.common.html" title="pyFTS.common package"
|
||||
@ -67,6 +67,7 @@
|
||||
<li><a class="reference internal" href="#submodules">Submodules</a></li>
|
||||
<li><a class="reference internal" href="#module-pyFTS.data.common">pyFTS.data.common module</a></li>
|
||||
<li><a class="reference internal" href="#datasets">Datasets</a></li>
|
||||
<li><a class="reference internal" href="#module-pyFTS.data.artificial">Artificial and synthetic data generators</a></li>
|
||||
<li><a class="reference internal" href="#module-pyFTS.data.AirPassengers">AirPassengers dataset</a></li>
|
||||
<li><a class="reference internal" href="#module-pyFTS.data.Bitcoin">Bitcoin dataset</a></li>
|
||||
<li><a class="reference internal" href="#module-pyFTS.data.DowJones">DowJones dataset</a></li>
|
||||
@ -81,7 +82,6 @@
|
||||
<li><a class="reference internal" href="#module-pyFTS.data.SONDA">SONDA dataset</a></li>
|
||||
<li><a class="reference internal" href="#module-pyFTS.data.SP500">S&P 500 dataset</a></li>
|
||||
<li><a class="reference internal" href="#module-pyFTS.data.TAIEX">TAIEX dataset</a></li>
|
||||
<li><a class="reference internal" href="#module-pyFTS.data.artificial">pyFTS.data.artificial module</a></li>
|
||||
<li><a class="reference internal" href="#module-pyFTS.data.henon">Henon chaotic time series</a></li>
|
||||
<li><a class="reference internal" href="#module-pyFTS.data.logistic_map">Logistic_map chaotic time series</a></li>
|
||||
<li><a class="reference internal" href="#module-pyFTS.data.lorentz">Lorentz chaotic time series</a></li>
|
||||
@ -96,8 +96,8 @@
|
||||
<p class="topless"><a href="pyFTS.common.html"
|
||||
title="previous chapter">pyFTS.common package</a></p>
|
||||
<h4>Next topic</h4>
|
||||
<p class="topless"><a href="pyFTS.hyperparam.html"
|
||||
title="next chapter">pyFTS.hyperparam package</a></p>
|
||||
<p class="topless"><a href="pyFTS.distributed.html"
|
||||
title="next chapter">pyFTS.distributed package</a></p>
|
||||
<div role="note" aria-label="source link">
|
||||
<h3>This Page</h3>
|
||||
<ul class="this-page-menu">
|
||||
@ -165,6 +165,273 @@ If the file don’t already exists, it will be downloaded and decompressed.</p>
|
||||
</div>
|
||||
<div class="section" id="datasets">
|
||||
<h2>Datasets<a class="headerlink" href="#datasets" title="Permalink to this headline">¶</a></h2>
|
||||
</div>
|
||||
<div class="section" id="module-pyFTS.data.artificial">
|
||||
<span id="artificial-and-synthetic-data-generators"></span><h2>Artificial and synthetic data generators<a class="headerlink" href="#module-pyFTS.data.artificial" title="Permalink to this headline">¶</a></h2>
|
||||
<p>Facilities to generate synthetic stochastic processes</p>
|
||||
<dl class="class">
|
||||
<dt id="pyFTS.data.artificial.SignalEmulator">
|
||||
<em class="property">class </em><code class="descclassname">pyFTS.data.artificial.</code><code class="descname">SignalEmulator</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#SignalEmulator"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.data.artificial.SignalEmulator" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.7)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
|
||||
<p>Emulate a complex signal built from several additive and non-additive components</p>
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.data.artificial.SignalEmulator.blip">
|
||||
<code class="descname">blip</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#SignalEmulator.blip"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.data.artificial.SignalEmulator.blip" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Creates an outlier greater than the maximum or lower then the minimum previous values of the signal,
|
||||
and insert it on a random location of the signal.</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">the current SignalEmulator instance, for method chaining</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.data.artificial.SignalEmulator.components">
|
||||
<code class="descname">components</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.data.artificial.SignalEmulator.components" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Components of the signal</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.data.artificial.SignalEmulator.incremental_gaussian">
|
||||
<code class="descname">incremental_gaussian</code><span class="sig-paren">(</span><em>mu</em>, <em>sigma</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#SignalEmulator.incremental_gaussian"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.data.artificial.SignalEmulator.incremental_gaussian" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Creates an additive gaussian interference on a previous signal</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>mu</strong> – increment on mean</li>
|
||||
<li><strong>sigma</strong> – increment on variance</li>
|
||||
<li><strong>start</strong> – lag index to start this signal, the default value is 0</li>
|
||||
<li><strong>it</strong> – Number of iterations, the default value is 1</li>
|
||||
<li><strong>length</strong> – Number of samples generated on each iteration, the default value is 100</li>
|
||||
<li><strong>vmin</strong> – Lower bound value of generated data, the default value is None</li>
|
||||
<li><strong>vmax</strong> – Upper bound value of generated data, the default value is None</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">the current SignalEmulator instance, for method chaining</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.data.artificial.SignalEmulator.periodic_gaussian">
|
||||
<code class="descname">periodic_gaussian</code><span class="sig-paren">(</span><em>type</em>, <em>period</em>, <em>mu_min</em>, <em>sigma_min</em>, <em>mu_max</em>, <em>sigma_max</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#SignalEmulator.periodic_gaussian"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.data.artificial.SignalEmulator.periodic_gaussian" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Creates an additive periodic gaussian interference on a previous signal</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>type</strong> – ‘linear’ or ‘sinoidal’</li>
|
||||
<li><strong>period</strong> – the period of recurrence</li>
|
||||
<li><strong>mu</strong> – increment on mean</li>
|
||||
<li><strong>sigma</strong> – increment on variance</li>
|
||||
<li><strong>start</strong> – lag index to start this signal, the default value is 0</li>
|
||||
<li><strong>it</strong> – Number of iterations, the default value is 1</li>
|
||||
<li><strong>length</strong> – Number of samples generated on each iteration, the default value is 100</li>
|
||||
<li><strong>vmin</strong> – Lower bound value of generated data, the default value is None</li>
|
||||
<li><strong>vmax</strong> – Upper bound value of generated data, the default value is None</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">the current SignalEmulator instance, for method chaining</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.data.artificial.SignalEmulator.run">
|
||||
<code class="descname">run</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#SignalEmulator.run"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.data.artificial.SignalEmulator.run" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Render the signal</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">a list of float values</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.data.artificial.SignalEmulator.stationary_gaussian">
|
||||
<code class="descname">stationary_gaussian</code><span class="sig-paren">(</span><em>mu</em>, <em>sigma</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#SignalEmulator.stationary_gaussian"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.data.artificial.SignalEmulator.stationary_gaussian" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Creates a continuous Gaussian signal with mean mu and variance sigma.</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>mu</strong> – mean</li>
|
||||
<li><strong>sigma</strong> – variance</li>
|
||||
<li><strong>additive</strong> – If False it cancels the previous signal and start this one, if True
|
||||
this signal is added to the previous one</li>
|
||||
<li><strong>start</strong> – lag index to start this signal, the default value is 0</li>
|
||||
<li><strong>it</strong> – Number of iterations, the default value is 1</li>
|
||||
<li><strong>length</strong> – Number of samples generated on each iteration, the default value is 100</li>
|
||||
<li><strong>vmin</strong> – Lower bound value of generated data, the default value is None</li>
|
||||
<li><strong>vmax</strong> – Upper bound value of generated data, the default value is None</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">the current SignalEmulator instance, for method chaining</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.data.artificial.generate_gaussian_linear">
|
||||
<code class="descclassname">pyFTS.data.artificial.</code><code class="descname">generate_gaussian_linear</code><span class="sig-paren">(</span><em>mu_ini</em>, <em>sigma_ini</em>, <em>mu_inc</em>, <em>sigma_inc</em>, <em>it=100</em>, <em>num=10</em>, <em>vmin=None</em>, <em>vmax=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#generate_gaussian_linear"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.data.artificial.generate_gaussian_linear" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Generate data sampled from Gaussian distribution, with constant or linear changing parameters</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>mu_ini</strong> – Initial mean</li>
|
||||
<li><strong>sigma_ini</strong> – Initial variance</li>
|
||||
<li><strong>mu_inc</strong> – Mean increment after ‘num’ samples</li>
|
||||
<li><strong>sigma_inc</strong> – Variance increment after ‘num’ samples</li>
|
||||
<li><strong>it</strong> – Number of iterations</li>
|
||||
<li><strong>num</strong> – Number of samples generated on each iteration</li>
|
||||
<li><strong>vmin</strong> – Lower bound value of generated data</li>
|
||||
<li><strong>vmax</strong> – Upper bound value of generated data</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">A list of it*num float values</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.data.artificial.generate_linear_periodic_gaussian">
