Removing dispy imports on hyperparam

docs/build/html/genindex.html

@@ -109,8 +109,6 @@
       <li><a href="pyFTS.common.html#pyFTS.common.Transformations.AdaptiveExpectation">AdaptiveExpectation (class in pyFTS.common.Transformations)</a>
 </li>
       <li><a href="pyFTS.models.html#pyFTS.models.pwfts.ProbabilisticWeightedFTS.add_new_PWFLGR">add_new_PWFLGR() (pyFTS.models.pwfts.ProbabilisticWeightedFTS method)</a>
-</li>
-      <li><a href="pyFTS.common.html#pyFTS.common.Transformations.aggregate">aggregate() (in module pyFTS.common.Transformations)</a>
 </li>
       <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS">AllMethodEnsembleFTS (class in pyFTS.models.ensemble.ensemble)</a>
 </li>
@@ -202,6 +200,10 @@
         <li><a href="pyFTS.common.html#pyFTS.common.Transformations.BoxCox.apply">(pyFTS.common.Transformations.BoxCox method)</a>
 </li>
         <li><a href="pyFTS.common.html#pyFTS.common.Transformations.Differential.apply">(pyFTS.common.Transformations.Differential method)</a>
+</li>
+        <li><a href="pyFTS.common.html#pyFTS.common.Transformations.LinearTrend.apply">(pyFTS.common.Transformations.LinearTrend method)</a>
+</li>
+        <li><a href="pyFTS.common.html#pyFTS.common.Transformations.ROI.apply">(pyFTS.common.Transformations.ROI method)</a>
 </li>
         <li><a href="pyFTS.common.html#pyFTS.common.Transformations.Scale.apply">(pyFTS.common.Transformations.Scale method)</a>
 </li>
@@ -453,6 +455,8 @@
       <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.benchmarks.html#pyFTS.benchmarks.benchmarks.distributed_model_train_test_time">distributed_model_train_test_time() (in module pyFTS.benchmarks.benchmarks)</a>
 </li>
       <li><a href="pyFTS.distributed.html#pyFTS.distributed.spark.distributed_predict">distributed_predict() (in module pyFTS.distributed.spark)</a>
 </li>
@@ -855,6 +859,8 @@
       <li><a href="pyFTS.common.html#pyFTS.common.FLR.generate_high_order_recurrent_flr">generate_high_order_recurrent_flr() (in module pyFTS.common.FLR)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.FLR.generate_indexed_flrs">generate_indexed_flrs() (in module pyFTS.common.FLR)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.Transformations.LinearTrend.generate_indexes">generate_indexes() (pyFTS.common.Transformations.LinearTrend method)</a>
 </li>
       <li><a href="pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.generate_lhs_flrg">generate_lhs_flrg() (pyFTS.models.hofts.HighOrderFTS method)</a>
 
