pyFTS/docs/build/html/pyFTS.hyperparam.html



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  <div class="section" id="pyfts-hyperparam-package">
<h1>pyFTS.hyperparam package<a class="headerlink" href="#pyfts-hyperparam-package" title="Permalink to this headline">¶</a></h1>
<div class="section" id="module-pyFTS.hyperparam">
<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-pyFTS.hyperparam" title="Permalink to this headline">¶</a></h2>
</div>
<div class="section" id="submodules">
<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
</div>
<div class="section" id="module-pyFTS.hyperparam.Util">
<span id="pyfts-hyperparam-util-module"></span><h2>pyFTS.hyperparam.Util module<a class="headerlink" href="#module-pyFTS.hyperparam.Util" title="Permalink to this headline">¶</a></h2>
<p>Common facilities for hyperparameter optimization</p>
<dl class="py function">
<dt id="pyFTS.hyperparam.Util.create_hyperparam_tables">
<code class="sig-prename descclassname">pyFTS.hyperparam.Util.</code><code class="sig-name descname">create_hyperparam_tables</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">conn</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Util.html#create_hyperparam_tables"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Util.create_hyperparam_tables" title="Permalink to this definition">¶</a></dt>
<dd><p>Create a sqlite3 table designed to store benchmark results.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>conn</strong> – a sqlite3 database connection</p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt id="pyFTS.hyperparam.Util.insert_hyperparam">
<code class="sig-prename descclassname">pyFTS.hyperparam.Util.</code><code class="sig-name descname">insert_hyperparam</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span></em>, <em class="sig-param"><span class="n">conn</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Util.html#insert_hyperparam"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Util.insert_hyperparam" title="Permalink to this definition">¶</a></dt>
<dd><p>Insert benchmark data on database</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>data</strong> – a tuple with the benchmark data with format:</p>
</dd>
</dl>
<p>Dataset: Identify on which dataset the dataset was performed
Tag: a user defined word that indentify a benchmark set
Model: FTS model
Transformation: The name of data transformation, if one was used
mf: membership function
Order: the order of the FTS method
Partitioner: UoD partitioning scheme
Partitions: Number of partitions
alpha: alpha cut
lags: lags
Measure: accuracy measure
Value: the measure value</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>conn</strong> – a sqlite3 database connection</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt id="pyFTS.hyperparam.Util.open_hyperparam_db">
<code class="sig-prename descclassname">pyFTS.hyperparam.Util.</code><code class="sig-name descname">open_hyperparam_db</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">name</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Util.html#open_hyperparam_db"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Util.open_hyperparam_db" title="Permalink to this definition">¶</a></dt>
<dd><p>Open a connection with a Sqlite database designed to store benchmark results.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>name</strong> – database filenem</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a sqlite3 database connection</p>
</dd>
</dl>
</dd></dl>

</div>
<div class="section" id="module-pyFTS.hyperparam.GridSearch">
<span id="pyfts-hyperparam-gridsearch-module"></span><h2>pyFTS.hyperparam.GridSearch module<a class="headerlink" href="#module-pyFTS.hyperparam.GridSearch" title="Permalink to this headline">¶</a></h2>
<dl class="py function">
<dt id="pyFTS.hyperparam.GridSearch.cluster_method">
<code class="sig-prename descclassname">pyFTS.hyperparam.GridSearch.</code><code class="sig-name descname">cluster_method</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">individual</span></em>, <em class="sig-param"><span class="n">dataset</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/GridSearch.html#cluster_method"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.GridSearch.cluster_method" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

<dl class="py function">
<dt id="pyFTS.hyperparam.GridSearch.dict_individual">
<code class="sig-prename descclassname">pyFTS.hyperparam.GridSearch.</code><code class="sig-name descname">dict_individual</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">mf</span></em>, <em class="sig-param"><span class="n">partitioner</span></em>, <em class="sig-param"><span class="n">partitions</span></em>, <em class="sig-param"><span class="n">order</span></em>, <em class="sig-param"><span class="n">lags</span></em>, <em class="sig-param"><span class="n">alpha_cut</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/GridSearch.html#dict_individual"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.GridSearch.dict_individual" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

<dl class="py function">
<dt id="pyFTS.hyperparam.GridSearch.execute">
<code class="sig-prename descclassname">pyFTS.hyperparam.GridSearch.</code><code class="sig-name descname">execute</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">hyperparams</span></em>, <em class="sig-param"><span class="n">datasetname</span></em>, <em class="sig-param"><span class="n">dataset</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/GridSearch.html#execute"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.GridSearch.execute" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