|
||||
<code class="descclassname">pyFTS.data.artificial.</code><code class="descname">generate_linear_periodic_gaussian</code><span class="sig-paren">(</span><em>period</em>, <em>mu_min</em>, <em>sigma_min</em>, <em>mu_max</em>, <em>sigma_max</em>, <em>it=100</em>, <em>num=10</em>, <em>vmin=None</em>, <em>vmax=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#generate_linear_periodic_gaussian"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.data.artificial.generate_linear_periodic_gaussian" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Generates a periodic linear variation on mean and variance</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>period</strong> – the period of recurrence</li>
|
||||
<li><strong>mu_min</strong> – initial (and minimum) mean of each period</li>
|
||||
<li><strong>sigma_min</strong> – initial (and minimum) variance of each period</li>
|
||||
<li><strong>mu_max</strong> – final (and maximum) mean of each period</li>
|
||||
<li><strong>sigma_max</strong> – final (and maximum) variance of each period</li>
|
||||
<li><strong>it</strong> – Number of iterations</li>
|
||||
<li><strong>num</strong> – Number of samples generated on each iteration</li>
|
||||
<li><strong>vmin</strong> – Lower bound value of generated data</li>
|
||||
<li><strong>vmax</strong> – Upper bound value of generated data</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">A list of it*num float values</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.data.artificial.generate_sinoidal_periodic_gaussian">
|
||||
<code class="descclassname">pyFTS.data.artificial.</code><code class="descname">generate_sinoidal_periodic_gaussian</code><span class="sig-paren">(</span><em>period</em>, <em>mu_min</em>, <em>sigma_min</em>, <em>mu_max</em>, <em>sigma_max</em>, <em>it=100</em>, <em>num=10</em>, <em>vmin=None</em>, <em>vmax=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#generate_sinoidal_periodic_gaussian"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.data.artificial.generate_sinoidal_periodic_gaussian" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Generates a periodic sinoidal variation on mean and variance</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>period</strong> – the period of recurrence</li>
|
||||
<li><strong>mu_min</strong> – initial (and minimum) mean of each period</li>
|
||||
<li><strong>sigma_min</strong> – initial (and minimum) variance of each period</li>
|
||||
<li><strong>mu_max</strong> – final (and maximum) mean of each period</li>
|
||||
<li><strong>sigma_max</strong> – final (and maximum) variance of each period</li>
|
||||
<li><strong>it</strong> – Number of iterations</li>
|
||||
<li><strong>num</strong> – Number of samples generated on each iteration</li>
|
||||
<li><strong>vmin</strong> – Lower bound value of generated data</li>
|
||||
<li><strong>vmax</strong> – Upper bound value of generated data</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">A list of it*num float values</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.data.artificial.generate_uniform_linear">
|
||||
<code class="descclassname">pyFTS.data.artificial.</code><code class="descname">generate_uniform_linear</code><span class="sig-paren">(</span><em>min_ini</em>, <em>max_ini</em>, <em>min_inc</em>, <em>max_inc</em>, <em>it=100</em>, <em>num=10</em>, <em>vmin=None</em>, <em>vmax=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#generate_uniform_linear"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.data.artificial.generate_uniform_linear" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Generate data sampled from Uniform distribution, with constant or linear changing bounds</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>mu_ini</strong> – Initial mean</li>
|
||||
<li><strong>sigma_ini</strong> – Initial variance</li>
|
||||
<li><strong>mu_inc</strong> – Mean increment after ‘num’ samples</li>
|
||||
<li><strong>sigma_inc</strong> – Variance increment after ‘num’ samples</li>
|
||||
<li><strong>it</strong> – Number of iterations</li>
|
||||
<li><strong>num</strong> – Number of samples generated on each iteration</li>
|
||||
<li><strong>vmin</strong> – Lower bound value of generated data</li>
|
||||
<li><strong>vmax</strong> – Upper bound value of generated data</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">A list of it*num float values</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.data.artificial.random_walk">
|
||||
<code class="descclassname">pyFTS.data.artificial.</code><code class="descname">random_walk</code><span class="sig-paren">(</span><em>n=500</em>, <em>type='gaussian'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#random_walk"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.data.artificial.random_walk" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Simple random walk</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>n</strong> – number of samples</li>
|
||||
<li><strong>type</strong> – ‘gaussian’ or ‘uniform’</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"></p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.data.artificial.white_noise">
|
||||
<code class="descclassname">pyFTS.data.artificial.</code><code class="descname">white_noise</code><span class="sig-paren">(</span><em>n=500</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#white_noise"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.data.artificial.white_noise" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Simple Gaussian noise signal
|
||||
:param n: number of samples
|
||||
:return:</p>
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-pyFTS.data.AirPassengers">
|
||||
<span id="airpassengers-dataset"></span><h2>AirPassengers dataset<a class="headerlink" href="#module-pyFTS.data.AirPassengers" title="Permalink to this headline">¶</a></h2>
|
||||
@ -628,74 +895,6 @@ If the file don’t already exists, it will be downloaded and decompressed.</p>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-pyFTS.data.artificial">
|
||||
<span id="pyfts-data-artificial-module"></span><h2>pyFTS.data.artificial module<a class="headerlink" href="#module-pyFTS.data.artificial" title="Permalink to this headline">¶</a></h2>
|
||||
<p>Facilities to generate synthetic stochastic processes</p>
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.data.artificial.generate_gaussian_linear">
|
||||
<code class="descclassname">pyFTS.data.artificial.</code><code class="descname">generate_gaussian_linear</code><span class="sig-paren">(</span><em>mu_ini</em>, <em>sigma_ini</em>, <em>mu_inc</em>, <em>sigma_inc</em>, <em>it=100</em>, <em>num=10</em>, <em>vmin=None</em>, <em>vmax=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#generate_gaussian_linear"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.data.artificial.generate_gaussian_linear" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Generate data sampled from Gaussian distribution, with constant or linear changing parameters</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>mu_ini</strong> – Initial mean</li>
|
||||
<li><strong>sigma_ini</strong> – Initial variance</li>
|
||||
<li><strong>mu_inc</strong> – Mean increment after ‘num’ samples</li>
|
||||
<li><strong>sigma_inc</strong> – Variance increment after ‘num’ samples</li>
|
||||
<li><strong>it</strong> – Number of iterations</li>
|
||||
<li><strong>num</strong> – Number of samples generated on each iteration</li>
|
||||
<li><strong>vmin</strong> – Lower bound value of generated data</li>
|
||||
<li><strong>vmax</strong> – Upper bound value of generated data</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">A list of it*num float values</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.data.artificial.generate_uniform_linear">
|
||||
<code class="descclassname">pyFTS.data.artificial.</code><code class="descname">generate_uniform_linear</code><span class="sig-paren">(</span><em>min_ini</em>, <em>max_ini</em>, <em>min_inc</em>, <em>max_inc</em>, <em>it=100</em>, <em>num=10</em>, <em>vmin=None</em>, <em>vmax=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#generate_uniform_linear"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.data.artificial.generate_uniform_linear" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Generate data sampled from Uniform distribution, with constant or linear changing bounds</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>mu_ini</strong> – Initial mean</li>
|
||||
<li><strong>sigma_ini</strong> – Initial variance</li>
|
||||
<li><strong>mu_inc</strong> – Mean increment after ‘num’ samples</li>
|
||||
<li><strong>sigma_inc</strong> – Variance increment after ‘num’ samples</li>
|
||||
<li><strong>it</strong> – Number of iterations</li>
|
||||
<li><strong>num</strong> – Number of samples generated on each iteration</li>
|
||||
<li><strong>vmin</strong> – Lower bound value of generated data</li>
|
||||
<li><strong>vmax</strong> – Upper bound value of generated data</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">A list of it*num float values</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.data.artificial.random_walk">
|
||||
<code class="descclassname">pyFTS.data.artificial.</code><code class="descname">random_walk</code><span class="sig-paren">(</span><em>n=500</em>, <em>type='gaussian'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#random_walk"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.data.artificial.random_walk" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.data.artificial.white_noise">
|
||||
<code class="descclassname">pyFTS.data.artificial.</code><code class="descname">white_noise</code><span class="sig-paren">(</span><em>n=500</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/data/artificial.html#white_noise"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.data.artificial.white_noise" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-pyFTS.data.henon">
|
||||
<span id="henon-chaotic-time-series"></span><h2>Henon chaotic time series<a class="headerlink" href="#module-pyFTS.data.henon" title="Permalink to this headline">¶</a></h2>
|
||||
@ -955,7 +1154,7 @@ dz/dt = b + z( x - c )</p>