@@ -991,11 +997,11 @@
       <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.get_distribution_ahead_statistics">get_distribution_ahead_statistics() (in module pyFTS.benchmarks.Measures)</a>
 </li>
       <li><a href="pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_distribution_interquantile">get_distribution_interquantile() (pyFTS.models.ensemble.ensemble.EnsembleFTS method)</a>
-</li>
-      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.get_distribution_statistics">get_distribution_statistics() (in module pyFTS.benchmarks.Measures)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.get_distribution_statistics">get_distribution_statistics() (in module pyFTS.benchmarks.Measures)</a>
+</li>
       <li><a href="pyFTS.common.html#pyFTS.common.FuzzySet.get_fuzzysets">get_fuzzysets() (in module pyFTS.common.FuzzySet)</a>
 </li>
       <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.SeasonalIndexer.DateTimeSeasonalIndexer.get_index">get_index() (pyFTS.models.seasonal.SeasonalIndexer.DateTimeSeasonalIndexer method)</a>
@@ -1241,6 +1247,8 @@
       <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.common.html#pyFTS.common.Transformations.LinearTrend.increment">increment() (pyFTS.common.Transformations.LinearTrend method)</a>
 </li>
       <li><a href="pyFTS.data.html#pyFTS.data.artificial.SignalEmulator.incremental_gaussian">incremental_gaussian() (pyFTS.data.artificial.SignalEmulator method)</a>
 </li>
@@ -1262,10 +1270,10 @@
 </li>
       <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Util.insert_benchmark">insert_benchmark() (in module pyFTS.benchmarks.Util)</a>
 </li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="pyFTS.hyperparam.html#pyFTS.hyperparam.Util.insert_hyperparam">insert_hyperparam() (in module pyFTS.hyperparam.Util)</a>
 </li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="pyFTS.common.html#pyFTS.common.SortedCollection.SortedCollection.insert_right">insert_right() (pyFTS.common.SortedCollection.SortedCollection method)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.SortedCollection.SortedCollection.inside">inside() (pyFTS.common.SortedCollection.SortedCollection method)</a>
@@ -1288,6 +1296,10 @@
         <li><a href="pyFTS.common.html#pyFTS.common.Transformations.BoxCox.inverse">(pyFTS.common.Transformations.BoxCox method)</a>
 </li>
         <li><a href="pyFTS.common.html#pyFTS.common.Transformations.Differential.inverse">(pyFTS.common.Transformations.Differential method)</a>
+</li>
+        <li><a href="pyFTS.common.html#pyFTS.common.Transformations.LinearTrend.inverse">(pyFTS.common.Transformations.LinearTrend method)</a>
+</li>
+        <li><a href="pyFTS.common.html#pyFTS.common.Transformations.ROI.inverse">(pyFTS.common.Transformations.ROI method)</a>
 </li>
         <li><a href="pyFTS.common.html#pyFTS.common.Transformations.Scale.inverse">(pyFTS.common.Transformations.Scale method)</a>
 </li>
@@ -1329,10 +1341,12 @@
