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<h1>Source code for pyFTS.hyperparam.GridSearch</h1><div class="highlight"><pre>
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<span></span>
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<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">Util</span><span class="p">,</span> <span class="n">Membership</span>
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<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">hofts</span>
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<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="k">import</span> <span class="n">Grid</span><span class="p">,</span> <span class="n">Entropy</span>
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<span class="kn">from</span> <span class="nn">pyFTS.benchmarks</span> <span class="k">import</span> <span class="n">Measures</span>
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<span class="kn">from</span> <span class="nn">pyFTS.hyperparam</span> <span class="k">import</span> <span class="n">Util</span> <span class="k">as</span> <span class="n">hUtil</span>
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<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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<span class="kn">import</span> <span class="nn">dispy</span>
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<span class="kn">from</span> <span class="nn">itertools</span> <span class="k">import</span> <span class="n">product</span>
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<div class="viewcode-block" id="dict_individual"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.GridSearch.dict_individual">[docs]</a><span class="k">def</span> <span class="nf">dict_individual</span><span class="p">(</span><span class="n">mf</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">partitions</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">lags</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="p">):</span>
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<span class="k">return</span> <span class="p">{</span>
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<span class="s1">'mf'</span><span class="p">:</span> <span class="n">mf</span><span class="p">,</span>
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<span class="s1">'partitioner'</span><span class="p">:</span> <span class="n">partitioner</span><span class="p">,</span>
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<span class="s1">'npart'</span><span class="p">:</span> <span class="n">partitions</span><span class="p">,</span>
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<span class="s1">'alpha'</span><span class="p">:</span> <span class="n">alpha_cut</span><span class="p">,</span>
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<span class="s1">'order'</span><span class="p">:</span> <span class="n">order</span><span class="p">,</span>
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<span class="s1">'lags'</span><span class="p">:</span> <span class="n">lags</span>
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<span class="p">}</span></div>
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<div class="viewcode-block" id="cluster_method"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.GridSearch.cluster_method">[docs]</a><span class="k">def</span> <span class="nf">cluster_method</span><span class="p">(</span><span class="n">individual</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">):</span>
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<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">Util</span><span class="p">,</span> <span class="n">Membership</span>
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<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">hofts</span>
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<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="k">import</span> <span class="n">Grid</span><span class="p">,</span> <span class="n">Entropy</span>
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<span class="kn">from</span> <span class="nn">pyFTS.benchmarks</span> <span class="k">import</span> <span class="n">Measures</span>
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<span class="k">if</span> <span class="n">individual</span><span class="p">[</span><span class="s1">'mf'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
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<span class="n">mf</span> <span class="o">=</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span>
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<span class="k">elif</span> <span class="n">individual</span><span class="p">[</span><span class="s1">'mf'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
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<span class="n">mf</span> <span class="o">=</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trapmf</span>
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<span class="k">elif</span> <span class="n">individual</span><span class="p">[</span><span class="s1">'mf'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">3</span> <span class="ow">and</span> <span class="n">individual</span><span class="p">[</span><span class="s1">'partitioner'</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
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<span class="n">mf</span> <span class="o">=</span> <span class="n">Membership</span><span class="o">.</span><span class="n">gaussmf</span>
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<span class="k">else</span><span class="p">:</span>
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<span class="n">mf</span> <span class="o">=</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span>
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<span class="k">if</span> <span class="n">individual</span><span class="p">[</span><span class="s1">'partitioner'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
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<span class="n">partitioner</span> <span class="o">=</span> <span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">train</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">'npart'</span><span class="p">],</span> <span class="n">func</span><span class="o">=</span><span class="n">mf</span><span class="p">)</span>