<dl class="py function">
<dt id="pyFTS.hyperparam.GridSearch.process_jobs">
<code class="sig-prename descclassname">pyFTS.hyperparam.GridSearch.</code><code class="sig-name descname">process_jobs</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">jobs</span></em>, <em class="sig-param"><span class="n">datasetname</span></em>, <em class="sig-param"><span class="n">conn</span></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="module-pyFTS.hyperparam.Evolutionary">
<span id="pyfts-hyperparam-evolutionary-module"></span><h2>pyFTS.hyperparam.Evolutionary module<a class="headerlink" href="#module-pyFTS.hyperparam.Evolutionary" title="Permalink to this headline">¶</a></h2>
<p>Distributed Evolutionary Hyperparameter Optimization (DEHO) for MVFTS</p>
<dl class="py function">
<dt id="pyFTS.hyperparam.Evolutionary.GeneticAlgorithm">
<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">GeneticAlgorithm</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataset</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#GeneticAlgorithm"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.GeneticAlgorithm" title="Permalink to this definition">¶</a></dt>
<dd><p>Genetic algoritm for Distributed Evolutionary Hyperparameter Optimization (DEHO)</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>dataset</strong> – The time series to optimize the FTS</p></li>
<li><p><strong>ngen</strong> – An integer value with the maximum number of generations, default value: 30</p></li>
<li><p><strong>mgen</strong> – An integer value with the maximum number of generations without improvement to stop, default value 7</p></li>
<li><p><strong>npop</strong> – An integer value with the population size, default value: 20</p></li>
<li><p><strong>pcross</strong> – A float value between 0 and 1 with the probability of crossover, default: .5</p></li>
<li><p><strong>psel</strong> – A float value between 0 and 1 with the probability of selection, default: .5</p></li>
<li><p><strong>pmut</strong> – A float value between 0 and 1 with the probability of mutation, default: .3</p></li>
<li><p><strong>fts_method</strong> – The FTS method to optimize</p></li>
<li><p><strong>parameters</strong> – dict with model specific arguments for fts_method</p></li>
<li><p><strong>elitism</strong> – A boolean value indicating if the best individual must always survive to next population</p></li>
<li><p><strong>initial_operator</strong> – a function that receives npop and return a random population with size npop</p></li>
<li><p><strong>evalutation_operator</strong> – a function that receives a dataset and an individual and return its fitness</p></li>
<li><p><strong>selection_operator</strong> – a function that receives the whole population and return a selected individual</p></li>
<li><p><strong>crossover_operator</strong> – a function that receives the whole population and return a descendent individual</p></li>
<li><p><strong>mutation_operator</strong> – a function that receives one individual and return a changed individual</p></li>
<li><p><strong>window_size</strong> – An integer value with the the length of scrolling window for train/test on dataset</p></li>
<li><p><strong>train_rate</strong> – A float value between 0 and 1 with the train/test split ([0,1])</p></li>
<li><p><strong>increment_rate</strong> – A float value between 0 and 1 with the the increment of the scrolling window,
relative to the window_size ([0,1])</p></li>
<li><p><strong>collect_statistics</strong> – A boolean value indicating to collect statistics for each generation</p></li>
<li><p><strong>distributed</strong> – A value indicating it the execution will be local and sequential (distributed=False),
or parallel and distributed (distributed=’dispy’ or distributed=’spark’)</p></li>
<li><p><strong>cluster</strong> – If distributed=’dispy’ the list of cluster nodes, else if distributed=’spark’ it is the master node</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>the best genotype</p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt id="pyFTS.hyperparam.Evolutionary.crossover">
<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">crossover</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">population</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#crossover"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.crossover" title="Permalink to this definition">¶</a></dt>
<dd><p>Crossover operation between two parents</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>population</strong> – the original population</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a genotype</p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt id="pyFTS.hyperparam.Evolutionary.double_tournament">
<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">double_tournament</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">population</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#double_tournament"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.double_tournament" title="Permalink to this definition">¶</a></dt>
<dd><p>Double tournament selection strategy.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>population</strong> – </p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt id="pyFTS.hyperparam.Evolutionary.elitism">
<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">elitism</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">population</span></em>, <em class="sig-param"><span class="n">new_population</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#elitism"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.elitism" title="Permalink to this definition">¶</a></dt>
<dd><p>Elitism operation, always select the best individual of the population and discard the worst</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>population</strong> – </p></li>
<li><p><strong>new_population</strong> – </p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt id="pyFTS.hyperparam.Evolutionary.evaluate">
<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">evaluate</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataset</span></em>, <em class="sig-param"><span class="n">individual</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#evaluate"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.evaluate" title="Permalink to this definition">¶</a></dt>
<dd><p>Evaluate an individual using a sliding window cross validation over the dataset.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>dataset</strong> – Evaluation dataset</p></li>
<li><p><strong>individual</strong> – genotype to be tested</p></li>
<li><p><strong>window_size</strong> – The length of scrolling window for train/test on dataset</p></li>
<li><p><strong>train_rate</strong> – The train/test split ([0,1])</p></li>
<li><p><strong>increment_rate</strong> – The increment of the scrolling window, relative to the window_size ([0,1])</p></li>
<li><p><strong>parameters</strong> – dict with model specific arguments for fit method.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a tuple (len_lags, rmse) with the parsimony fitness value and the accuracy fitness value</p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt id="pyFTS.hyperparam.Evolutionary.execute">