       <li><a href="pyFTS.models.nonstationary.html#pyFTS.models.nonstationary.perturbation.linear">linear() (in module pyFTS.models.nonstationary.perturbation)</a>
 </li>
       <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.linearmodel">linearmodel() (pyFTS.benchmarks.quantreg.QuantileRegression method)</a>
+</li>
+      <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.SeasonalIndexer.LinearSeasonalIndexer">LinearSeasonalIndexer (class in pyFTS.models.seasonal.SeasonalIndexer)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.SeasonalIndexer.LinearSeasonalIndexer">LinearSeasonalIndexer (class in pyFTS.models.seasonal.SeasonalIndexer)</a>
+      <li><a href="pyFTS.common.html#pyFTS.common.Transformations.LinearTrend">LinearTrend (class in pyFTS.common.Transformations)</a>
 </li>
       <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.ResidualAnalysis.ljung_box_test">ljung_box_test() (in module pyFTS.benchmarks.ResidualAnalysis)</a>
 </li>
@@ -1878,7 +1892,7 @@
 </li>
       <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><a href="pyFTS.common.html#pyFTS.common.Transformations.ROI">ROI (class in pyFTS.common.Transformations)</a>
 </li>
       <li><a href="pyFTS.data.html#pyFTS.data.artificial.SignalEmulator.run">run() (pyFTS.data.artificial.SignalEmulator method)</a>
 </li>
@@ -2011,8 +2025,6 @@
       <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.benchmarks.sliding_window_benchmarks2">sliding_window_benchmarks2() (in module pyFTS.benchmarks.benchmarks)</a>
 </li>
       <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.smape">smape() (in module pyFTS.benchmarks.Measures)</a>
-</li>
-      <li><a href="pyFTS.common.html#pyFTS.common.Transformations.smoothing">smoothing() (in module pyFTS.common.Transformations)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.SortedCollection.SortedCollection">SortedCollection (class in pyFTS.common.SortedCollection)</a>
 </li>
@@ -2056,6 +2068,8 @@
         <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.train">(pyFTS.benchmarks.knn.KNearestNeighbors method)</a>
 </li>
         <li><a href="pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.train">(pyFTS.benchmarks.quantreg.QuantileRegression method)</a>
+</li>
+        <li><a href="pyFTS.common.html#pyFTS.common.Transformations.LinearTrend.train">(pyFTS.common.Transformations.LinearTrend method)</a>
 </li>
         <li><a href="pyFTS.common.html#pyFTS.common.fts.FTS.train">(pyFTS.common.fts.FTS method)</a>
 </li>
@@ -2125,6 +2139,8 @@
       <li><a href="pyFTS.common.html#pyFTS.common.Transformations.Transformation">Transformation (class in pyFTS.common.Transformations)</a>
 </li>
       <li><a href="pyFTS.common.html#pyFTS.common.Membership.trapmf">trapmf() (in module pyFTS.common.Membership)</a>
+</li>
+      <li><a href="pyFTS.common.html#pyFTS.common.Transformations.LinearTrend.trend">trend() (pyFTS.common.Transformations.LinearTrend method)</a>
 </li>
       <li><a href="pyFTS.models.html#pyFTS.models.cheng.TrendWeightedFLRG">TrendWeightedFLRG (class in pyFTS.models.cheng)</a>
 </li>