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<a href="pyFTS.common.html" title="pyFTS.common package"
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283
docs/build/html/pyFTS.distributed.html
vendored
Normal file
283
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vendored
Normal file
@ -0,0 +1,283 @@
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<li><a class="reference internal" href="#submodules">Submodules</a></li>
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<li><a class="reference internal" href="#pyfts-distributed-dispy-module">pyFTS.distributed.dispy module</a></li>
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<li><a class="reference internal" href="#module-pyFTS.distributed.spark">pyFTS.distributed.spark module</a></li>
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<div class="section" id="pyfts-distributed-package">
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<h1>pyFTS.distributed package<a class="headerlink" href="#pyfts-distributed-package" title="Permalink to this headline">¶</a></h1>
|
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<div class="section" id="module-pyFTS.distributed">
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<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-pyFTS.distributed" title="Permalink to this headline">¶</a></h2>
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</div>
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<div class="section" id="submodules">
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<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
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<div class="section" id="pyfts-distributed-dispy-module">
|
||||
<h2>pyFTS.distributed.dispy module<a class="headerlink" href="#pyfts-distributed-dispy-module" title="Permalink to this headline">¶</a></h2>
|
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</div>
|
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<div class="section" id="module-pyFTS.distributed.spark">
|
||||
<span id="pyfts-distributed-spark-module"></span><h2>pyFTS.distributed.spark module<a class="headerlink" href="#module-pyFTS.distributed.spark" title="Permalink to this headline">¶</a></h2>
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.distributed.spark.create_multivariate_model">
|
||||
<code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">create_multivariate_model</code><span class="sig-paren">(</span><em>**parameters</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/distributed/spark.html#create_multivariate_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.distributed.spark.create_multivariate_model" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.distributed.spark.create_spark_conf">
|
||||
<code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">create_spark_conf</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/distributed/spark.html#create_spark_conf"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.distributed.spark.create_spark_conf" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.distributed.spark.create_univariate_model">
|
||||
<code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">create_univariate_model</code><span class="sig-paren">(</span><em>**parameters</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/distributed/spark.html#create_univariate_model"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.distributed.spark.create_univariate_model" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.distributed.spark.distributed_predict">
|
||||
<code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">distributed_predict</code><span class="sig-paren">(</span><em>data</em>, <em>model</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/distributed/spark.html#distributed_predict"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.distributed.spark.distributed_predict" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>model</strong> – </li>
|
||||
<li><strong>data</strong> – </li>
|
||||
<li><strong>url</strong> – </li>
|
||||
<li><strong>app</strong> – </li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"></p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.distributed.spark.distributed_train">
|
||||
<code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">distributed_train</code><span class="sig-paren">(</span><em>model</em>, <em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/distributed/spark.html#distributed_train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.distributed.spark.distributed_train" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>model</strong> – </li>
|
||||
<li><strong>data</strong> – </li>
|
||||
<li><strong>url</strong> – </li>
|
||||
<li><strong>app</strong> – </li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"></p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.distributed.spark.get_clustered_partitioner">
|
||||
<code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">get_clustered_partitioner</code><span class="sig-paren">(</span><em>explanatory_variables</em>, <em>target_variable</em>, <em>**parameters</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/distributed/spark.html#get_clustered_partitioner"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.distributed.spark.get_clustered_partitioner" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.distributed.spark.get_partitioner">
|
||||
<code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">get_partitioner</code><span class="sig-paren">(</span><em>shared_partitioner</em>, <em>type='common'</em>, <em>variables=[]</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/distributed/spark.html#get_partitioner"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.distributed.spark.get_partitioner" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>part</strong> – </td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"></td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.distributed.spark.get_variables">
|
||||
<code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">get_variables</code><span class="sig-paren">(</span><em>**parameters</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/distributed/spark.html#get_variables"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.distributed.spark.get_variables" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.distributed.spark.share_parameters">
|
||||
<code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">share_parameters</code><span class="sig-paren">(</span><em>model</em>, <em>context</em>, <em>data</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/distributed/spark.html#share_parameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.distributed.spark.share_parameters" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.distributed.spark.slave_forecast_multivariate">
|
||||
<code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">slave_forecast_multivariate</code><span class="sig-paren">(</span><em>data</em>, <em>**parameters</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/distributed/spark.html#slave_forecast_multivariate"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.distributed.spark.slave_forecast_multivariate" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.distributed.spark.slave_forecast_univariate">
|
||||
<code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">slave_forecast_univariate</code><span class="sig-paren">(</span><em>data</em>, <em>**parameters</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/distributed/spark.html#slave_forecast_univariate"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.distributed.spark.slave_forecast_univariate" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>data</strong> – </td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"></td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.distributed.spark.slave_train_multivariate">
|
||||
<code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">slave_train_multivariate</code><span class="sig-paren">(</span><em>data</em>, <em>**parameters</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/distributed/spark.html#slave_train_multivariate"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.distributed.spark.slave_train_multivariate" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.distributed.spark.slave_train_univariate">
|
||||
<code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">slave_train_univariate</code><span class="sig-paren">(</span><em>data</em>, <em>**parameters</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/distributed/spark.html#slave_train_univariate"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.distributed.spark.slave_train_univariate" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>data</strong> – </td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"></td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
</div>
|
||||
|
||||
|
||||
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|
||||
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13
docs/build/html/pyFTS.html
vendored
13
docs/build/html/pyFTS.html
vendored
@ -142,6 +142,7 @@
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<li class="toctree-l2"><a class="reference internal" href="pyFTS.data.html#submodules">Submodules</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="pyFTS.data.html#module-pyFTS.data.common">pyFTS.data.common module</a></li>
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<li class="toctree-l2"><a class="reference internal" href="pyFTS.data.html#datasets">Datasets</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="pyFTS.data.html#module-pyFTS.data.artificial">Artificial and synthetic data generators</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="pyFTS.data.html#module-pyFTS.data.AirPassengers">AirPassengers dataset</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="pyFTS.data.html#module-pyFTS.data.Bitcoin">Bitcoin dataset</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="pyFTS.data.html#module-pyFTS.data.DowJones">DowJones dataset</a></li>
|
||||
@ -156,7 +157,6 @@
|
||||