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<span class="k">elif</span> <span class="n">individual</span><span class="p">[</span><span class="s1">'partitioner'</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
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<span class="n">npart</span> <span class="o">=</span> <span class="n">individual</span><span class="p">[</span><span class="s1">'npart'</span><span class="p">]</span> <span class="k">if</span> <span class="n">individual</span><span class="p">[</span><span class="s1">'npart'</span><span class="p">]</span> <span class="o">></span> <span class="mi">10</span> <span class="k">else</span> <span class="mi">10</span>
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<span class="n">partitioner</span> <span class="o">=</span> <span class="n">Entropy</span><span class="o">.</span><span class="n">EntropyPartitioner</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">train</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="n">npart</span><span class="p">,</span> <span class="n">func</span><span class="o">=</span><span class="n">mf</span><span class="p">)</span>
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<span class="n">model</span> <span class="o">=</span> <span class="n">hofts</span><span class="o">.</span><span class="n">WeightedHighOrderFTS</span><span class="p">(</span><span class="n">partitioner</span><span class="o">=</span><span class="n">partitioner</span><span class="p">,</span>
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<span class="n">lags</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">'lags'</span><span class="p">],</span>
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<span class="n">alpha_cut</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">'alpha'</span><span class="p">],</span>
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<span class="n">order</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">'order'</span><span class="p">])</span>
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<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train</span><span class="p">)</span>
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<span class="n">rmse</span><span class="p">,</span> <span class="n">mape</span><span class="p">,</span> <span class="n">u</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">get_point_statistics</span><span class="p">(</span><span class="n">test</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span>
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<span class="n">size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
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<span class="k">return</span> <span class="n">individual</span><span class="p">,</span> <span class="n">rmse</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">mape</span><span class="p">,</span> <span class="n">u</span></div>
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<div class="viewcode-block" id="process_jobs"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.GridSearch.process_jobs">[docs]</a><span class="k">def</span> <span class="nf">process_jobs</span><span class="p">(</span><span class="n">jobs</span><span class="p">,</span> <span class="n">datasetname</span><span class="p">,</span> <span class="n">conn</span><span class="p">):</span>
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<span class="k">for</span> <span class="n">job</span> <span class="ow">in</span> <span class="n">jobs</span><span class="p">:</span>
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<span class="n">result</span><span class="p">,</span> <span class="n">rmse</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">mape</span><span class="p">,</span> <span class="n">u</span> <span class="o">=</span> <span class="n">job</span><span class="p">()</span>
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<span class="k">if</span> <span class="n">job</span><span class="o">.</span><span class="n">status</span> <span class="o">==</span> <span class="n">dispy</span><span class="o">.</span><span class="n">DispyJob</span><span class="o">.</span><span class="n">Finished</span> <span class="ow">and</span> <span class="n">result</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
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<span class="nb">print</span><span class="p">(</span><span class="s2">"Processing result of </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">result</span><span class="p">))</span>
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<span class="n">metrics</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'rmse'</span><span class="p">:</span> <span class="n">rmse</span><span class="p">,</span> <span class="s1">'size'</span><span class="p">:</span> <span class="n">size</span><span class="p">,</span> <span class="s1">'mape'</span><span class="p">:</span> <span class="n">mape</span><span class="p">,</span> <span class="s1">'u'</span><span class="p">:</span> <span class="n">u</span> <span class="p">}</span>
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<span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="n">metrics</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
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<span class="n">record</span> <span class="o">=</span> <span class="p">(</span><span class="n">datasetname</span><span class="p">,</span> <span class="s1">'GridSearch'</span><span class="p">,</span> <span class="s1">'WHOFTS'</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">result</span><span class="p">[</span><span class="s1">'mf'</span><span class="p">],</span>
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||||
<span class="n">result</span><span class="p">[</span><span class="s1">'order'</span><span class="p">],</span> <span class="n">result</span><span class="p">[</span><span class="s1">'partitioner'</span><span class="p">],</span> <span class="n">result</span><span class="p">[</span><span class="s1">'npart'</span><span class="p">],</span>
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<span class="n">result</span><span class="p">[</span><span class="s1">'alpha'</span><span class="p">],</span> <span class="nb">str</span><span class="p">(</span><span class="n">result</span><span class="p">[</span><span class="s1">'lags'</span><span class="p">]),</span> <span class="n">metric</span><span class="p">,</span> <span class="n">metrics</span><span class="p">[</span><span class="n">metric</span><span class="p">])</span>