docs/build/html/pyFTS.benchmarks.html

@@ -160,6 +160,30 @@
 <code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">compareModelsTable</code><span class="sig-paren">(</span><em>original</em>, <em>models_fo</em>, <em>models_ho</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.compareModelsTable" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
+<dl class="function">
+<dt id="pyFTS.benchmarks.benchmarks.distributed_model_train_test_time">
+<code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">distributed_model_train_test_time</code><span class="sig-paren">(</span><em>models</em>, <em>data</em>, <em>windowsize</em>, <em>train=0.8</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.distributed_model_train_test_time" title="Permalink to this definition">¶</a></dt>
+<dd><p>Assess the train and test times for a given list of configured models and save the results on a database.</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>models</strong> – A list of FTS models already configured, but not yet trained,</li>
+<li><strong>data</strong> – time series data, including train and test data</li>
+<li><strong>windowsize</strong> – Train/test data windows</li>
+<li><strong>train</strong> – Percent of data window that will be used to train the models</li>
+<li><strong>kwargs</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.benchmarks.benchmarks.get_benchmark_interval_methods">
 <code class="descclassname">pyFTS.benchmarks.benchmarks.</code><code class="descname">get_benchmark_interval_methods</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.benchmarks.benchmarks.get_benchmark_interval_methods" title="Permalink to this definition">¶</a></dt>

docs/build/html/pyFTS.common.html

@@ -885,6 +885,8 @@ bisect but with a simpler API and support for key functions.</p>
 <em class="property">class </em><code class="descclassname">pyFTS.common.Transformations.</code><code class="descname">BoxCox</code><span class="sig-paren">(</span><em>plambda</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.common.Transformations.BoxCox" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#pyFTS.common.Transformations.Transformation" title="pyFTS.common.Transformations.Transformation"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.Transformations.Transformation</span></code></a></p>
 <p>Box-Cox power transformation</p>
+<p>y’(t) = log( y(t) )
+y(t) = exp( y’(t) )</p>
 <dl class="method">
 <dt id="pyFTS.common.Transformations.BoxCox.apply">
 <code class="descname">apply</code><span class="sig-paren">(</span><em>data</em>, <em>param=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.common.Transformations.BoxCox.apply" title="Permalink to this definition">¶</a></dt>
@@ -940,6 +942,8 @@ bisect but with a simpler API and support for key functions.</p>
 <em class="property">class </em><code class="descclassname">pyFTS.common.Transformations.</code><code class="descname">Differential</code><span class="sig-paren">(</span><em>lag</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.common.Transformations.Differential" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#pyFTS.common.Transformations.Transformation" title="pyFTS.common.Transformations.Transformation"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.Transformations.Transformation</span></code></a></p>
 <p>Differentiation data transform</p>
+<p>y’(t) = y(t) - y(t-1)
+y(t) =  y(t-1)  + y’(t)</p>
 <dl class="method">
 <dt id="pyFTS.common.Transformations.Differential.apply">
 <code class="descname">apply</code><span class="sig-paren">(</span><em>data</em>, <em>param=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.common.Transformations.Differential.apply" title="Permalink to this definition">¶</a></dt>
@@ -990,6 +994,131 @@ bisect but with a simpler API and support for key functions.</p>
 