<li class="toctree-l2"><a class="reference internal" href="pyFTS.data.html#module-pyFTS.data.SONDA">SONDA dataset</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="pyFTS.data.html#module-pyFTS.data.SP500">S&P 500 dataset</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="pyFTS.data.html#module-pyFTS.data.TAIEX">TAIEX dataset</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="pyFTS.data.html#module-pyFTS.data.artificial">pyFTS.data.artificial module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="pyFTS.data.html#module-pyFTS.data.henon">Henon chaotic time series</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="pyFTS.data.html#module-pyFTS.data.logistic_map">Logistic_map chaotic time series</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="pyFTS.data.html#module-pyFTS.data.lorentz">Lorentz chaotic time series</a></li>
|
||||
@ -165,11 +165,19 @@
|
||||
<li class="toctree-l2"><a class="reference internal" href="pyFTS.data.html#module-pyFTS.data.sunspots">Sunspots dataset</a></li>
|
||||
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|
||||
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|
||||
<li class="toctree-l1"><a class="reference internal" href="pyFTS.distributed.html">pyFTS.distributed package</a><ul>
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||||
<li class="toctree-l2"><a class="reference internal" href="pyFTS.distributed.html#module-pyFTS.distributed">Module contents</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="pyFTS.distributed.html#submodules">Submodules</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="pyFTS.distributed.html#pyfts-distributed-dispy-module">pyFTS.distributed.dispy module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="pyFTS.distributed.html#module-pyFTS.distributed.spark">pyFTS.distributed.spark module</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="pyFTS.hyperparam.html">pyFTS.hyperparam package</a><ul>
|
||||
<li class="toctree-l2"><a class="reference internal" href="pyFTS.hyperparam.html#module-pyFTS.hyperparam">Module contents</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="pyFTS.hyperparam.html#submodules">Submodules</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="pyFTS.hyperparam.html#module-pyFTS.hyperparam.Util">pyFTS.hyperparam.Util module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="pyFTS.hyperparam.html#module-pyFTS.hyperparam.GridSearch">pyFTS.hyperparam.GridSearch module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="pyFTS.hyperparam.html#pyfts-hyperparam-evolutionary-module">pyFTS.hyperparam.Evolutionary module</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="pyFTS.models.html">pyFTS.models package</a><ul>
|
||||
@ -185,7 +193,8 @@
|
||||
<li class="toctree-l3"><a class="reference internal" href="pyFTS.models.incremental.html">pyFTS.models.incremental package</a><ul>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.incremental.html#module-pyFTS.models.incremental">Module contents</a></li>
|
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<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.incremental.html#submodules">Submodules</a></li>
|
||||
<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.incremental.html#pyfts-models-incremental-retrainer-module">pyFTS.models.incremental.Retrainer module</a></li>
|
||||
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<li class="toctree-l4"><a class="reference internal" href="pyFTS.models.incremental.html#module-pyFTS.models.incremental.IncrementalEnsemble">pyFTS.models.incremental.IncrementalEnsemble module</a></li>
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||||
<li class="toctree-l3"><a class="reference internal" href="pyFTS.models.multivariate.html">pyFTS.models.multivariate package</a><ul>
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||||
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||||
14
docs/build/html/pyFTS.hyperparam.html
vendored
14
docs/build/html/pyFTS.hyperparam.html
vendored
@ -29,7 +29,7 @@
|
||||
<link rel="index" title="Index" href="genindex.html" />
|
||||
<link rel="search" title="Search" href="search.html" />
|
||||
<link rel="next" title="pyFTS.models package" href="pyFTS.models.html" />
|
||||
<link rel="prev" title="pyFTS.data package" href="pyFTS.data.html" />
|
||||
<link rel="prev" title="pyFTS.distributed package" href="pyFTS.distributed.html" />
|
||||
<meta name="viewport" content="width=device-width,initial-scale=1.0">
|
||||
<!--[if lt IE 9]>
|
||||
<script type="text/javascript" src="_static/css3-mediaqueries.js"></script>
|
||||
@ -48,7 +48,7 @@
|
||||
<a href="pyFTS.models.html" title="pyFTS.models package"
|
||||
accesskey="N">next</a> |</li>
|
||||
<li class="right" >
|
||||
<a href="pyFTS.data.html" title="pyFTS.data package"
|
||||
<a href="pyFTS.distributed.html" title="pyFTS.distributed package"
|
||||
accesskey="P">previous</a> |</li>
|
||||
<li class="nav-item nav-item-0"><a href="index.html">pyFTS 1.4 documentation</a> »</li>
|
||||
<li class="nav-item nav-item-1"><a href="modules.html" >pyFTS</a> »</li>
|
||||
@ -67,13 +67,14 @@
|
||||
<li><a class="reference internal" href="#submodules">Submodules</a></li>
|
||||
<li><a class="reference internal" href="#module-pyFTS.hyperparam.Util">pyFTS.hyperparam.Util module</a></li>
|
||||
<li><a class="reference internal" href="#module-pyFTS.hyperparam.GridSearch">pyFTS.hyperparam.GridSearch module</a></li>
|
||||
<li><a class="reference internal" href="#pyfts-hyperparam-evolutionary-module">pyFTS.hyperparam.Evolutionary module</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
|
||||
<h4>Previous topic</h4>
|
||||
<p class="topless"><a href="pyFTS.data.html"
|
||||
title="previous chapter">pyFTS.data package</a></p>
|
||||
<p class="topless"><a href="pyFTS.distributed.html"
|
||||
title="previous chapter">pyFTS.distributed package</a></p>
|
||||
<h4>Next topic</h4>
|
||||
<p class="topless"><a href="pyFTS.models.html"
|
||||
title="next chapter">pyFTS.models package</a></p>
|
||||
@ -204,6 +205,9 @@ Value: the measure value</p>
|
||||
<code class="descclassname">pyFTS.hyperparam.GridSearch.</code><code class="descname">process_jobs</code><span class="sig-paren">(</span><em>jobs</em>, <em>datasetname</em>, <em>conn</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/GridSearch.html#process_jobs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.GridSearch.process_jobs" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="pyfts-hyperparam-evolutionary-module">
|
||||
<h2>pyFTS.hyperparam.Evolutionary module<a class="headerlink" href="#pyfts-hyperparam-evolutionary-module" title="Permalink to this headline">¶</a></h2>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@ -226,7 +230,7 @@ Value: the measure value</p>
|
||||
<a href="pyFTS.models.html" title="pyFTS.models package"
|
||||
>next</a> |</li>
|
||||
<li class="right" >
|
||||
<a href="pyFTS.data.html" title="pyFTS.data package"
|
||||
<a href="pyFTS.distributed.html" title="pyFTS.distributed package"
|
||||
>previous</a> |</li>
|
||||
<li class="nav-item nav-item-0"><a href="index.html">pyFTS 1.4 documentation</a> »</li>
|
||||
<li class="nav-item nav-item-1"><a href="modules.html" >pyFTS</a> »</li>
|
||||
|
||||
3
docs/build/html/pyFTS.models.html
vendored
3
docs/build/html/pyFTS.models.html
vendored
@ -133,7 +133,8 @@
|
||||
<li class="toctree-l1"><a class="reference internal" href="pyFTS.models.incremental.html">pyFTS.models.incremental package</a><ul>
|
||||
<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.incremental.html#module-pyFTS.models.incremental">Module contents</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.incremental.html#submodules">Submodules</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.incremental.html#pyfts-models-incremental-retrainer-module">pyFTS.models.incremental.Retrainer module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.incremental.html#module-pyFTS.models.incremental.TimeVariant">pyFTS.models.incremental.TimeVariant module</a></li>
|
||||
<li class="toctree-l2"><a class="reference internal" href="pyFTS.models.incremental.html#module-pyFTS.models.incremental.IncrementalEnsemble">pyFTS.models.incremental.IncrementalEnsemble module</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li class="toctree-l1"><a class="reference internal" href="pyFTS.models.multivariate.html">pyFTS.models.multivariate package</a><ul>
|
||||
|
||||
143
docs/build/html/pyFTS.models.incremental.html
vendored
143
docs/build/html/pyFTS.models.incremental.html
vendored
@ -66,7 +66,8 @@
|
||||
<li><a class="reference internal" href="#">pyFTS.models.incremental package</a><ul>
|
||||
<li><a class="reference internal" href="#module-pyFTS.models.incremental">Module contents</a></li>
|
||||
<li><a class="reference internal" href="#submodules">Submodules</a></li>
|
||||
<li><a class="reference internal" href="#pyfts-models-incremental-retrainer-module">pyFTS.models.incremental.Retrainer module</a></li>
|
||||
<li><a class="reference internal" href="#module-pyFTS.models.incremental.TimeVariant">pyFTS.models.incremental.TimeVariant module</a></li>
|
||||
<li><a class="reference internal" href="#module-pyFTS.models.incremental.IncrementalEnsemble">pyFTS.models.incremental.IncrementalEnsemble module</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
@ -113,29 +114,29 @@
|
||||
<div class="section" id="submodules">
|
||||
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
|
||||
</div>
|
||||
<div class="section" id="pyfts-models-incremental-retrainer-module">
|
||||
<h2>pyFTS.models.incremental.Retrainer module<a class="headerlink" href="#pyfts-models-incremental-retrainer-module" title="Permalink to this headline">¶</a></h2>
|
||||
<span class="target" id="module-pyFTS.models.incremental.Retrainer"></span><p>Meta model that wraps another FTS method and continously retrain it using a data window with the most recent data</p>
|
||||
<div class="section" id="module-pyFTS.models.incremental.TimeVariant">
|
||||
<span id="pyfts-models-incremental-timevariant-module"></span><h2>pyFTS.models.incremental.TimeVariant module<a class="headerlink" href="#module-pyFTS.models.incremental.TimeVariant" title="Permalink to this headline">¶</a></h2>
|
||||
<p>Meta model that wraps another FTS method and continously retrain it using a data window with the most recent data</p>
|
||||
<dl class="class">
|
||||
<dt id="pyFTS.models.incremental.Retrainer.Retrainer">