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<span class="nb">print</span><span class="p">(</span><span class="n">record</span><span class="p">)</span>
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<span class="n">hUtil</span><span class="o">.</span><span class="n">insert_hyperparam</span><span class="p">(</span><span class="n">record</span><span class="p">,</span> <span class="n">conn</span><span class="p">)</span>
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<span class="k">else</span><span class="p">:</span>
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<span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">exception</span><span class="p">)</span>
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<span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">stdout</span><span class="p">)</span></div>
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<div class="viewcode-block" id="execute"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.GridSearch.execute">[docs]</a><span class="k">def</span> <span class="nf">execute</span><span class="p">(</span><span class="n">hyperparams</span><span class="p">,</span> <span class="n">datasetname</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
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|
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<span class="n">nodes</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'nodes'</span><span class="p">,[</span><span class="s1">'127.0.0.1'</span><span class="p">])</span>
|
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|
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<span class="n">individuals</span> <span class="o">=</span> <span class="p">[]</span>
|
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||||
<span class="k">if</span> <span class="s1">'lags'</span> <span class="ow">in</span> <span class="n">hyperparams</span><span class="p">:</span>
|
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<span class="n">lags</span> <span class="o">=</span> <span class="n">hyperparams</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'lags'</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
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<span class="n">lags</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">50</span><span class="p">)]</span>
|
||||
|
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<span class="n">keys_sorted</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">hyperparams</span><span class="o">.</span><span class="n">keys</span><span class="p">())]</span>
|
||||
|
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<span class="n">index</span> <span class="o">=</span> <span class="p">{}</span>
|
||||
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">keys_sorted</span><span class="p">)):</span>
|
||||
<span class="n">index</span><span class="p">[</span><span class="n">keys_sorted</span><span class="p">[</span><span class="n">k</span><span class="p">]]</span> <span class="o">=</span> <span class="n">k</span>
|
||||
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Evaluation order: </span><span class="se">\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">index</span><span class="p">))</span>
|
||||
|
||||
<span class="n">hp_values</span> <span class="o">=</span> <span class="p">[</span>
|
||||
<span class="p">[</span><span class="n">v</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">hyperparams</span><span class="p">[</span><span class="n">hp</span><span class="p">]]</span>
|
||||
<span class="k">for</span> <span class="n">hp</span> <span class="ow">in</span> <span class="n">keys_sorted</span>
|
||||
<span class="p">]</span>
|
||||
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Evaluation values: </span><span class="se">\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">hp_values</span><span class="p">))</span>
|
||||
|
||||
<span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span> <span class="o">=</span> <span class="n">Util</span><span class="o">.</span><span class="n">start_dispy_cluster</span><span class="p">(</span><span class="n">cluster_method</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="n">nodes</span><span class="p">)</span>
|
||||
<span class="n">conn</span> <span class="o">=</span> <span class="n">hUtil</span><span class="o">.</span><span class="n">open_hyperparam_db</span><span class="p">(</span><span class="s1">'hyperparam.db'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">instance</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">hp_values</span><span class="p">):</span>
|
||||
<span class="n">partitions</span> <span class="o">=</span> <span class="n">instance</span><span class="p">[</span><span class="n">index</span><span class="p">[</span><span class="s1">'partitions'</span><span class="p">]]</span>
|
||||
<span class="n">partitioner</span> <span class="o">=</span> <span class="n">instance</span><span class="p">[</span><span class="n">index</span><span class="p">[</span><span class="s1">'partitioner'</span><span class="p">]]</span>
|
||||
<span class="n">mf</span> <span class="o">=</span> <span class="n">instance</span><span class="p">[</span><span class="n">index</span><span class="p">[</span><span class="s1">'mf'</span><span class="p">]]</span>
|
||||
<span class="n">alpha_cut</span> <span class="o">=</span> <span class="n">instance</span><span class="p">[</span><span class="n">index</span><span class="p">[</span><span class="s1">'alpha'</span><span class="p">]]</span>
|
||||
<span class="n">order</span> <span class="o">=</span> <span class="n">instance</span><span class="p">[</span><span class="n">index</span><span class="p">[</span><span class="s1">'order'</span><span class="p">]]</span>
|
||||
<span class="n">count</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="k">for</span> <span class="n">lag1</span> <span class="ow">in</span> <span class="n">lags</span><span class="p">:</span> <span class="c1"># o é o lag1</span>
|
||||
<span class="n">_lags</span> <span class="o">=</span> <span class="p">[</span><span class="n">lag1</span><span class="p">]</span>
|
||||
<span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
|
||||
<span class="k">if</span> <span class="n">order</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">lag2</span> <span class="ow">in</span> <span class="n">lags</span><span class="p">:</span> <span class="c1"># o é o lag1</span>
|
||||