 </dd></dl>
 
+<dl class="class">
+<dt id="pyFTS.common.Transformations.LinearTrend">
+<em class="property">class </em><code class="descclassname">pyFTS.common.Transformations.</code><code class="descname">LinearTrend</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.common.Transformations.LinearTrend" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyFTS.common.Transformations.Transformation" title="pyFTS.common.Transformations.Transformation"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.Transformations.Transformation</span></code></a></p>
+<p>Linear Trend. Estimate</p>
+<p>y’(t) = y(t) - (a*t+b)
+y(t) =  y’(t) + (a*t+b)</p>
+<dl class="method">
+<dt id="pyFTS.common.Transformations.LinearTrend.apply">
+<code class="descname">apply</code><span class="sig-paren">(</span><em>data</em>, <em>param=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.common.Transformations.LinearTrend.apply" title="Permalink to this definition">¶</a></dt>
+<dd><p>Apply the transformation on input data</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> – input data</li>
+<li><strong>param</strong> – </li>
+<li><strong>kwargs</strong> – </li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">numpy array with transformed data</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="method">
+<dt id="pyFTS.common.Transformations.LinearTrend.generate_indexes">
+<code class="descname">generate_indexes</code><span class="sig-paren">(</span><em>data</em>, <em>value</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.common.Transformations.LinearTrend.generate_indexes" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="method">
+<dt id="pyFTS.common.Transformations.LinearTrend.increment">
+<code class="descname">increment</code><span class="sig-paren">(</span><em>value</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.common.Transformations.LinearTrend.increment" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="method">
+<dt id="pyFTS.common.Transformations.LinearTrend.inverse">
+<code class="descname">inverse</code><span class="sig-paren">(</span><em>data</em>, <em>param=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.common.Transformations.LinearTrend.inverse" 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>data</strong> – transformed data</li>
+<li><strong>param</strong> – </li>
+<li><strong>kwargs</strong> – </li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">numpy array with inverse transformed data</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="method">
+<dt id="pyFTS.common.Transformations.LinearTrend.train">
+<code class="descname">train</code><span class="sig-paren">(</span><em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.common.Transformations.LinearTrend.train" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="method">
+<dt id="pyFTS.common.Transformations.LinearTrend.trend">
+<code class="descname">trend</code><span class="sig-paren">(</span><em>data</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.common.Transformations.LinearTrend.trend" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="class">
+<dt id="pyFTS.common.Transformations.ROI">
+<em class="property">class </em><code class="descclassname">pyFTS.common.Transformations.</code><code class="descname">ROI</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.common.Transformations.ROI" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#pyFTS.common.Transformations.Transformation" title="pyFTS.common.Transformations.Transformation"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.common.Transformations.Transformation</span></code></a></p>
+<p>Return of Investment (ROI) transformation. Retrieved from Sadaei and Lee (2014) - Multilayer Stock
+Forecasting Model Using Fuzzy Time Series</p>
+<p>y’(t) = ( y(t) - y(t-1) ) / y(t-1)
+y(t) = ( y(t-1) * y’(t) ) + y(t-1)</p>
+<dl class="method">
+<dt id="pyFTS.common.Transformations.ROI.apply">
+<code class="descname">apply</code><span class="sig-paren">(</span><em>data</em>, <em>param=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.common.Transformations.ROI.apply" title="Permalink to this definition">¶</a></dt>
+<dd><p>Apply the transformation on input data</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> – input data</li>
+<li><strong>param</strong> – </li>
+<li><strong>kwargs</strong> – </li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">numpy array with transformed data</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+<dl class="method">
+<dt id="pyFTS.common.Transformations.ROI.inverse">
+<code class="descname">inverse</code><span class="sig-paren">(</span><em>data</em>, <em>param=None</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.common.Transformations.ROI.inverse" 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>data</strong> – transformed data</li>
+<li><strong>param</strong> – </li>
+<li><strong>kwargs</strong> – </li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">numpy array with inverse transformed data</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
+
+</dd></dl>
+
 <dl class="class">
 <dt id="pyFTS.common.Transformations.Scale">
 <em class="property">class </em><code class="descclassname">pyFTS.common.Transformations.</code><code class="descname">Scale</code><span class="sig-paren">(</span><em>min=0</em>, <em>max=1</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.common.Transformations.Scale" title="Permalink to this definition">¶</a></dt>
@@ -1100,21 +1229,6 @@ bisect but with a simpler API and support for key functions.</p>
 <code class="descclassname">pyFTS.common.Transformations.</code><code class="descname">Z</code><span class="sig-paren">(</span><em>original</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.common.Transformations.Z" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
-<dl class="function">
-<dt id="pyFTS.common.Transformations.aggregate">
-<code class="descclassname">pyFTS.common.Transformations.</code><code class="descname">aggregate</code><span class="sig-paren">(</span><em>original</em>, <em>operation</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.common.Transformations.aggregate" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="pyFTS.common.Transformations.roi">
-<code class="descclassname">pyFTS.common.Transformations.</code><code class="descname">roi</code><span class="sig-paren">(</span><em>original</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.common.Transformations.roi" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="function">
-<dt id="pyFTS.common.Transformations.smoothing">
-<code class="descclassname">pyFTS.common.Transformations.</code><code class="descname">smoothing</code><span class="sig-paren">(</span><em>original</em>, <em>lags</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.common.Transformations.smoothing" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
 </div>
 <div class="section" id="module-pyFTS.common.Util">
 <span id="pyfts-common-util-module"></span><h2>pyFTS.common.Util module<a class="headerlink" href="#module-pyFTS.common.Util" title="Permalink to this headline">¶</a></h2>