<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">execute</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">datasetname</span></em>, <em class="sig-param"><span class="n">dataset</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#execute"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.execute" title="Permalink to this definition">¶</a></dt>
<dd><p>Batch execution of Distributed Evolutionary Hyperparameter Optimization (DEHO) for monovariate methods</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>datasetname</strong> – </p></li>
<li><p><strong>dataset</strong> – The time series to optimize the FTS</p></li>
<li><p><strong>file</strong> – </p></li>
<li><p><strong>experiments</strong> – </p></li>
<li><p><strong>distributed</strong> – </p></li>
<li><p><strong>ngen</strong> – An integer value with the maximum number of generations, default value: 30</p></li>
<li><p><strong>mgen</strong> – An integer value with the maximum number of generations without improvement to stop, default value 7</p></li>
<li><p><strong>npop</strong> – An integer value with the population size, default value: 20</p></li>
<li><p><strong>pcross</strong> – A float value between 0 and 1 with the probability of crossover, default: .5</p></li>
<li><p><strong>psel</strong> – A float value between 0 and 1 with the probability of selection, default: .5</p></li>
<li><p><strong>pmut</strong> – A float value between 0 and 1 with the probability of mutation, default: .3</p></li>
<li><p><strong>fts_method</strong> – The FTS method to optimize</p></li>
<li><p><strong>parameters</strong> – dict with model specific arguments for fts_method</p></li>
<li><p><strong>elitism</strong> – A boolean value indicating if the best individual must always survive to next population</p></li>
<li><p><strong>initial_operator</strong> – a function that receives npop and return a random population with size npop</p></li>
<li><p><strong>random_individual</strong> – create an random genotype</p></li>
<li><p><strong>evalutation_operator</strong> – a function that receives a dataset and an individual and return its fitness</p></li>
<li><p><strong>selection_operator</strong> – a function that receives the whole population and return a selected individual</p></li>
<li><p><strong>crossover_operator</strong> – a function that receives the whole population and return a descendent individual</p></li>
<li><p><strong>mutation_operator</strong> – a function that receives one individual and return a changed individual</p></li>
<li><p><strong>window_size</strong> – An integer value with the the length of scrolling window for train/test on dataset</p></li>
<li><p><strong>train_rate</strong> – A float value between 0 and 1 with the train/test split ([0,1])</p></li>
<li><p><strong>increment_rate</strong> – A float value between 0 and 1 with the the increment of the scrolling window,
relative to the window_size ([0,1])</p></li>
<li><p><strong>collect_statistics</strong> – A boolean value indicating to collect statistics for each generation</p></li>
<li><p><strong>distributed</strong> – A value indicating it the execution will be local and sequential (distributed=False),
or parallel and distributed (distributed=’dispy’ or distributed=’spark’)</p></li>
<li><p><strong>cluster</strong> – If distributed=’dispy’ the list of cluster nodes, else if distributed=’spark’ it is the master node</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>the best genotype</p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt id="pyFTS.hyperparam.Evolutionary.genotype">
<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">genotype</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">mf</span></em>, <em class="sig-param"><span class="n">npart</span></em>, <em class="sig-param"><span class="n">partitioner</span></em>, <em class="sig-param"><span class="n">order</span></em>, <em class="sig-param"><span class="n">alpha</span></em>, <em class="sig-param"><span class="n">lags</span></em>, <em class="sig-param"><span class="n">f1</span></em>, <em class="sig-param"><span class="n">f2</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#genotype"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.genotype" title="Permalink to this definition">¶</a></dt>
<dd><p>Create the individual genotype</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>mf</strong> – membership function</p></li>
<li><p><strong>npart</strong> – number of partitions</p></li>
<li><p><strong>partitioner</strong> – partitioner method</p></li>
<li><p><strong>order</strong> – model order</p></li>
<li><p><strong>alpha</strong> – alpha-cut</p></li>
<li><p><strong>lags</strong> – array with lag indexes</p></li>
<li><p><strong>f1</strong> – accuracy fitness value</p></li>
<li><p><strong>f2</strong> – parsimony fitness value</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>the genotype, a dictionary with all hyperparameters</p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt id="pyFTS.hyperparam.Evolutionary.initial_population">
<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">initial_population</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">n</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#initial_population"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.initial_population" title="Permalink to this definition">¶</a></dt>
<dd><p>Create a random population of size n</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>n</strong> – the size of the population</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a list with n random individuals</p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt id="pyFTS.hyperparam.Evolutionary.lag_crossover2">
<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">lag_crossover2</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">best</span></em>, <em class="sig-param"><span class="n">worst</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#lag_crossover2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.lag_crossover2" title="Permalink to this definition">¶</a></dt>
<dd><p>Cross over two lag genes</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>best</strong> – best genotype</p></li>
<li><p><strong>worst</strong> – worst genotype</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a tuple (order, lags)</p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt id="pyFTS.hyperparam.Evolutionary.log_result">
<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">log_result</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">conn</span></em>, <em class="sig-param"><span class="n">datasetname</span></em>, <em class="sig-param"><span class="n">fts_method</span></em>, <em class="sig-param"><span class="n">result</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#log_result"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.log_result" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