|
||||
<em class="property">class </em><code class="descclassname">pyFTS.models.incremental.Retrainer.</code><code class="descname">Retrainer</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/incremental/Retrainer.html#Retrainer"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer" title="Permalink to this definition">¶</a></dt>
|
||||
<dt id="pyFTS.models.incremental.TimeVariant.Retrainer">
|
||||
<em class="property">class </em><code class="descclassname">pyFTS.models.incremental.TimeVariant.</code><code class="descname">Retrainer</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/incremental/TimeVariant.html#Retrainer"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="pyFTS.common.html#pyFTS.common.fts.FTS" title="pyFTS.common.fts.FTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.fts.FTS</span></code></a></p>
|
||||
<p>Meta model for incremental/online learning</p>
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.auto_update">
|
||||
<code class="descname">auto_update</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer.auto_update" title="Permalink to this definition">¶</a></dt>
|
||||
<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.auto_update">
|
||||
<code class="descname">auto_update</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.auto_update" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>If true the model is updated at each time and not recreated</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.batch_size">
|
||||
<code class="descname">batch_size</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer.batch_size" title="Permalink to this definition">¶</a></dt>
|
||||
<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.batch_size">
|
||||
<code class="descname">batch_size</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.batch_size" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The batch interval between each retraining</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.forecast">
|
||||
<code class="descname">forecast</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/incremental/Retrainer.html#Retrainer.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer.forecast" title="Permalink to this definition">¶</a></dt>
|
||||
<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.forecast">
|
||||
<code class="descname">forecast</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/incremental/TimeVariant.html#Retrainer.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.forecast" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Point forecast one step ahead</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
@ -155,44 +156,44 @@
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.fts_method">
|
||||
<code class="descname">fts_method</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer.fts_method" title="Permalink to this definition">¶</a></dt>
|
||||
<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.fts_method">
|
||||
<code class="descname">fts_method</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.fts_method" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The FTS method to be called when a new model is build</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.fts_params">
|
||||
<code class="descname">fts_params</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer.fts_params" title="Permalink to this definition">¶</a></dt>
|
||||
<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.fts_params">
|
||||
<code class="descname">fts_params</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.fts_params" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The FTS method specific parameters</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.model">
|
||||
<code class="descname">model</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer.model" title="Permalink to this definition">¶</a></dt>
|
||||
<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.model">
|
||||
<code class="descname">model</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.model" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The most recent trained model</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.partitioner">
|
||||
<code class="descname">partitioner</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer.partitioner" title="Permalink to this definition">¶</a></dt>
|
||||
<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.partitioner">
|
||||
<code class="descname">partitioner</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.partitioner" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The most recent trained partitioner</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.partitioner_method">
|
||||
<code class="descname">partitioner_method</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer.partitioner_method" title="Permalink to this definition">¶</a></dt>
|
||||
<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.partitioner_method">
|
||||
<code class="descname">partitioner_method</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.partitioner_method" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The partitioner method to be called when a new model is build</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.partitioner_params">
|
||||
<code class="descname">partitioner_params</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer.partitioner_params" title="Permalink to this definition">¶</a></dt>
|
||||
<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.partitioner_params">
|
||||
<code class="descname">partitioner_params</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.partitioner_params" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The partitioner method parameters</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.train">
|
||||
<code class="descname">train</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/incremental/Retrainer.html#Retrainer.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer.train" title="Permalink to this definition">¶</a></dt>
|
||||
<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.train">
|
||||
<code class="descname">train</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/incremental/TimeVariant.html#Retrainer.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.train" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Method specific parameter fitting</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
@ -209,8 +210,94 @@
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.incremental.Retrainer.Retrainer.window_length">
|
||||
<code class="descname">window_length</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.Retrainer.Retrainer.window_length" title="Permalink to this definition">¶</a></dt>
|
||||
<dt id="pyFTS.models.incremental.TimeVariant.Retrainer.window_length">
|
||||
<code class="descname">window_length</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.TimeVariant.Retrainer.window_length" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The memory window length</p>
|
||||
</dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-pyFTS.models.incremental.IncrementalEnsemble">
|
||||
<span id="pyfts-models-incremental-incrementalensemble-module"></span><h2>pyFTS.models.incremental.IncrementalEnsemble module<a class="headerlink" href="#module-pyFTS.models.incremental.IncrementalEnsemble" title="Permalink to this headline">¶</a></h2>
|
||||
<p>Time Variant/Incremental Ensemble of FTS methods</p>
|
||||
<dl class="class">
|
||||
<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS">
|
||||
<em class="property">class </em><code class="descclassname">pyFTS.models.incremental.IncrementalEnsemble.</code><code class="descname">IncrementalEnsembleFTS</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/incremental/IncrementalEnsemble.html#IncrementalEnsembleFTS"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference internal" href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS" title="pyFTS.models.ensemble.ensemble.EnsembleFTS"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.models.ensemble.ensemble.EnsembleFTS</span></code></a></p>
|
||||
<p>Time Variant/Incremental Ensemble of FTS methods</p>
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.batch_size">
|
||||
<code class="descname">batch_size</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.batch_size" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The batch interval between each retraining</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.forecast">
|
||||
<code class="descname">forecast</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/incremental/IncrementalEnsemble.html#IncrementalEnsembleFTS.forecast"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.forecast" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Point forecast one step ahead</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
|
||||
<li><strong>data</strong> – time series data with the minimal length equal to the max_lag of the model</li>
|
||||
<li><strong>kwargs</strong> – model specific parameters</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a list with the forecasted values</p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.fts_method">
|
||||
<code class="descname">fts_method</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.fts_method" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The FTS method to be called when a new model is build</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.fts_params">
|
||||
<code class="descname">fts_params</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.fts_params" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The FTS method specific parameters</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.partitioner_method">
|
||||
<code class="descname">partitioner_method</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.partitioner_method" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The partitioner method to be called when a new model is build</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.partitioner_params">
|
||||
<code class="descname">partitioner_params</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.partitioner_params" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The partitioner method parameters</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.train">