docs/build/html/pyFTS.distributed.html

@@ -120,34 +120,70 @@
 <dl class="function">
 <dt id="pyFTS.distributed.spark.create_multivariate_model">
 <code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">create_multivariate_model</code><span class="sig-paren">(</span><em>**parameters</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.distributed.spark.create_multivariate_model" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
+<dd><p>From the dictionary of parameters, create a multivariate FTS model</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>parameters</strong> – dictionary of parameters</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">multivariate FTS model</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
 
 <dl class="function">
 <dt id="pyFTS.distributed.spark.create_spark_conf">
 <code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">create_spark_conf</code><span class="sig-paren">(</span><em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.distributed.spark.create_spark_conf" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
+<dd><p>Configure the Spark master node</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>kwargs</strong> – </td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"></td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
 
 <dl class="function">
 <dt id="pyFTS.distributed.spark.create_univariate_model">
 <code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">create_univariate_model</code><span class="sig-paren">(</span><em>**parameters</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.distributed.spark.create_univariate_model" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
+<dd><p>From the dictionary of parameters, create an univariate FTS model</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>parameters</strong> – dictionary of parameters</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">univariate FTS model</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
 
 <dl class="function">
 <dt id="pyFTS.distributed.spark.distributed_predict">
 <code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">distributed_predict</code><span class="sig-paren">(</span><em>data</em>, <em>model</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.distributed.spark.distributed_predict" title="Permalink to this definition">¶</a></dt>
-<dd><table class="docutils field-list" frame="void" rules="none">
+<dd><p>The main method for distributed forecasting with FTS models using Spark clusters.</p>
+<p>It takes a trained FTS model and the test data, connect with the Spark cluster,
+proceed the distributed forecasting and return the merged forecasted values.</p>
+<table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>model</strong> – </li>
-<li><strong>data</strong> – </li>
-<li><strong>url</strong> – </li>
+<li><strong>model</strong> – an FTS trained model</li>
+<li><strong>data</strong> – test data</li>
+<li><strong>url</strong> – URL of the Spark master</li>
 <li><strong>app</strong> – </li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"></p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">forecasted values</p>
 </td>
 </tr>
 </tbody>
@@ -157,19 +193,22 @@
 <dl class="function">
 <dt id="pyFTS.distributed.spark.distributed_train">
 <code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">distributed_train</code><span class="sig-paren">(</span><em>model</em>, <em>data</em>, <em>**kwargs</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.distributed.spark.distributed_train" title="Permalink to this definition">¶</a></dt>
-<dd><table class="docutils field-list" frame="void" rules="none">
+<dd><p>The main method for distributed training of FTS models using Spark clusters.</p>
+<p>It takes an empty model and the train data, connect with the Spark cluster, proceed the
+distributed training and return the learned model.</p>
+<table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
 <tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
-<li><strong>model</strong> – </li>
-<li><strong>data</strong> – </li>
-<li><strong>url</strong> – </li>
-<li><strong>app</strong> – </li>
+<li><strong>model</strong> – An empty (non-trained) FTS model</li>
+<li><strong>data</strong> – train data</li>
+<li><strong>url</strong> – URL of the Spark master node</li>
+<li><strong>app</strong> – Application name</li>
 </ul>
 </td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last"></p>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">trained model</p>
 </td>
 </tr>
 </tbody>
@@ -179,18 +218,42 @@
 <dl class="function">
 <dt id="pyFTS.distributed.spark.get_clustered_partitioner">
 <code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">get_clustered_partitioner</code><span class="sig-paren">(</span><em>explanatory_variables</em>, <em>target_variable</em>, <em>**parameters</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.distributed.spark.get_clustered_partitioner" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
+<dd><p>Return the UoD partitioner from the ‘shared_partitioner’ fuzzy sets, special case for
+clustered multivariate FTS.</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>explanatory_variables</strong> – the list with the names of the explanatory variables</li>
+<li><strong>target_variable</strong> – the name of the target variable</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">Partitioner object</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
 
 <dl class="function">
 <dt id="pyFTS.distributed.spark.get_partitioner">
 <code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">get_partitioner</code><span class="sig-paren">(</span><em>shared_partitioner</em>, <em>type='common'</em>, <em>variables=[]</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.distributed.spark.get_partitioner" title="Permalink to this definition">¶</a></dt>
-<dd><table class="docutils field-list" frame="void" rules="none">
+<dd><p>Return the UoD partitioner from the ‘shared_partitioner’ fuzzy sets</p>
+<table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>part</strong> – </td>
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>shared_partitioner</strong> – the shared variable with the fuzzy sets</li>
+<li><strong>type</strong> – the type of the partitioner</li>
+<li><strong>variables</strong> – in case of a Multivariate FTS, the list of variables</li>
+</ul>
+</td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"></td>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">Partitioner object</p>
+</td>
 </tr>
 </tbody>
 </table>
@@ -199,28 +262,78 @@
 <dl class="function">
 <dt id="pyFTS.distributed.spark.get_variables">
 <code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">get_variables</code><span class="sig-paren">(</span><em>**parameters</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.distributed.spark.get_variables" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
+<dd><p>From the dictionary of parameters, return a tuple with the list of explanatory and target variables</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>parameters</strong> – dictionary of parameters</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">a tuple with the list of explanatory and target variables</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
 