<span class="n">_lags2</span> <span class="o">=</span> <span class="p">[</span><span class="n">lag1</span><span class="p">,</span> <span class="n">lag1</span><span class="o">+</span><span class="n">lag2</span><span class="p">]</span>
|
||||
<span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
|
||||
<span class="k">if</span> <span class="n">order</span> <span class="o">></span> <span class="mi">2</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">lag3</span> <span class="ow">in</span> <span class="n">lags</span><span class="p">:</span> <span class="c1"># o é o lag1</span>
|
||||
<span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
|
||||
<span class="n">_lags3</span> <span class="o">=</span> <span class="p">[</span><span class="n">lag1</span><span class="p">,</span> <span class="n">lag1</span> <span class="o">+</span> <span class="n">lag2</span><span class="p">,</span> <span class="n">lag1</span> <span class="o">+</span> <span class="n">lag2</span><span class="o">+</span><span class="n">lag3</span> <span class="p">]</span>
|
||||
<span class="n">individuals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dict_individual</span><span class="p">(</span><span class="n">mf</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">partitions</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">_lags3</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="p">))</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">individuals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
|
||||
<span class="n">dict_individual</span><span class="p">(</span><span class="n">mf</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">partitions</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">_lags2</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="p">))</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">individuals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dict_individual</span><span class="p">(</span><span class="n">mf</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">partitions</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">_lags</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="p">))</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">count</span> <span class="o">></span> <span class="mi">50</span><span class="p">:</span>
|
||||
<span class="n">jobs</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">ind</span> <span class="ow">in</span> <span class="n">individuals</span><span class="p">:</span>
|
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Testing individual </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ind</span><span class="p">))</span>
|
||||
<span class="n">job</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">submit</span><span class="p">(</span><span class="n">ind</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">)</span>
|
||||
<span class="n">jobs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">job</span><span class="p">)</span>
|
||||
|
||||
<span class="n">process_jobs</span><span class="p">(</span><span class="n">jobs</span><span class="p">,</span> <span class="n">datasetname</span><span class="p">,</span> <span class="n">conn</span><span class="p">)</span>
|
||||
|
||||
<span class="n">count</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
|
||||
<span class="n">individuals</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="n">Util</span><span class="o">.</span><span class="n">stop_dispy_cluster</span><span class="p">(</span><span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span><span class="p">)</span></div>
|
||||
</pre></div>
|
||||
|
||||
</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>
|
||||
2
docs/build/html/modules.html
vendored
2
docs/build/html/modules.html
vendored
@ -164,7 +164,7 @@
|
||||
<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-gridsearch-module">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>
|
||||
|
||||
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
@ -121,13 +121,12 @@ def fuzzyfy(data, partitioner, **kwargs):
|
||||
:param data: input value to be fuzzyfied
|
||||
:param partitioner: a trained pyFTS.partitioners.Partitioner object
|
||||
:param kwargs: dict, optional arguments
|
||||
|
||||
:keyword alpha_cut: the minimal membership value to be considered on fuzzyfication (only for mode='sets')
|
||||
:keyword method: the fuzzyfication method (fuzzy: all fuzzy memberships, maximum: only the maximum membership)
|
||||
:keyword mode: the fuzzyfication mode (sets: return the fuzzy sets names, vector: return a vector with the membership
|
||||
values for all fuzzy sets, both: return a list with tuples (fuzzy set, membership value) )
|
||||
|
||||
:returns a list with the fuzzyfied values, depending on the mode
|
||||
|
||||
"""
|
||||
alpha_cut = kwargs.get('alpha_cut', 0.)
|
||||
mode = kwargs.get('mode', 'sets')
|
||||
|
||||
@ -127,7 +127,7 @@ class Partitioner(object):
|
||||
|
||||
def fuzzyfy(self, data, **kwargs):
|
||||
"""
|
||||
A general method for fuzzyfication.
|
||||
Fuzzyfy the input data according to this partitioner fuzzy sets.
|
||||
|
||||
:param data: input value to be fuzzyfied
|
||||
:keyword alpha_cut: the minimal membership value to be considered on fuzzyfication (only for mode='sets')
|
||||
@ -178,6 +178,14 @@ class Partitioner(object):
|
||||
return sets
|
||||
|
||||
def check_bounds(self, data):
|
||||
'''
|
||||
Check if the input data is outside the known Universe of Discourse and, if it is, round it to the closest
|
||||
fuzzy set.
|
||||
|
||||
:param data: input data to be verified
|
||||
:return: the index of the closest fuzzy set when data is outside de universe of discourse or None if
|
||||
the data is inside the UoD.
|
||||
'''
|
||||
if data < self.min:
|
||||
return 0
|
||||
elif data > self.max:
|
||||
|
||||
@ -7,7 +7,7 @@ from pyFTS.probabilistic import ProbabilityDistribution
|
||||
|
||||
class Mixture(ProbabilityDistribution.ProbabilityDistribution):
|
||||
"""
|
||||
|
||||
Mix two or more Probability Distributions smoothing them with weights.
|
||||
"""
|
||||
def __init__(self, type="mixture", **kwargs):
|
||||
self.models = []
|
||||
@ -21,7 +21,7 @@ class Mixture(ProbabilityDistribution.ProbabilityDistribution):
|
||||
if not isinstance(values, list):
|
||||
values = [values]
|
||||
|
||||
for ct, m in enumerate(self.models):
|
||||
#for ct, m in enumerate(self.models):
|
||||
|
||||
probs = [m.density(values) ]
|
||||
#probs = [m.density(values) ]
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user