|
||||
<code class="descname">train</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/incremental/IncrementalEnsemble.html#IncrementalEnsembleFTS.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.train" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Method specific parameter fitting</p>
|
||||
<table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
|
||||
<li><strong>data</strong> – training time series data</li>
|
||||
<li><strong>kwargs</strong> – Method specific parameters</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.window_length">
|
||||
<code class="descname">window_length</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.window_length" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>The memory window length</p>
|
||||
</dd></dl>
|
||||
|
||||
|
||||
28
docs/build/html/pyFTS.models.multivariate.html
vendored
28
docs/build/html/pyFTS.models.multivariate.html
vendored
@ -189,7 +189,7 @@
|
||||
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.models.multivariate.common.fuzzyfy_instance">
|
||||
<code class="descclassname">pyFTS.models.multivariate.common.</code><code class="descname">fuzzyfy_instance</code><span class="sig-paren">(</span><em>data_point</em>, <em>var</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/common.html#fuzzyfy_instance"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.common.fuzzyfy_instance" title="Permalink to this definition">¶</a></dt>
|
||||
<code class="descclassname">pyFTS.models.multivariate.common.</code><code class="descname">fuzzyfy_instance</code><span class="sig-paren">(</span><em>data_point</em>, <em>var</em>, <em>tuples=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/common.html#fuzzyfy_instance"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.common.fuzzyfy_instance" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
@ -212,6 +212,12 @@ transformations and partitioners.</p>
|
||||
<dd><p>A string with the alias of the variable</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.multivariate.variable.Variable.alpha_cut">
|
||||
<code class="descname">alpha_cut</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.multivariate.variable.Variable.alpha_cut" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Minimal membership value to be considered on fuzzyfication process</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.multivariate.variable.Variable.apply_inverse_transformations">
|
||||
<code class="descname">apply_inverse_transformations</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/variable.html#Variable.apply_inverse_transformations"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.variable.Variable.apply_inverse_transformations" title="Permalink to this definition">¶</a></dt>
|
||||
@ -261,6 +267,18 @@ transformations and partitioners.</p>
|
||||
<dd><p>A string with the name of the variable</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.multivariate.variable.Variable.partitioner">
|
||||
<code class="descname">partitioner</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.multivariate.variable.Variable.partitioner" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>UoD partitioner for the variable data</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.multivariate.variable.Variable.transformation">
|
||||
<code class="descname">transformation</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.multivariate.variable.Variable.transformation" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Pre processing transformation for the variable</p>
|
||||
</dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
@ -448,6 +466,14 @@ transformations and partitioners.</p>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.models.multivariate.mvfts.product_dict">
|
||||
<code class="descclassname">pyFTS.models.multivariate.mvfts.</code><code class="descname">product_dict</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/multivariate/mvfts.html#product_dict"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.multivariate.mvfts.product_dict" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Code by Seth Johnson
|
||||
:param kwargs:
|
||||
:return:</p>
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
<div class="section" id="module-pyFTS.models.multivariate.wmvfts">
|
||||
<span id="pyfts-models-multivariate-wmvfts-module"></span><h2>pyFTS.models.multivariate.wmvfts module<a class="headerlink" href="#module-pyFTS.models.multivariate.wmvfts" title="Permalink to this headline">¶</a></h2>
|
||||
|
||||
35
docs/build/html/pyFTS.models.seasonal.html
vendored
35
docs/build/html/pyFTS.models.seasonal.html
vendored
@ -359,7 +359,7 @@
|
||||
<dt id="pyFTS.models.seasonal.common.DateTime">
|
||||
<em class="property">class </em><code class="descclassname">pyFTS.models.seasonal.common.</code><code class="descname">DateTime</code><a class="reference internal" href="_modules/pyFTS/models/seasonal/common.html#DateTime"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.seasonal.common.DateTime" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/enum.html#enum.Enum" title="(in Python v3.7)"><code class="xref py py-class docutils literal notranslate"><span class="pre">enum.Enum</span></code></a></p>
|
||||
<p>An enumeration.</p>
|
||||
<p>Data and Time granularity for time granularity and seasonality identification</p>
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.seasonal.common.DateTime.day_of_month">
|
||||
<code class="descname">day_of_month</code><em class="property"> = 30</em><a class="headerlink" href="#pyFTS.models.seasonal.common.DateTime.day_of_month" title="Permalink to this definition">¶</a></dt>
|
||||
@ -376,8 +376,8 @@
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.seasonal.common.DateTime.hour">
|
||||
<code class="descname">hour</code><em class="property"> = 6</em><a class="headerlink" href="#pyFTS.models.seasonal.common.DateTime.hour" title="Permalink to this definition">¶</a></dt>
|
||||
<dt id="pyFTS.models.seasonal.common.DateTime.half">
|
||||
<code class="descname">half</code><em class="property"> = 2</em><a class="headerlink" href="#pyFTS.models.seasonal.common.DateTime.half" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
@ -430,6 +430,11 @@
|
||||
<code class="descname">month</code><em class="property"> = 12</em><a class="headerlink" href="#pyFTS.models.seasonal.common.DateTime.month" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.seasonal.common.DateTime.quarter">
|
||||
<code class="descname">quarter</code><em class="property"> = 4</em><a class="headerlink" href="#pyFTS.models.seasonal.common.DateTime.quarter" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.seasonal.common.DateTime.second">
|
||||
<code class="descname">second</code><em class="property"> = 8</em><a class="headerlink" href="#pyFTS.models.seasonal.common.DateTime.second" title="Permalink to this definition">¶</a></dt>
|
||||
@ -450,6 +455,16 @@
|
||||
<code class="descname">second_of_minute</code><em class="property"> = 60.00001</em><a class="headerlink" href="#pyFTS.models.seasonal.common.DateTime.second_of_minute" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.seasonal.common.DateTime.sixth">
|
||||
<code class="descname">sixth</code><em class="property"> = 6</em><a class="headerlink" href="#pyFTS.models.seasonal.common.DateTime.sixth" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.seasonal.common.DateTime.third">
|
||||
<code class="descname">third</code><em class="property"> = 3</em><a class="headerlink" href="#pyFTS.models.seasonal.common.DateTime.third" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.seasonal.common.DateTime.year">
|
||||
<code class="descname">year</code><em class="property"> = 1</em><a class="headerlink" href="#pyFTS.models.seasonal.common.DateTime.year" title="Permalink to this definition">¶</a></dt>
|
||||
@ -482,7 +497,7 @@
|
||||
|
||||
<dl class="function">
|
||||
<dt id="pyFTS.models.seasonal.common.strip_datepart">
|
||||
<code class="descclassname">pyFTS.models.seasonal.common.</code><code class="descname">strip_datepart</code><span class="sig-paren">(</span><em>date</em>, <em>date_part</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/seasonal/common.html#strip_datepart"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.seasonal.common.strip_datepart" title="Permalink to this definition">¶</a></dt>
|
||||
<code class="descclassname">pyFTS.models.seasonal.common.</code><code class="descname">strip_datepart</code><span class="sig-paren">(</span><em>date</em>, <em>date_part</em>, <em>mask=''</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/seasonal/common.html#strip_datepart"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.seasonal.common.strip_datepart" title="Permalink to this definition">¶</a></dt>
|
||||
<dd></dd></dl>
|
||||
|
||||
</div>
|
||||
@ -585,6 +600,12 @@
|
||||
</table>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.seasonal.partitioner.TimeGridPartitioner.mask">
|
||||
<code class="descname">mask</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.seasonal.partitioner.TimeGridPartitioner.mask" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>A string with datetime formating mask</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="method">
|
||||
<dt id="pyFTS.models.seasonal.partitioner.TimeGridPartitioner.plot">
|
||||
<code class="descname">plot</code><span class="sig-paren">(</span><em>ax</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/models/seasonal/partitioner.html#TimeGridPartitioner.plot"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.models.seasonal.partitioner.TimeGridPartitioner.plot" title="Permalink to this definition">¶</a></dt>
|
||||
@ -593,6 +614,12 @@
|
||||
:return:</p>
|
||||
</dd></dl>
|
||||
|
||||
<dl class="attribute">
|
||||
<dt id="pyFTS.models.seasonal.partitioner.TimeGridPartitioner.season">
|
||||
<code class="descname">season</code><em class="property"> = None</em><a class="headerlink" href="#pyFTS.models.seasonal.partitioner.TimeGridPartitioner.season" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><p>Seasonality, a pyFTS.models.seasonal.common.DateTime object</p>