 <dl class="function">
 <dt id="pyFTS.distributed.spark.share_parameters">
 <code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">share_parameters</code><span class="sig-paren">(</span><em>model</em>, <em>context</em>, <em>data</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.distributed.spark.share_parameters" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
+<dd><p>Create a shared variable with a dictionary of the model parameters and hyperparameters</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>model</strong> – the FTS model to extract the parameters and hyperparameters</li>
+<li><strong>context</strong> – Spark context</li>
+<li><strong>data</strong> – dataset</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">the shared variable with the dictionary of parameters</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
 
 <dl class="function">
 <dt id="pyFTS.distributed.spark.slave_forecast_multivariate">
 <code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">slave_forecast_multivariate</code><span class="sig-paren">(</span><em>data</em>, <em>**parameters</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.distributed.spark.slave_forecast_multivariate" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
+<dd><p>Receive test data, create a multivariate FTS model from the parameters and return the forecasted values</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>data</strong> – test data</li>
+<li><strong>parameters</strong> – dictionary of parameters</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">forecasted values from the data input</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
 
 <dl class="function">
 <dt id="pyFTS.distributed.spark.slave_forecast_univariate">
 <code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">slave_forecast_univariate</code><span class="sig-paren">(</span><em>data</em>, <em>**parameters</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.distributed.spark.slave_forecast_univariate" title="Permalink to this definition">¶</a></dt>
-<dd><table class="docutils field-list" frame="void" rules="none">
+<dd><p>Receive test data, create an univariate FTS model from the parameters and return the forecasted values</p>
+<table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>data</strong> – </td>
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>data</strong> – test data</li>
+<li><strong>parameters</strong> – dictionary of parameters</li>
+</ul>
+</td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"></td>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">forecasted values from the data input</p>
+</td>
 </tr>
 </tbody>
 </table>
@@ -229,18 +342,40 @@
 <dl class="function">
 <dt id="pyFTS.distributed.spark.slave_train_multivariate">
 <code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">slave_train_multivariate</code><span class="sig-paren">(</span><em>data</em>, <em>**parameters</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.distributed.spark.slave_train_multivariate" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
+<dd><p>Receive train data, train a multivariate FTS model and return the learned rules</p>
+<table class="docutils field-list" frame="void" rules="none">
+<col class="field-name" />
+<col class="field-body" />
+<tbody valign="top">
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>data</strong> – train data</li>
+<li><strong>parameters</strong> – dictionary of parameters</li>
+</ul>
+</td>
+</tr>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">Key/value list of the learned rules</p>
+</td>
+</tr>
+</tbody>
+</table>
+</dd></dl>
 
 <dl class="function">
 <dt id="pyFTS.distributed.spark.slave_train_univariate">
 <code class="descclassname">pyFTS.distributed.spark.</code><code class="descname">slave_train_univariate</code><span class="sig-paren">(</span><em>data</em>, <em>**parameters</em><span class="sig-paren">)</span><a class="headerlink" href="#pyFTS.distributed.spark.slave_train_univariate" title="Permalink to this definition">¶</a></dt>
-<dd><table class="docutils field-list" frame="void" rules="none">
+<dd><p>Receive train data, train an univariate FTS model and return the learned rules</p>
+<table class="docutils field-list" frame="void" rules="none">
 <col class="field-name" />
 <col class="field-body" />
 <tbody valign="top">
-<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>data</strong> – </td>
+<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
+<li><strong>data</strong> – train data</li>
+<li><strong>parameters</strong> – dictionary of parameters</li>
+</ul>
+</td>
 </tr>
-<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"></td>
+<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">Key/value list of the learned rules</p>
+</td>
 </tr>
 </tbody>
 </table>