<dl class="py function">
<dt id="pyFTS.hyperparam.Evolutionary.mutation">
<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">mutation</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">individual</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#mutation"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.mutation" title="Permalink to this definition">¶</a></dt>
<dd><p>Mutation operator</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>individual</strong> – an individual genotype</p></li>
<li><p><strong>pmut</strong> – individual probability o</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt id="pyFTS.hyperparam.Evolutionary.mutation_lags">
<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">mutation_lags</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">lags</span></em>, <em class="sig-param"><span class="n">order</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#mutation_lags"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.mutation_lags" title="Permalink to this definition">¶</a></dt>
<dd><p>Mutation operation for lags gene</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>lags</strong> – </p></li>
<li><p><strong>order</strong> – </p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt id="pyFTS.hyperparam.Evolutionary.persist_statistics">
<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">persist_statistics</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">datasetname</span></em>, <em class="sig-param"><span class="n">statistics</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#persist_statistics"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.persist_statistics" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>

<dl class="py function">
<dt id="pyFTS.hyperparam.Evolutionary.phenotype">
<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">phenotype</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">individual</span></em>, <em class="sig-param"><span class="n">train</span></em>, <em class="sig-param"><span class="n">fts_method</span></em>, <em class="sig-param"><span class="n">parameters</span><span class="o">=</span><span class="default_value">{}</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#phenotype"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.phenotype" title="Permalink to this definition">¶</a></dt>
<dd><p>Instantiate the genotype, creating a fitted model with the genotype hyperparameters</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>individual</strong> – a genotype</p></li>
<li><p><strong>train</strong> – the training dataset</p></li>
<li><p><strong>fts_method</strong> – the FTS method</p></li>
<li><p><strong>parameters</strong> – dict with model specific arguments for fit method.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a fitted FTS model</p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt id="pyFTS.hyperparam.Evolutionary.process_experiment">
<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">process_experiment</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">fts_method</span></em>, <em class="sig-param"><span class="n">result</span></em>, <em class="sig-param"><span class="n">datasetname</span></em>, <em class="sig-param"><span class="n">conn</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#process_experiment"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.process_experiment" title="Permalink to this definition">¶</a></dt>
<dd><p>Persist the results of an DEHO execution in sqlite database (best hyperparameters) and json file (generation statistics)</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>fts_method</strong> – </p></li>
<li><p><strong>result</strong> – </p></li>
<li><p><strong>datasetname</strong> – </p></li>
<li><p><strong>conn</strong> – </p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt id="pyFTS.hyperparam.Evolutionary.random_genotype">
<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">random_genotype</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#random_genotype"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.random_genotype" title="Permalink to this definition">¶</a></dt>
<dd><p>Create random genotype</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>the genotype, a dictionary with all hyperparameters</p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt id="pyFTS.hyperparam.Evolutionary.tournament">
<code class="sig-prename descclassname">pyFTS.hyperparam.Evolutionary.</code><code class="sig-name descname">tournament</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">population</span></em>, <em class="sig-param"><span class="n">objective</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/hyperparam/Evolutionary.html#tournament"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#pyFTS.hyperparam.Evolutionary.tournament" title="Permalink to this definition">¶</a></dt>
<dd><p>Simple tournament selection strategy.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>population</strong> – the population</p></li>
<li><p><strong>objective</strong> – the objective to be considered on tournament</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p></p>
</dd>
</dl>
</dd></dl>

</div>
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