|
||||
</dd></dl>
|
||||
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
|
||||
2
docs/build/html/searchindex.js
vendored
2
docs/build/html/searchindex.js
vendored
File diff suppressed because one or more lines are too long
@ -2,6 +2,6 @@ pyFTS
|
||||
=====
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 4
|
||||
:maxdepth: 5
|
||||
|
||||
pyFTS
|
||||
|
||||
@ -29,6 +29,14 @@ pyFTS.data.common module
|
||||
Datasets
|
||||
--------
|
||||
|
||||
Artificial and synthetic data generators
|
||||
----------------------------------------
|
||||
|
||||
.. automodule:: pyFTS.data.artificial
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
AirPassengers dataset
|
||||
-------------------------------
|
||||
|
||||
@ -143,14 +151,6 @@ TAIEX dataset
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
pyFTS.data.artificial module
|
||||
----------------------------
|
||||
|
||||
.. automodule:: pyFTS.data.artificial
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Henon chaotic time series
|
||||
-------------------------
|
||||
|
||||
|
||||
32
docs/pyFTS.distributed.rst
Normal file
32
docs/pyFTS.distributed.rst
Normal file
@ -0,0 +1,32 @@
|
||||
pyFTS.distributed package
|
||||
=========================
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
|
||||
.. automodule:: pyFTS.distributed
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
Submodules
|
||||
----------
|
||||
|
||||
pyFTS.distributed.dispy module
|
||||
------------------------------
|
||||
|
||||
.. automodule:: pyFTS.distributed.dispy
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
pyFTS.distributed.spark module
|
||||
----------------------------------
|
||||
|
||||
.. automodule:: pyFTS.distributed.spark
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
|
||||
|
||||
@ -28,4 +28,12 @@ pyFTS.hyperparam.GridSearch module
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
pyFTS.hyperparam.Evolutionary module
|
||||
------------------------------------
|
||||
|
||||
.. automodule:: pyFTS.hyperparam.Evolutionary
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
pyFTS.models.incremental package
|
||||
=============================
|
||||
================================
|
||||
|
||||
Module contents
|
||||
---------------
|
||||
@ -13,10 +13,19 @@ Module contents
|
||||
Submodules
|
||||
----------
|
||||
|
||||
pyFTS.models.incremental.Retrainer module
|
||||
-------------------------------------
|
||||
pyFTS.models.incremental.TimeVariant module
|
||||
-------------------------------------------
|
||||
|
||||
.. automodule:: pyFTS.models.incremental.Retrainer
|
||||
.. automodule:: pyFTS.models.incremental.TimeVariant
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
|
||||
pyFTS.models.incremental.IncrementalEnsemble module
|
||||
---------------------------------------------------
|
||||
|
||||
.. automodule:: pyFTS.models.incremental.IncrementalEnsemble
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
@ -9,6 +9,7 @@ Subpackages
|
||||
pyFTS.benchmarks
|
||||
pyFTS.common
|
||||
pyFTS.data
|
||||
pyFTS.distributed
|
||||
pyFTS.hyperparam
|
||||
pyFTS.models
|
||||
pyFTS.partitioners
|
||||
|
||||
@ -5,6 +5,148 @@ Facilities to generate synthetic stochastic processes
|
||||
import numpy as np
|
||||
|
||||
|
||||
class SignalEmulator(object):
|
||||
"""
|
||||
Emulate a complex signal built from several additive and non-additive components
|
||||
"""
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super(SignalEmulator, self).__init__()
|
||||
|
||||
self.components = []
|
||||
"""Components of the signal"""
|
||||
|
||||
def stationary_gaussian(self, mu, sigma, **kwargs):
|
||||
"""
|
||||
Creates a continuous Gaussian signal with mean mu and variance sigma.
|
||||
|
||||
:param mu: mean
|
||||
:param sigma: variance
|
||||
:keyword additive: If False it cancels the previous signal and start this one, if True
|
||||
this signal is added to the previous one
|
||||
:keyword start: lag index to start this signal, the default value is 0
|
||||
:keyword it: Number of iterations, the default value is 1
|
||||
:keyword length: Number of samples generated on each iteration, the default value is 100
|
||||
:keyword vmin: Lower bound value of generated data, the default value is None
|
||||
:keyword vmax: Upper bound value of generated data, the default value is None
|
||||
:return: the current SignalEmulator instance, for method chaining
|
||||
"""
|
||||
parameters = {'mu': mu, 'sigma': sigma}
|
||||
self.components.append({'dist': 'gaussian', 'type': 'constant',
|
||||
'parameters': parameters, 'args': kwargs})
|
||||
return self
|
||||
|
||||
def incremental_gaussian(self, mu, sigma, **kwargs):
|
||||
"""
|
||||
Creates an additive gaussian interference on a previous signal
|
||||
|
||||
:param mu: increment on mean
|
||||
:param sigma: increment on variance
|
||||
:keyword start: lag index to start this signal, the default value is 0
|
||||
:keyword it: Number of iterations, the default value is 1
|
||||
:keyword length: Number of samples generated on each iteration, the default value is 100
|
||||
:keyword vmin: Lower bound value of generated data, the default value is None
|
||||
:keyword vmax: Upper bound value of generated data, the default value is None
|
||||
:return: the current SignalEmulator instance, for method chaining
|
||||
"""
|
||||
parameters = {'mu': mu, 'sigma': sigma}
|
||||
self.components.append({'dist': 'gaussian', 'type': 'incremental',
|
||||
'parameters': parameters, 'args': kwargs})
|
||||
return self
|
||||
|
||||
def periodic_gaussian(self, type, period, mu_min, sigma_min, mu_max, sigma_max, **kwargs):
|
||||
"""
|
||||
Creates an additive periodic gaussian interference on a previous signal
|
||||
|
||||
:param type: 'linear' or 'sinoidal'
|
||||
:param period: the period of recurrence
|
||||
:param mu: increment on mean
|
||||
:param sigma: increment on variance
|
||||
:keyword start: lag index to start this signal, the default value is 0
|
||||
:keyword it: Number of iterations, the default value is 1
|
||||
:keyword length: Number of samples generated on each iteration, the default value is 100
|
||||
:keyword vmin: Lower bound value of generated data, the default value is None
|
||||
:keyword vmax: Upper bound value of generated data, the default value is None
|
||||
:return: the current SignalEmulator instance, for method chaining
|
||||
"""
|
||||
parameters = {'type':type, 'period':period,
|
||||
'mu_min': mu_min, 'sigma_min': sigma_min, 'mu_max': mu_max, 'sigma_max': sigma_max}
|
||||
self.components.append({'dist': 'gaussian', 'type': 'periodic',
|
||||
'parameters': parameters, 'args': kwargs})
|
||||
return self
|
||||
|
||||
def blip(self, **kwargs):
|
||||
"""
|
||||
Creates an outlier greater than the maximum or lower then the minimum previous values of the signal,
|
||||
and insert it on a random location of the signal.
|
||||
|
||||
:return: the current SignalEmulator instance, for method chaining
|
||||
"""
|
||||
parameters = {}
|
||||
self.components.append({'dist': 'blip', 'type': 'blip',
|
||||
'parameters': parameters, 'args':kwargs})
|
||||
return self
|
||||
|
||||
def run(self):
|
||||
"""
|
||||
Render the signal
|
||||
|
||||
:return: a list of float values
|
||||
"""
|
||||
signal = []
|
||||
last_it = 10
|
||||
last_num = 10
|
||||
for ct, component in enumerate(self.components):
|
||||
parameters = component['parameters']
|
||||
kwargs = component['args']
|
||||
additive = kwargs.get('additive', True)
|
||||
start = kwargs.get('start', 0)
|
||||
it = kwargs.get('it', last_it)
|
||||
num = kwargs.get('length', last_num)
|
||||
vmin = kwargs.get('vmin',None)
|
||||
vmax = kwargs.get('vmax', None)
|
||||
if component['type'] == 'constant':
|
||||
tmp = generate_gaussian_linear(parameters['mu'], parameters['sigma'], 0, 0,
|
||||
it=it, num=num, vmin=vmin, vmax=vmax)
|
||||
elif component['type'] == 'incremental':
|
||||
tmp = generate_gaussian_linear(0, 0, parameters['mu'], parameters['sigma'],
|
||||
it=num, num=1, vmin=vmin, vmax=vmax)
|
||||
elif component['type'] == 'periodic':
|
||||
period = parameters['period']
|
||||
mu_min, sigma_min = parameters['mu_min'],parameters['sigma_min']
|
||||
mu_max, sigma_max = parameters['mu_max'],parameters['sigma_max']
|
||||
|
||||
if parameters['type'] == 'sinoidal':
|
||||
tmp = generate_sinoidal_periodic_gaussian(period, mu_min, sigma_min, mu_max, sigma_max,
|
||||
it=num, num=1, vmin=vmin, vmax=vmax)
|
||||
else:
|
||||
tmp = generate_linear_periodic_gaussian(period, mu_min, sigma_min, mu_max, sigma_max,
|
||||
it=num, num=1, vmin=vmin, vmax=vmax)
|
||||
elif component['type'] == 'blip':
|
||||
_mx = np.nanmax(signal)
|
||||
_mn = np.nanmin(signal)
|
||||
|
||||
_mx += 2*_mx if _mx > 0 else -2*_mx
|
||||
_mn += -2*_mn if _mn > 0 else 2*_mn
|
||||
|
||||
if vmax is not None:
|
||||
_mx = min(_mx, vmax) if vmax > 0 else max(_mx, vmax)
|
||||
if vmin is not None:
|
||||
_mn = max(_mn, vmin) if vmin > 0 else min(_mn, vmin)
|
||||
|
||||
start = np.random.randint(0, len(signal))
|
||||
tmp = [_mx] if np.random.rand() >= .5 else [-_mn]
|
||||
|
||||
last_num = num
|
||||
last_it = it
|
||||
|
||||
signal = _append(additive, start, signal, tmp)
|
||||
|
||||
return signal
|
||||
|
||||
|
||||
|
||||
|
||||
def generate_gaussian_linear(mu_ini, sigma_ini, mu_inc, sigma_inc, it=100, num=10, vmin=None, vmax=None):
|
||||
"""
|
||||
Generate data sampled from Gaussian distribution, with constant or linear changing parameters
|
||||
@ -157,6 +299,7 @@ def white_noise(n=500):
|
||||
def random_walk(n=500, type='gaussian'):
|
||||
"""
|
||||
Simple random walk
|
||||
|
||||
:param n: number of samples
|
||||
:param type: 'gaussian' or 'uniform'
|
||||
:return:
|
||||
@ -195,139 +338,3 @@ def _append(additive, start, before, new):
|
||||
return tmp.tolist()
|
||||
|
||||
|
||||
|
||||
class SignalEmulator(object):
|
||||
"""
|
||||
Emulate a complex signal built from several additive and non-additive components
|
||||
"""
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super(SignalEmulator, self).__init__()
|
||||
|
||||
self.components = []
|
||||
|
||||
def stationary_gaussian(self, mu, sigma, **kwargs):
|
||||
"""
|
||||
Creates a continuous Gaussian signal with mean mu and variance sigma.
|
||||
:param mu: mean
|
||||
:param sigma: variance
|
||||
:keyword additive: If False it cancels the previous signal and start this one, if True
|
||||
this signal is added to the previous one
|
||||
:keyword start: lag index to start this signal, the default value is 0
|
||||
:keyword it: Number of iterations, the default value is 1
|
||||
:keyword length: Number of samples generated on each iteration, the default value is 100
|
||||
:keyword vmin: Lower bound value of generated data, the default value is None
|
||||
:keyword vmax: Upper bound value of generated data, the default value is None
|
||||
:return: the current SignalEmulator instance, for method chaining
|
||||
"""
|
||||
parameters = {'mu': mu, 'sigma': sigma}
|
||||
self.components.append({'dist': 'gaussian', 'type': 'constant',
|
||||
'parameters': parameters, 'args': kwargs})
|
||||
return self
|
||||
|
||||
def incremental_gaussian(self, mu, sigma, **kwargs):
|
||||
"""
|
||||
Creates an additive gaussian interference on a previous signal
|
||||
:param mu: increment on mean
|
||||
:param sigma: increment on variance
|
||||
:keyword start: lag index to start this signal, the default value is 0
|
||||
:keyword it: Number of iterations, the default value is 1
|
||||
:keyword length: Number of samples generated on each iteration, the default value is 100
|
||||
:keyword vmin: Lower bound value of generated data, the default value is None
|
||||
:keyword vmax: Upper bound value of generated data, the default value is None
|
||||
:return: the current SignalEmulator instance, for method chaining
|
||||
"""
|
||||
parameters = {'mu': mu, 'sigma': sigma}
|
||||
self.components.append({'dist': 'gaussian', 'type': 'incremental',
|
||||
'parameters': parameters, 'args': kwargs})
|
||||
return self
|
||||
|
||||
def periodic_gaussian(self, type, period, mu_min, sigma_min, mu_max, sigma_max, **kwargs):
|
||||
"""
|
||||
Creates an additive periodic gaussian interference on a previous signal
|
||||
:param type: 'linear' or 'sinoidal'
|
||||
:param period: the period of recurrence
|
||||
:param mu: increment on mean
|
||||
:param sigma: increment on variance
|
||||
:keyword start: lag index to start this signal, the default value is 0
|
||||
:keyword it: Number of iterations, the default value is 1
|
||||
:keyword length: Number of samples generated on each iteration, the default value is 100
|
||||
:keyword vmin: Lower bound value of generated data, the default value is None
|
||||
:keyword vmax: Upper bound value of generated data, the default value is None
|
||||
:return: the current SignalEmulator instance, for method chaining
|
||||
"""
|
||||
parameters = {'type':type, 'period':period,
|
||||
'mu_min': mu_min, 'sigma_min': sigma_min, 'mu_max': mu_max, 'sigma_max': sigma_max}
|
||||
self.components.append({'dist': 'gaussian', 'type': 'periodic',
|
||||
'parameters': parameters, 'args': kwargs})
|
||||
return self
|
||||
|
||||
def blip(self, **kwargs):
|
||||
"""
|
||||
Creates an outlier greater than the maximum or lower then the minimum previous values of the signal,
|
||||
and insert it on a random location of the signal.
|
||||
|
||||
:return: the current SignalEmulator instance, for method chaining
|
||||
"""
|
||||
parameters = {}
|
||||
self.components.append({'dist': 'blip', 'type': 'blip',
|
||||
'parameters': parameters, 'args':kwargs})
|
||||
return self
|
||||
|
||||
def run(self):
|
||||
"""
|
||||
Render the signal
|
||||
|
||||
:return: a list of float values
|
||||
"""
|
||||
signal = []
|
||||
last_it = 10
|
||||
last_num = 10
|
||||
for ct, component in enumerate(self.components):
|
||||
parameters = component['parameters']
|
||||
kwargs = component['args']
|
||||
additive = kwargs.get('additive', True)
|
||||
start = kwargs.get('start', 0)
|
||||
it = kwargs.get('it', last_it)
|
||||
num = kwargs.get('length', last_num)
|
||||
vmin = kwargs.get('vmin',None)
|
||||
vmax = kwargs.get('vmax', None)
|
||||
if component['type'] == 'constant':
|
||||
tmp = generate_gaussian_linear(parameters['mu'], parameters['sigma'], 0, 0,
|
||||
it=it, num=num, vmin=vmin, vmax=vmax)
|
||||
elif component['type'] == 'incremental':
|
||||
tmp = generate_gaussian_linear(0, 0, parameters['mu'], parameters['sigma'],
|
||||
it=num, num=1, vmin=vmin, vmax=vmax)
|
||||
elif component['type'] == 'periodic':
|
||||
period = parameters['period']
|
||||
mu_min, sigma_min = parameters['mu_min'],parameters['sigma_min']
|
||||
mu_max, sigma_max = parameters['mu_max'],parameters['sigma_max']
|
||||
|
||||
if parameters['type'] == 'sinoidal':
|
||||
tmp = generate_sinoidal_periodic_gaussian(period, mu_min, sigma_min, mu_max, sigma_max,
|
||||
it=num, num=1, vmin=vmin, vmax=vmax)
|
||||
else:
|
||||
tmp = generate_linear_periodic_gaussian(period, mu_min, sigma_min, mu_max, sigma_max,
|
||||
it=num, num=1, vmin=vmin, vmax=vmax)
|
||||
elif component['type'] == 'blip':
|
||||
_mx = np.nanmax(signal)
|
||||
_mn = np.nanmin(signal)
|
||||
|
||||
_mx += 2*_mx if _mx > 0 else -2*_mx
|
||||
_mn += -2*_mn if _mn > 0 else 2*_mn
|
||||
|
||||
if vmax is not None:
|
||||
_mx = min(_mx, vmax) if vmax > 0 else max(_mx, vmax)
|
||||
if vmin is not None:
|
||||
_mn = max(_mn, vmin) if vmin > 0 else min(_mn, vmin)
|
||||
|
||||
start = np.random.randint(0, len(signal))
|
||||
tmp = [_mx] if np.random.rand() >= .5 else [-_mn]
|
||||
|
||||
last_num = num
|
||||
last_it = it
|
||||
|
||||
signal = _append(additive, start, signal, tmp)
|
||||
|
||||
return signal
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user